dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The work presented in this manuscript was undertaken as part of the development of the  2020 European Seismic Hazard Model  (ESHM20) and 2020  European Seismic Risk Model. It benefitted from continued interaction and feedback from the scientists in the core development teams of the ESHM20 ( Céline Beauval ,  Pierre-Yves Bard ,  Roberto Basili ,  Andrea Rovida ,  Karin Sesetyan ,  Shyam Nandan ,  Celso Reyes ,  Stefan Wiemer ,  Domenico Giardini ) and the ESRM20 ( Helen Crowley ,  Jamal Dabbeek ,  Venetia Despotaki ,  Daniela Rodrigues ,  Vitor Silva ,  Luis Martins ,  Xavier Romão ,  Nuno Pereira ). Their efforts and expertise in compiling the model has been invaluable. In addition to the core teams, we have received valuable feedback on the models from the wider community of engineering seismologists and scientists, among which we wish to specifically thank  John Douglas ,  Milad Kowsari ,  Benedikt Halldorsson ,  Elena Manea ,  Ilaria Mosca ,  Roberto Paolucci ,  Olga Ktenidou  and  Paola Traversa.
The data set used for much of the analysis is the Engineering Strong Motion (ESM) database, for which we appreciate the ongoing efforts of  Giovanni Lanzano  and  Lucia Luzi , and their colleagues as  Insitito di Geofisica e Vulcanolgia (INGV) Milan. The OpenQuake software for PSHA calculation is developed and supported by the  GEM Foundation , and we wish to thank  Marco Pagani ,  Michele Simionato  and  Robin Gee  for their assistance and feedback in the implementation of the models for ESHM20 and ESRM20. Data analysis and visualisation presented here has been undertaken using exclusively open-source software, including the R language (thanks to the  R Core Team ) and Python and its scientific data stack (Numpy,  Scipy ,  Pandas ,  Matplotlib , h5py and Geopandas). Maps and further geospatial analysis were developed using QGIS (http://www.
Code Availability: Open-source Python implementations of the ground motions models and their adaptations presented here can be found in the online repository https://gitlab. seismo. ethz. ch/efehr/eshm20_gmms, while the files to configure and run the seismic hazard and risk calculations using the models can be found in the EFEHR Gitlab Repository: https://gitlab. seismo. ethz. ch/efehr. Data Availability: Ground motion data are taken primarily from the Engineering Strong Motion Data Flatfile (Lanzano et al. ,   2018), which is available for download from https://esm-db. eu/#/products/flat_file. Ground motion data from the RESORCE database can be retrieved from https://www. resorce-portal. eu/.
and the underlying datasets accessed via the European Site Response Model Datasets Viewer (https://maps. eu-risk. eucentre. it/) While site amplification and the role of site-to-site variability (𝜙 !4! is a key component of aleatory uncertainty, further adjustments to the aleatory uncertainty model of Kotha et al. (2020) were made. Initially, the Kotha et al. (2020) GMM adopted a homoscedastic model of region-corrected between-event variability (𝜏 # ) and site-corrected within-event variability (𝜙 # ).
( ORFEUS), who coordinate the acquisition and dissemination of ground motion data via services such as the European Integrated Data Archive (EIDA) and the Engineering Strong Motion database (ESM). Likewise, the development of opensource hazard and risk calculation tools such as OpenQuake have helped to create a common framework through which a growing body of researchers and stakeholder can develop and execute probabilistic earthquake hazard and risk tools. Having such data and tools available at the beginning of the model development, as we did in this case, allows us to explore and test out new ideas and understand their implications on estimates of shaking or loss. The revisions and updates to the model presented here emerged from an iterative process of implementation and user community feedback that allowed us to adapt our initial ideas to incorporate local knowledge.
The fs pulse duration of the XFEL allows probing of the atomic arrangement before any atomic motions induced by the heating of the XFEL probe  [8]. In addition, the high brightness of the XFEL pulse often enables (quasi-)single-shot measurements, ensuring better statistics, higher reliability of the data and time for scanning wide conditions during an experiment. While XAS is one of the most powerful synchrotron x-ray diagnostics for studying the interplay between the electronic structure and the local atomic arrangement, its implementation at XFEL facilities has so far been limited. The main barrier to performing XAS with XFEL sources arises as a natural side effect of how the pulses are generated. The self-amplified spontaneous emission (SASE) process, currently used in all hard X-ray facilities, results in high XFEL brightness but yields poor source longitudinal coherence and highly fluctuating spectral structure making XAS measurement particularly difficult.
Unfortunately, this technique developed so far still requires assuming sufficiently identical experimental conditions, which in the best case is not ideal and can also place undesirable constraints on the experimental conditions and configurations, even at the cost of losing a significant amount of data  [16]. In this work, we demonstrate a new approach to performing ultrafast single-shot X-ray Absorption Near Edge Spectroscopy (XANES) measurements at XFEL facilities. We also show the capability to perform Extended Xray Fine Absorption Spectroscopy (EXAFS) by accumulating tens of shots.
