dataesr/scientific-paragraphs-categorization huggingface dataset:
The first severe clinical event that occurred during natural history follow-up and time to first clinical event was analyzed for each of the event types. We analyzed electrocardiogram (ECG) data separately to further define arrhythmia subtypes. We compared clinical data from p. N215S patients to Fabry Registry data from patients harboring GLA mutations associated with classic Fabry phenotype followed at the same clinical centers. The mutation classification "classic Fabry disease" for these patients was verified using data from the Fabry-database. org mutation database (Fabry disease mutation database, 2017). The final list of classic mutations (Table  S1 ) was reviewed and approved by a senior geneticist experienced in the molecular diagnosis and management of Fabry patients (D. P. G).
| Statistical methods We analyzed IVST, LVPWT and eGFR data by age group: <25 years; 25-34 years; 35-44 years; 45-54 years; 55-64 years; and 65-74 years. Kaplan-Meier models depicting estimates of time to first reported event during natural history follow-up were performed for p. N215S and classic Fabry patients of both genders. We used descriptive statistics to characterize the individuals within the respective age groups. Statistical analyses were performed using SAS statistical software V. 9. 2 (SAS Institute Inc. , Cary, NC, USA).
| Fabry Registry The Fabry Registry (NCT00196742; sponsor: Sanofi Genzyme) is a multicenter, international, longitudinal, observational program designed to track the natural history and outcomes of patients with Fabry disease (Fabry Registry website, 2017). Patient and investigator participation is voluntary. Each independent site is responsible for obtaining patients' informed written consent to submit their health information to the Registry, and to use and disclose this information in aggregate analyses. The Registry protocol, informed consent form and any locally required authorization documents to send patient information to the Registry are reviewed and approved by the local fully constituted Institutional Review Board (IRB) or Independent Ethics Committee (IEC) unless the site provides the Registry with documentation that approval is not required or has been waived by a particular IRB/IEC.
| Patient demographics Data from 125 patients (59 males, 66 females) with the p. N215S genotype from the 10 centers were available in the Fabry Registry; the total number of p. N215S patients enrolled worldwide was 231 (105 males, 126 females). Mean age (range) at first assessment of IVST, LVPWT and eGFR in p. N215S males was 49. 1 (8. 7-72) years, 50. 6 (8. 7-71. 4) years and 51. 9 (8. 7-72. 5) years, respectively. For p. N215S females, mean age (range) was 41 (0. 5-69. 3) years for  IVST and LVPWT, 2) years for eGFR. These data were compared to data from 401 patients (164 males, 237 females) harboring classic Fabry disease mutations followed at the same centers.
| DISCUSSION The p. N215S missense mutation has been labeled as a later-onset cardiac phenotype of Fabry disease  (Eng et al. , 1993 (Eng et al. , , 1994;; Germain, 2010; Germain et al. , 1996; Ishii et al. , 1992 Ishii et al. , , 2002;; Nakao et al. , 1995; Patel et al. , 2015; von Scheidt et al. , 1991) ; however, published phenotypic data are still scarce. Three publications reported clinical findings in cohorts of patients with this mutation, but lacked detailed gender-specific clinical information  (Arends et al. , 2017; Havranek, Linhart, Urbanova, & Ramaswami, 2013; Patel et al. , 2015). In this Fabry Registry study, we analyzed data from clinical descriptions of 59 male and 66 female p. N215S patients, making it the largest cohort reported to date. Our study confirms that p. N215S is a disease-causing Fabry mutation with severe clinical manifestations essentially limited to the heart until late adulthood, especially in males.
Data may not have been entered retrospectively for enrolled patients, and given that newly diagnosed severe classic patients may be initiated on ERT soon after diagnosis without all recommended assessments having been performed, natural history data may be incomplete. This also applies to documentation of early disease symptoms in p. N215S patients diagnosed well into adulthood, which may not be fully available in the Fabry Registry. Furthermore, classic patients are more likely to be initiated on ERT and at a younger age (particularly male patients). Therefore, the rate of severe clinical events during natural history follow-up should be interpreted with caution. Cardiac or renal biopsy data from p. N215S patients were not available in the Fabry Registry. Cardiovascular risk factors including the extent of myocardial fibrosis, hypertension, hypercholesterolemia, obesity, tobacco use, lack of physical activity, and diabetes have not been assessed in this analysis.
