dataesr/scientific-paragraphs-categorization huggingface dataset:
Selecting Reference Traffic Sample The traffic data to be used for the optimization was selected taking into account both, ATM network as well as meteorological aspects. The objective was to choose a representative date that was on the one hand as unimpeded by ATM regulations as possible and on the other hand was dominated by weather patterns with a certain complexity for expecting interesting optimization results. Based on air traffic demand data of the year 2015 (obtained from EUROCONTROL's Demand Data Repository DDR2), first the busiest day of each month was selected in order to compile a list of candidate days. Based on this, two days per winter and two days per summer season each were down-selected under criteria of high numbers of flight movements while at the same time ensuring a low impact by messages from the Aeronautical Information Management (AIM).
Meteorological Situation in Case Study 18 December 2015 For each day, contrail formation regions were identified using infrared satellite imagery and data from the ECMWF ERA-Interim re-analysis  [53]. As indicator for photochemical activity in the atmosphere, the ozone production efficiency was determined with the ECHAM/MESSy atmospheric chemistry model. Additionally geopotential height was determined from ECMWF ERA-Interim re-analysis. As ECFs depend to a large extent on synoptical situation, particular focus was given on selecting candidate days for our case study. A meteorological situation is selected which represents a medium to high complexity of the meteorological environment which ATM is encountering. From the list of candidate days, 18 December 2015 turned out to be best suited as reference day for the optimization case study.
Engine Emissions and Environmental Performance The air traffic over Europe on the selected day is used as a reference scenario for the optimization task in ATM4E. Further assumptions are made to filter the traffic data for a better processability. As ATM4E focuses on the European airspace, only intra-ECAC (European Civil Aviation Conference) flights are considered and only flights that can be modelled with aircraft performance data from EUROCONTROL's Base of Aircraft Data (BADA) 4. 0 are taken into account. This required simplification reduces the amount of available seat kilometres (ASK) in the data set by only 8-9%, since especially large commercial aircraft representing major parts of ASK are included in BADA 4. 0. Lastly, flights which depart before or arrive after 18 December 2015 are filtered out leading to a final dataset of 13,276 flights (from originally 28,337).
In order to assess the environmental caused by the traffic sample and in order to prepare trajectory optimization, for the described reference flight set the overall performance parameters with respect to gaseous aircraft emissions, the provoked contrail formation and the overall climate impact are calculated. For this purpose, DLR's Global Air Traffic Emission Distribution Laboratory (GRIDLAB) is applied which contains a database of precomputed emission profiles for various aircraft types, mission ranges and load factors  [54]. For the computation of these reduced profiles a numerical trajectory simulation tool is used. Aircraft mechanics are modeled by using simplified equations of motion also known as Total Energy Model and evaluating BADA model equations for aerodynamics, thrust and fuel flow to obtain the forces acting on the aircraft as well as the engine state relevant for emission calculation in each simulation time step  [30].
Standard profiles are simulated assuming typical speed schedules for climb, cruise and descent. In cruise, step climbs are performed where required by monitoring and following the aircraft's optimum altitude profile. Emissions are modelled using the state-of-the-art Boeing Fuel Flow Method 2, which allows for the estimation of emission indices (EI) for the species NO x , HC and CO derived from EIs for sea level conditions obtained from the Engine Emission Databank by the International Civil Aviation Organization (ICAO)  [33] , whereas species that are produced proportionally to fuel burn, i. e. , CO 2 , H 2 O are calculated using constant EIs. Four-dimensional (longitude, latitude, altitude, time) emission inventories are generated by simulating every flight in the traffic scenario and determining the corresponding emission distribution.
For longer time horizon this ratio tends to lower values, going down from about 20 to about 6, when comparing a time horizon of 20 and 100 years, respectively. In terms of implementation of such concept described, additional environmental MET information data products need to be made available to ATM. Hence complexity of the ATM environment due to meteorology, needs to be transferred via MET information into the ATM infrastructure, making sure that ATM is having available high quality information for efficient flight planning. Within the SESAR 2020 Master Plan such information is made available system-wide via the SWIM infrastructure, where MET is one component in it, as is e. g. , AIM information. Trade-offs in optimisation for local and global impacts might be expected to be low, because standard local impact ECFs show a relatively weak overlap with climate ECFs.