Although unlikely, Fabry mutations currently categorized as classic in the Fabry-database. org mutation database may eventually prove to be associated with a later-onset phenotype based on evolving knowledge about the mutation's phenotypic characteristics. Incomplete data sets for left ventricular mass (indexed), urine protein-to-creatinine ratio, Fabry disease biomarkers [e. g. globotriaosylceramide (GL-3), globotriaosylsphingosine (lyso-GL-3)], and chronic white matter hyperintensities on brain magnetic resonance imaging precluded meaningful analysis of these parameters. Data from patients were analyzed per age category and, therefore, these analyses are cross-sectional rather than longitudinal. Not all p. N215S Fabry disease patients have been diagnosed and enrolled in the Fabry Registry.
This may be particularly applicable to female heterozygotes. Relatively few females were included in this analysis, since, given the Xlinked inheritance pattern, the female-to-male ratio should approach 2:1  (Germain & Jurca-Simina, 2018). A patient selection bias toward the inclusion of more severely affected patients cannot be excluded, whereas asymptomatic patients may not have come to medical attention. The Fabry Registry contains observational data of Fabry patients only. It is not a prospective clinical trial, and data interpretation is limited by the lack of an appropriately matched control group.
ACKNOWLEDGMENTS The authors were responsible for all content and editorial decisions and received no honoraria related to the development of this publication. The authors received editorial/ writing support in the preparation of this manuscript from  Tom Rouwette of Excerpta Medica , funded by  Sanofi Genzyme , and  Hans Ebels of Sanofi Genzyme. D. P. Germain  was supported by the  Plan National Maladies Rares of the French Ministry of Health  and  A. Ortiz  was supported by  Intensificaci on FIS  and  FEDER Funds   REDINREN RD012/0021. The Fabry Registry is sponsored by  Sanofi Genzyme. The authors thank the patients who agreed to participate in the Fabry Registry.
We use data from the Radial Velocity Experiment (RAVE) and the Tycho-Gaia astrometric solution catalogue (TGAS) to compute the velocity fields yielded by the radial (V R ), azimuthal (V φ ) and vertical (V z ) components of associated Galactocentric velocity. We search in particular for variation in all three velocity components with distance above and below the disc midplane, as well as how each component of V z (line-of-sight and tangential velocity projections) modifies the obtained vertical structure. To study the dependence of velocity on proper motion and distance we use two main samples: a RAVE sample including proper motions from the Tycho -2, PP-MXL and UCAC4 catalogues, and a RAVE-TGAS sample with inferred distances and proper motions from the TGAS and UCAC5 catalogues. In both samples, we identify asymmetries in V R and V z.
RAVE data selection RAVE is a spectroscopic magnitude-limited (9 I 12) southern hemisphere survey, which collected data from 2003 to 2013. It acquired line-of-sight velocities and stellar parameters (effective temperature, surface gravity, overall metallicity) in order to probe the chemical and dynamical evolution of the Milky Way. In this paper we use its latest data release, Data Release 5  (Kunder et al. 2017 ). DR5 contains 520,781 spectra of 457,588 unique stars. It includes observations that were previously discarded, resulting in ∼ 30, 000 more spectra than the fourth data release (DR4;  Kordopatis et al. 2013a ). DR5 also improves on the distance pipeline of DR4  (Binney et al. 2014) , especially as regards metal-poor stars, and applies a new calibration of its stellar parameters that improves their accuracy by up to 15% compared to DR4 (See Section 6 in  Kunder et al.
5 and T eff < 4000 K), metal poor stars ([M/H] < -1. 2 dex), hot stars (T eff > 7400 K) and spectra for which atmospheric parameters could not be determined reliably (Algoconv = 1), since in that case, the derived distances (obtained through isochrone fitting, see  Binney et al. 2014)  were not reliable either. Finally, stars at Galactic latitude |b| < 10 • were removed due to uncertain interstellar extinctions possibly affecting the derived distances. Our data selection applies to all proper-motion catalogues, with the exception of the Galactic latitude cut for the PPMXL catalogue, from which we remove all stars with |b| < 20 •. This cut was suggested by  Vickers et al. (2016) , who presented a correction to the PP-MXL proper motions. Although we do not make use of the code provided to correct the proper motions, the Galactic latitude cut solves the high discrepancy between the velocity fields of PPMXL and all other catalogues.