Acknowledgments: The project  ATM4E  has received funding from the  SESAR Joint Undertaking  under grant agreement No  699395  under European Union's  Horizon 2020 research and innovation programme. The work of authors of this study has been supported by  DLR  Project  WeCare. This study received constructive input on presentation during the ECATS Conference on Aviation and Environment, held in Athens, 7-9 November 2016, which was organised by the  ECATS IASBL, Brussels.
Criterion validity shows that the SQ-R has a modest but significant correlation with the Mental Rotation Test (r =. 25, P =. 013), as well as its subscales  21. Autistic individuals, on average, score higher on the SQ-R in multiple different studies  22, 23. Further, the SQ-R also predicts autistic traits, with a combination of the SQ-R and the Empathy Quotient predicting as much as 75% of the variance on the Autism Spectrum Quotient, a measure of autistic traits  4. In the Cambridge Autism Research Database, of 5,663 individuals, there were clear differences in scores on the SQ-R based on diagnosis, with autistic individuals scoring significantly higher on the SQ-R than non-autistic individuals, and the SQ-R being significantly and positively correlated with the AQ (Supplementary Note). Genotyping, imputation, and quality control: Details of genotyping, imputation and quality control are given elsewhere  24.
Briefly, unrelated participants were included if they had a call rate of greater than 98. 5%, and were of primarily European ancestry (97% European ancestry). A total of 1,030,430 SNPs (including InDels) were genotyped. SNPs were excluded if: they failed the Hardy-Weinberg Equilibrium Test at P < 10 -20 ; had a genotype rate of less than 90%; they failed the parent-offspring 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 (chi-square test, P < 10 -20 ). Phasing was conducted using Beagle (version 3. 3. 1)  25 in batches of 8000-9000 individuals. This was followed by imputation against all-ethnicity 1000 Genomes haplotypes (excluding monomorphic and singleton sites) using Minimac2  26.
GWAS of the SCDC: The SCDC (The Social and Communication Disorders Checklist)  28  scores were calculated from children of the 90s (ALSPAC cohort)  29  , 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 = 5421 unrelated individuals. The SCDC score was not normally distributed so we log-transformed the scores and ran regression analyses using the first 2 genetic principal components and sex as the covariates using Plink 2. 0 30. Dosage data from BGEN files were converted using hardcalls, with calls with uncertainty > 0. 1 treated as missing data. We excluded SNPs that deviated from Hardy-Weinberg Equilibrium (P < 1x10 -6 ), with minor allele frequency < 0.
01 and missing call rates > 2%. We further excluded individuals with genotype missing rates > 5%. Further details are provided in the Supplementary Note. Genomic inflation factor, heritability, and functional enrichment: LDSR 31,32 was used to calculate for inflation in test statistics due to unaccounted population stratification. Heritability and genetic correlation for all the traits was calculated using LDSR using the north-west European LD scores. For all phenotypes we performed genetic correlation without constraining the intercept. We identified significant genetic correlations using a Bonferroni adjusted P-value < 0. 05. For autism, we validated the genetic correlation using a second, larger dataset: the PGC-iPSYCH meta-analysis  33  autism GWAS dataset (Supplementary Note). Details of the phenotypes, the sample sizes and the references are provided in Supplementary Table  3.
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 36 database within FUMA. In addition, we also investigated chromatin interactions from the Dorsolateral Prefrontal Cortex, the hippocampus, and neural progenitor cells. Significant results were identified using a Benjamini-Hochberg FDR corrected P < 0. 05. We further conducted partitioned heritability using extended methods in LDSR 37. We conducted partitioned heritability for baseline categories. GWIS: To investigate if the SQ-R is genetically correlated with autism independent of the genetic effects of cognition, we constructed a unique SQ-R phenotype after conditioning on the genetic effects of educational attainment using GWIS  38.
Phenotypic regression analyses: To identify if the SQ-R predicts attention to detail and rigidity as defined by Bralten and colleagues  39  , we conducted regression analysis in n = 5,663 individuals from the Cambridge Autism Research Database (CARD) who had completed the AQ and the SQ-R. We derived the variables "attention to detail" and "rigidity" based on the scoring criteria provided by Bralten and colleagues  39. We note that this is different from the attention to detail subscale as defined in the original paper describing the AQ  40. Linear regression analyses were conducted using "attention to detail" and "rigidity" scores as the dependent variable, total SQ score as the predictor variable, and sex, status, and age of completion of both the SQ-R and the AQ as covariates. Status was divided into five categories: Autistic individuals, controls, first degree relative of an autistic individual, suspected autism, and suspected autism and relative of an autistic individual.
Supplementary Table  1  provides a list of all independent SNPs with P < 1x10 -6. For two of these loci (rs1559586 and rs8005092), eQTL and chromatin interactions identified 5 genes for the three loci: TIMM21, FBXO15, RP11-169F17. 1, NOVA1, and STXBP6 (Supplementary Figure  2  and Supplementary Table  2 ). Of these, STXBP6, NOVA1, and FBXO15 have relatively high expression in the brain in the GTEx database. STXBP6 encodes a protein that binds to Syntaxin 1 and help assemble the SNARE complex, found in the presynaptic terminal  41. NOVA1 encodes a neuron-specific RNA binding protein that regulates neuronal miRNA  42. FBXO15 encodes an F-box domain containing protein which is involved in protein degradation via ubiquitination. The most significant SNP in each GWAS analysis explained 0. 001 -0.
We confirmed that the SQminEdu GWAS was not genetically correlated with educational attainment (r g = 0. 063±0. 032, P = 0. 052), and was genetically correlated with SQ-R (r g = 1. 04±0. 005 , P < 2. 2x10  -16  ). The SQminEdu GWAS was still genetically correlated with both the PGC autism dataset (r g = 0. 14±0. 06, P = 0. 032) and the PGC-iPSYCH autism dataset (r g = 0. 20±0. 05, P = 4. 6x10 -4 ) (Figure  3 , Supplementary Table  5 ). These results suggest that SQ-R contributes to the risk for autism independent of the genetic effects of educational attainment. insert Figure  3  here A recent study  39  identified that polygenic scores for autism predicts autistic traits in specific subdomains of the Autism Spectrum Quotient (AQ) which they termed "attention to detail" and "rigidity. " We queried if systemizing predicts both of these domains within the AQ.
Whilst we did not have access to a cohort with both genetic data and AQ scores, we had information on n = 5,663 individuals who had completed both the AQ and the SQ-R in the Cambridge Autism Research Database. Our results show that the SQ-R significantly predicts variation in scores in the "attention to detail" and "rigidity" domains on the AQ as defined by Bralten et al. , 2017  39  (Attention to detail: Beta = 0. 025±0. 0009, P < 2. 2x10 -6 ; Rigidity: Beta = 0. 015±0. 001, P < 2. 2x10 -6 ).
Acknowledgements We are grateful to  Michel Nivard  and  Beate St. Pourcain  for their help with the analytical methods. We thank the research participants and employees of 23andMe for making this work possible. We specifically thank the following members of the 23andMe Research Team:  Michelle Agee ,  Babak Alipanahi ,  Adam Auton ,  Robert K. Bell ,  Katarzyna Bryc ,  Sarah L. Elson ,  Pierre Fontanillas ,  Nicholas A. Furlotte ,  Karen E. Huber ,  Aaron Kleinman ,  Nadia K. Litterman ,  Jennifer C. McCreight ,  Matthew H. McIntyre ,  Joanna L. Mountain ,  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 extremely grateful to all the families who took part in this study, 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.
Gene Expression Analysis For real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR), nodules were harvested at different time points in liquid nitrogen, RNA was extracted with TRI Reagent (Sigma) and Direct-zol RNA MiniPrep Kit (Zymo Research, Irvine, CA, USA). RNA was treated on column with DNaseI according to the manufacturer's instructions. Total RNA was quantified using a spectrophotometer (Nanodrop-1000; NanoDrop Technologies, Wilmington, DE, USA) and checked for quality by gel electrophoresis. Complementary DNA (cDNA) was prepared from 1 µg total RNA with SuperScript III First-Strand Synthesis System for RT-PCR (Life Technologies) using oligo-dT primers according to the manufacturer's instructions. Quantitative real time RT-PCR was performed using a LightCycler 96 (Roche, Basel, Switzerland) and qPCRBIO SyGreen Mix (PCR BIOSYSTEMS, London, UK), according to the manufacturer's protocol.