The Tycho-Gaia astrometric solution catalogue An important aspect of RAVE DR5 is that with almost 256,000 spectra that overlap with a TGAS star, this data release currently has a larger overlap with the Tycho-Gaia astrometric solution (TGAS) catalogue than any other spectroscopic survey. TGAS is the primary astrometric data set included in Gaia DR1, which includes proper motions, sky positions and parallaxes of 2,057,050 stars from the Hipparcos  (Perryman & ESA 1997; van Leeuwen 2007)   et al. 1979; Casagrande et al. 2010). The RAVE-TGAS sample with McMillan distances involves a cut additional to those listed in Section 2. 2: we removed stars with log g < 2, because distances to low-gravity stars are systematically overestimated (see  McMillan et al. 2017). The data selection results in a sample of 68,477 stars.
RAVE FIFTH DATA RELEASE W13 studied the differences in the Galactocentric velocity distributions for stars above and below the Galactic plane. For this, they used a sample of 72,635 red-clump stars obtained from the RAVE internal third data release  (Siebert et al. 2011a). For comparison purposes, we use a similar data set obtained from the internal fourth data release used in  Kordopatis et al. (2013b). We apply the same cuts described in Section 2. 2. The data selection results in a sample of 127,722 stars (Williams/Kordopatis sample). The resulting velocity fields differ mainly from W13 in being based on the full RAVE sample instead of only red-clump stars. This allows for a better comparison to the velocity fields obtained with our RAVE samples. In addition to the advantages of using RAVE DR5 data over RAVE DR4 (see Section 2.
The Galactocentric vertical velocity is thus given by: V z = V los sin b + V b cos b (3) where V b = 4. 74 d µ b + (-U cos l sin b -V sin l sin b + W cos b). (4) Since the small uncertainty in V los from RAVE does not affect the V z pattern, we are in a position to study how distance and proper motion affect the vertical velocity. First, we want to avoid the effects of disc perturbations. For this + = we used Galaxia  (Sharma et al. 2011 ) as a front end for the (axisymmetric) Besançon Galaxy Model  (Robin et al. 2003). Galaxia uses isochrones from the Padova database to compute photometric magnitudes for the model stars  (Bertelli et al. 1994; Marigo et al. 2008 ) and it quickly generates from models mock observational catalogues that cover a specified area on the sky with any specified selection function.
This can be seen in Fig. 6 , which displays the components of V z in the Williams/Kordopatis sample. Here, although the pattern obtained from the distance has lower velocities than in Galaxia, the pattern of proper motions is strong enough to dominate the contribution from V los , breaking the balance previously shown in Galaxia and generating the pattern of a breathing mode. In Fig. 7  we study the effects of introducing errors to the mock data from the axisymmetric and relaxed Galaxia model. The first panel shows the Galaxia sample with no errors. In the second panel we introduce artificial errors and draw new velocity values, from a normal distribution centred on the actual velocity and σ µ = 5 mas yr -1. Although these errors are larger than the typical error in our DR5 samples (up to σ µ ∼ 4 mas yr -1 ), the assigned values produce some clumps but do not affect the general structure of the sample.
2, using the inverse parallax as a distance estimate produces biased distances for at least ∼ 50% of the stars in both samples. RAVE-TGAS sample as well as the full TGAS sample. The parallaxes of Gaia DR1 suffer from a possible offset by ±0. 1 mas of the parallax zero point and ±0. 2 mas due to position and colour-dependent systematics. A detailed description of these systematics is given in  Lindegren et al. (2016) , appendix E. It is therefore recommended to add a systematic error of ±0. 3 mas to the parallax uncertainties (Gaia Collaboration et al. 2016b). The dotted curves show the same TGAS data but consider the systematic errors. As can be seen, the full TGAS and our RAVE-TGAS sample have a relatively large number of stars (∼ 50%) within f Obs < 0. 2. Nevertheless, using the inverse parallax in the RAVE-TGAS sample would lead to potentially biased distance estimates for a minimum of ∼ 50% of the stars.
ACKNOWLEDGEMENTS I. Carrillo  is grateful to  Ralf-Dieter Scholz  for the many discussions about proper motions, to  Mary E. K. Williams  for comments that improved the quality and clarity of this paper, to  Lawrence M. Widrow  and  Matthew H. Chequers  for useful discussions about breathing and bending modes, to  Jorrit Hagen  for discussions on the effects of distance errors and to  Haifeng Wang  for discussions on velocity asymmetries. Funding for RAVE has been provided by: the  Australian Astronomical Observatory ; the  Leibniz-Institut fuer Astrophysik Potsdam (AIP) ; the  Australian National University ; the  Australian Research Council ; the  French National Research Agency ; the  German Research Foundation  ( SPP 1177  and  SFB 881 ); the  European Research Council  ( ERC-StG 240271 Galactica ); the  Istituto Nazionale di Astrofisica at Padova ; The  Johns Hopkins University ; the  National Science Foundation of the USA  ( AST-0908326 ); the W.
The green fluorescence strips were recorded at indicated time points and then analyzed with Image J software. All results represent images of 20 cells from three independent experiments. HR and NHEJ assays. To measure the repair frequency, DR-GFP U2OS or EJ5-GFP U2OS cells were plated into six-well plates and transfected the next day with the indicated siRNAs and pCBASceI vector using Lipofectamine 2000 (Invitrogen)  60. Forty-eight hours later, cells were harvested and washed with PBS, and the GFP positive cells were recorded by flow cytometry on a FACSCalibur. Neutral comet assay. For evaluating DNA double-strand breaks, the neutral version of comet assay was performed. Briefly, cells were exposed to 5 Gy and recovered at different time points. Then the cells were collected and mixed with 0.
8% low melting agarose and layered onto agarose-coated slides. Slides were then submerged into cold lysis buffer for 3 h. After lysis, slides were incubated for 1 h in electrophoresis buffer. After electrophoresis, slides were neutralized, placed into 100% ethanol and then air-dried. Slides were subsequently stained with 5 μg/ml propidium iodide and images were taken using a fluorescent microscope (Olympus). Average Olive Tail Moment (OTM) was analyzed (50 cells/slide) by using Comet Assay Software Project Casp-1. 2. 2 (University of Wroclaw, Poland). All experiments were repeated three times. Statistical analysis. Data are represented as mean ± s. d. as indicated from three independent experiments. Significance of differences was evaluated by Student's ttest. NS: non significant; *statistically significant (p < 0.
Acknowledgements The mass spectrometry was performed with equipment at the  City of Hope Mass Spectrometry and Proteomics Core Facility. We are grateful to  Roger Moore  for providing technical support on the mass spectrometry analysis. We also thank  Dr. John Pascal  for His-PARP1 construct. We appreciate  Dr. Jeremy Stark  generous sharing of DR-GFP  U2OS  and  EJ5-GFP U2OS  cell lines. This work was supported in part by grants from  National Institutes of Health  ( CA132755 ,  CA130899  and  CA187209  to X. Y. ) and the  Department of Defense  ( BA160420  to X. Y. ). X. Y. is a recipient of  Research Scholar Award  from  Leukemia and Lymphoma Society.
Climate Characterization The 168 geo-localized populations were characterized for 20 biologically meaningful climate variables retrieved from the ClimateEU database (Table  1 ). Climate data has been generated with the ClimateEU v4. 63 software package 1  based on methodology described by  Hamann et al. (2013). The grid resolution of the 20 climate variables (∼1. 25 arcmin, ∼600 m) was smaller than the average distance among populations. Climate data were averaged across the 2003-2013 annual data. In addition, altitude was obtained from http://www. gpscoordinates. net. Following  Hancock et al. (2011) , the set of 21 climate variables was pruned based on the pairwise Spearman correlations of the variables (Supplementary Figure  S1 ), by taking into account only variables that did not display a Spearman's rho greater than 0.
8. This step was performed to avoid intercorrelation between two variables. In cases where variables were strongly inter-correlated, we selected the variable with the most obvious link to the ecology of A. thaliana. The final set of six non-correlated climate variables considered in this study was composed by mean annual temperature, mean coldest month temperature and precipitations in winter, spring, summer, and autumn. In order to compare the level of climate variation among the 168 populations of the Midi-Pyrénées region with the level of climate variation among natural populations of A. thaliana located in Europe, we first extracted data for the six climate variables (ClimateEU database) for 521 locations where A. thaliana have been collected and geo-localized in Europe, including 95 French locations  (Hancock et al.