Cycle threshold values were obtained and data were analyzed by the LightCycler 96 SW1. 1 software (Roche). The relative expression levels were calculated by normalization against the expression of the PTB (Polypyrimidine Tract-Binding-like protein, Medtr3g090960) and the ubiquitin-like gene (Medtr3g091400) (see Table  S2  and  [37] ). Values of relative transcript levels were the mean of three biological replicates. Fold induction was calculated by normalizing the data for each time series to the samples of wild-type 0 dpi (time course experiment), to mock inoculated wild-type 14 dpi (experiments with S. meliloti 1021 mutants) and to the M. truncatula wild-type samples (expression analyses in plant mutants). Primers that were used for qRT-PCR are used in previous  [37, 38]  or this studies and listed in Supplementary Table  S2.
Oligonucleotide primers were synthesized for the NAD1 gene and PCR reactions were carried out to amplify genomic fragments of NAD1 from the 7Y mutant. The sequence analysis revealed a 50-bp deletion starting at 26 bp downstream of the predicted AUG start codon in 7Y; therefore, 7Y is hereafter termed nad1-3 (Figure  2A ). The gene structure of NAD1 available at the Medicago genome database (Mt4. 0 JBrowse) is based on an expressed sequence tag (EST) sequence (EST483823; GenBank BG582085. 1), predicting that the NAD1 gene consists of four exons encoding a 70 amino acid long putative protein (Mt4. 0 JBrowse and Figure  2A ). Because the 50-bp deletion is located in the first predicted intron of this NAD1 gene model, cDNA of NAD1 was generated and sequenced to search for other transcript versions.
Both Exons of NAD1 Are Required for Complete Rescue of the Symbiotic Phenotype of nad1-3 and nad1-4 Mutants In order to test whether the entire transcript of NAD1 harboring two exons or other alternative transcripts are able to restore the symbiotic phenotype of nad1-3 and nad1-4, we carried out genetic complementation experiments using the following constructs: the genomic copy of NAD1, the first exon (containing the entire coding sequence for the protein), the first exon with a 78-bp deletion mimicking the first intron predicted at the Medicago genome database based on the BG582085 EST and the whole gene with the same 78-bp deletion fused to the native NAD1 promoter (Figure  S3 ). These constructs were introduced into nad1-3 and nad1-4 roots using Agrobacterium rhizogenes mediated hairy root transformation.
The roots were inoculated with S. medicae strain WSM419 (pXLGD4), and the symbiotic phenotype was assessed for the presence of infected cells in the nodules with the help of X-gal staining, as well as for the vanishing of the brown pigmentation, the hallmark of the nad1 mutation. Brownish empty nodules were only formed on the nad1-3 roots transformed either with the empty vector (Supplementary Figure  S3D-F ) or the constructs harboring the 78-bp deletion that mimics the first intron prediction at the Medicago genome database, based on the BG582085 EST sequence (Supplementary Figure  S3L, N ). This result proved that the lack of this sequence presumed to be the "first intron" abolishes the function of NAD1, so it is an integral part of the first exon. The mixture of wild-type nodules with proper zonation (Supplementary Figure  S3J ) and nodules showing brown pigmentation (Supplementary Figure  S3K ) developed on nad1-3 roots transformed with the first exon harboring the coding sequence for the NAD1 protein, suggesting the loss of proper regulation of NAD1 in the absence of the rest of the transcript and/or the intron and/or the 3 -UTR to restore the symbiotic phenotype.