, 2011; Brachi et al. , 2013). To visualize the climatic space encountered by A. thaliana at different geographical scales, we then performed a principal component analysis (PCA) based on the 689 locations by using the ade4 1. 7-6 version package in the R environment  (Chessel et al. , 2004; Dray et al. , 2017). Finally, the percentage of climatic variation in Europe observed among the 168 populations was calculated by dividing the extent of variation observed on the two first Principal Components (PC climate ) at the scale of the Midi-Pyrénées region by the extent of variation observed at the European scale.
Spatial Grains of Climatic Variation To estimate the spatial grain of variation of each climate variable, a spectral decomposition of the spatial relationships among the 168 populations was first modeled with Principal Coordinates of Neighbor Matrices (PCNM), by running the pcnm() function implemented in the vegan package (R package version 2. 3-5;  Oksanen et al. , 2016)  using the Euclidean distance matrix based on the GPS coordinates of the 168 populations. This analysis allows decomposing the spatial structure among the sites under study into orthogonal PCNM components corresponding to successive spatial grains  (Borcard and Legendre, 2002). The first PCNM components define a large spatial grain, while the last PCNM components correspond to finer grains  (Borcard et al. , 2004; Ramette and Tiedje, 2007).
After this step, PCR products were purified with Agencourt R AMPure R XP paramagnetic beads. The quality of the libraries was assessed with the Advanced Analytical Fragment Analyzer and libraries were then quantified by qPCR by using the Kapa Library quantification Kit. Samples were sequenced at the Get-PlaGe core facility (INRA of Toulouse) on an Illumina HiSeq 3000 sequencer by using a paired-end read length of 2 × 150 bp with the Illumina HiSeq3000 Reagents Kits. In particular, each Illumina lane was composed of a pool of 14 DNA samples. Twelve lines were used in total to sequence the DNAs (i. e. , 14 A. thaliana populations were sequenced on each Illumina lane). Raw data for each population used in this study are available at the NCBI Sequence Read Archive (SRA) 2  through the study accession SRP103198.
Given the close similarity of the estimates obtained on the 32 sub-data sets, we retained for further analyses the matrix ˆ 1 (obtained on the first sub-data set) as an estimate of the scaled covariance matrix of population allele frequencies. To evaluate the spatial scale of genomic variation, we first performed a singular value decomposition (SVD) of ˆ 1. The coordinates of the two first resulting Principal Components (PC genomic ) were then regressed against latitude and longitude of population i according to the following formula (PROC GLM procedure in SAS 9. 3 SAS Institute Inc. , Cary, NC, United States): PC genomic coordinates i -latitude i + longitude i + latitude * i longitude i + ε i (1) Finally, genome-wide analysis of association with climate covariables were carried out under the AUX model (-auxmodel) parameterized with 1.
Testing the Power of GEA Analysis to Identify True Positives Because climate variation was significantly associated with the first PC genomic (Supplementary Table  S2 ), we performed a simulation study aiming at characterizing how the degree of correlation between a given environmental covariable and the major axis of population structure might affect the sensitivity of BayPass to detect associated SNPs. For this purpose, using the R function simulate. PCcorrelated. covariate that we specifically developed in this study (Supporting Information of Supplementary Material) and that will be integrated in a future release of the BayPass software, we first simulated 10 environmental covariable vectors Zi (of length 168, the number of populations) with a Spearman's coefficient correlation ρ i ranging from 0 to 0.
9 (ρ i = 0. 1(i-1) for i = 1,. , 10) with the first PC genomic. For each covariable vector, 25 data sets consisting of read counts for 10,000 SNPs including 100 associated SNPs (with a regression coefficient set at 0. 1) were simulated under the BayPass inference model using the function simulate. baypass() available from the BayPass software package  (Gautier, 2015). For all the simulated data sets, the simulation model was specified with the estimated matrix ˆ 1 and the estimated posterior means of the two shape parameters of the Betadistribution across population allele frequencies (\hat{a} = 2. 78 and \hat{b} = 0. 710 with option pi. maf = 0. 01). In addition, to preserve the characteristics of our design, we kept the same population haploid pool sizes and sampled the vector of SNP read coverages (across pool samples) from the observed data.