Acknowledgments: This work was supported by the  Hungarian National Research Fund/National Research, Development and Innovation Office  grants  OTKA-67576 ,  PD104334/108923 ,  106068 ,  119652  and  120122/120300  as well as by the bilateral  Hungarian-French  collaborative project  LEGUMICS  ( NFÜ  grant  TÉT_10-1-2011-0397  and  ANR  grant  2010-INTI3-1602-01 ). The generation of the Tnt1 insertion lines in M. truncatula Jemalong 2HA genotype was financed in the framework of the European  FP6 (FOOD-CT-2004-506223 GRAIN LEGUMES IP, GLIP program ). We thank  G. Halász ,  H. Cs. Tolnainé  and  K. Miró  for skillful technical assistance. The authors are especially grateful to the French and Hungarian participant of the  LEGUMICS  project who contributed to the different communal symbiotic mutant screens.
Background: Fibrodysplasia ossificans progressiva (FOP) is a rare, severely disabling, and life-shortening genetic disorder that causes the formation of heterotopic bone within soft connective tissue. Previous studies found that the FOP prevalence was about one in every two million lives. The aim of this study is to estimate the FOP prevalence in France by probabilistic record-linkage of 2 national databases: 1) the PMSI (Programme de médicalisation des systèmes d'information), an administrative database that records all hospitalization activities in France and 2) CEMARA, a registry database developed by the French Centres of Reference for Rare Diseases. Results: Using a capture-recapture methodology to adjust the crude number of patients identified in both data sources, 89 FOP patients were identified, which results in a prevalence of 1.
FOP is caused by a mono-allelic gain of function mutation in the activin receptor type IA (ACVR1), also known as activin like kinase 2 (ALK2) type I receptor  [6]. The vast majority of cases are due to the same alteration, R206H, and results from a spontaneous de novo mutation  [7]. A paternal age effect has been reported  [8]. Previous studies in Spain and the United Kingdom estimated FOP prevalence at about one case per two million population  [9, 10]. FOP has no ethnic, gender or geographic predisposition. The aim of this study was to estimate the FOP prevalence in France, by cross-linking 2 national databases: First, the PMSI (Programme de médicalisation des systèmes d'information), which began operation in France in 1986. Its use became mandatory in all public and private health facilities in 2005 to receive reimbursement for medical services  [11].
Material Our study extracted and matched data from the two above mentioned health datasets, CEMARA and PMSI. CEMARA is a non -population -based registry that was launched in 2007. It collects information on rare diseases' epidemiology and related medical activities  [12]  from rare diseases Centers of Reference (CR) in France. Its goal is to improve the understanding of the burden of disease for rare conditions as well as the resources needed to treat those conditions. CEMARA also helps identify patients who might be eligible for natural history studies and clinical trials. Since 2007, a minimum data set has been collected from all RD patients who are diagnosed, followed, and treated at designated national CRs. The minimum dataset includes administrative and medical data such as: date of birth, place of birth, place of residency, sex, diagnosis, familial or sporadic occurrence, diagnostic confirmation method (clinical, molecular or other), and date of death.
FOP-related information captured by this database has been validated by the CR-MOC (Centre de Référence des Maladies Osseuses Constitutionnelles; reference center for constitutional rare bone diseases). Participants are physicians involved in diagnostic procedures (including medical geneticists, paediatricians, rheumatologists) or management (including orthopaedists, endocrinologists, psychologists, genetic counsellors, social workers). The system allows longitudinal tracking of individual patients. The Programme de Médicalisation des Systèmes d'Information (PMSI) [medical information system program] is an administrative database that captures two main types of healthcare events  [13]. First, it includes full coverage of all hospital admissions in Medicine, Surgery, and Obstetrics (Médecine Chirurgie et Obstétrique, MCO).
The MCO also includes day hospitalizations which are less than 24 h' duration (no overnight stay). Outpatients who receive only consultations, or radiological or other examinations are not recorded in the PMSI. The second type of events captured in PMSIvia the SSR (Soins de Suite et de Réadaptation; readaptation and rehabilitation centers) database-is admissions to intermediate care homes/facilities, as well as readaptation and rehabilitation centers. PMSI has used longitudinal patient identifiers since 2006. The most recent data set provides coverage through 2014. The main variables in PMSI are: hospital identification number, patient identification number, patients' sex and age, whether the patient died after the admission, diagnoses (ICD 10-1 primary diagnosis ±1 related diagnosis and up to 20 significant associated diagnoses)  [14] , total duration of the patient's stay, procedures performed during the stay, origin of the patient (home, transfer from other institution, etc.