Results The presented experiment was performed at the XPP instrument of the LCLS  [17, 18]. Dispersive XAS measurements require normalizing a measured transmitted spectra I T (E) by the incident spectra I 0 (E) measured simultaneously for every single x-ray pulse. The energy dispersive experimental setup (see figure  1 ) consists of two identical spectrometers  [16, 19]  in a reference -transmission geometry. The first spectrometer, placed upstream of the sample, collects the reference source spectrum while the second spectrometer, placed downstream of the sample, collects the transmitted spectrum. This experimental setup, combined with a dedicated spectrometer alignment procedure, an advanced data analysis and an optimization of the XFEL spectral properties allows us to overcome the limitations with the typical shot-to-shot spectral and spatial variations that originates from the stochastic Self-Amplified Spontaneous Emission (SASE) FEL processes.
Despite the improved single shot spectral window (∼100 eV) obtained with the optimized XFEL settings, an extended spectral range is necessary for certain applications  [28, 29, 30]. This can be done by changing the central wavelength of the emitted XFEL spectrum. Note also that at LCLS at least, this can be done by the user in a matter of a fraction of a second within 100-200 eV. To test that our experimental approach was still valid in this case, we have changed the central energy by 100 eV over the data collection of several shots. Accumulated EXAFS spectra are shown for 1, 2, 4, 8, 16, 32 and 64 shots and are again compared with a typical long-timeaccumulation spectra from the ESRF synchrotron. Our results demonstrate that EXAFS features can be reconstructed with only a few shots and that averaging over 10-20 shots allows access to detailed spectral features over an extended energy range.
Discussion While it might have been assumed that averaging data to obtain a smooth incident spectra is an absolute requirement to provide X-ray absorption spectroscopy at XFELs, the presented results show that with the present spectrometer design, refined alignment procedure, and optimized machine parameters, X-ray absorption spectra can be measured with a single FEL pulse. The combination of over compression of the electron bunches with additional tuning of the 'spatial chirp' of the spectra provided single-shot XANES spectra with a few percent noise. In that operation mode, the XFEL bandwidth corresponds to almost 2%. It is important to note that this operation mode also implies a longer pulse duration of the XFEL, on the order of around 50fs. The spectral resolution is given by the thin-bend crystal spectrometers, ∼ 0.
Shock-compressed matter or ultrafast laser irradiation are systematically destructive experiments and their reproducibility are extremely sensitive to laser parameters and stability, sample qualities, or even alignment procedure. To date, the approach to managing this issue has been to average XAS spectra taken at similar conditions by ensuring parallel in-situ measurements for post facto sorting. This was performed at the cost of important error bars and loss of data and the presented approach puts an end to this critical experimental limitation  [16]. Furthemore, the application of high intensity magnetic field also seeks for high-quality single-shot XANES and X-ray Magnetic Circular Dichroïsm (XMCD) measurements. The fs pulse duration of the XFEL ensures as well the possibility to track unprecedented transient dynamics.
Analysis The python analysis code is freely available at https://github. com/marcocamma/dispersiveXanes. It includes tools to read and analyze the data as well as the scripts used to produce the figure of this manuscripts. Figures  SM2  and SM3  in the supplementary materials shows 2D images of the two spectrometers and their corresponding integrated spectra. We also show the difference of the 2D images and the ratio of the spectra with their calculated standard deviation in the considered spectral range. Figure  SM2  shows the results for a single-shot before image transformation while figure  SM3  shows the same single shot data but after data treatment. In the figures, we also overlay both spectra (red and blue respectively) to underline their differences before and after image transformation.
Once the image transformation is applied to the data, we can observe that the 2 spectra are very well matched. This analysis results in a significant improvement of the absorption fluctuations. For this specific shot, the standard variation σ, with no sample inserted in the beam, varies from 0. 71 to 0. 04 after data treatment. Author Contribution MH, AK, HTL, DZ, MCa designed and build the experimental setup and run the experiment with the help of MCh. HTL, DZ, MCa developed the on-line analysis. MH and MCa analyzed the data and wrote the manuscript with help from all other authors Aknowldgement The presented results were obtained at the XPP beamline of the LCLS facility at SLAC. In particular, we would like to thanks the support of the machine experts who optimized the XFEL beam spectral parameters for X-ray absorption spectroscopy.
1c ). UK Biobank data N = 484,307 with genetic and phenotypic data. 19,400,416 variants after QC filtering on INFO >0. 3 and Neff≥25 IC-RHR data N = 351,158 from 99 cohorts. 27,082,649 variants after QC filtering on INFO >0. 3 and Neff≥25 UK Biobank GWAS analysis UKB GWAS of HRC imputed SNPs HR ~SNP + sex + age + age 2 + BMI + heart rate measurement method + 30PCs + GenChip IC-RHR GWAS analysis GWAS of imputed SNPs (1000G) IVW Fixed-effects meta-analysis; Genomic control using lambda Internal replication 1) P < 1 × 10 -8 in the discovery (UKB+IC-RHR) meta-analysis 2) support (P < 0. 01) in the UKB GWAS alone 3) support (P < 0. 01) in the IC-RHR GWAS alone 4) concordant direction of effect between UKB and IC-RHR datasets UK Biobank + IC-RHR (N = 835,465) Genomic control using LDSC intercept Effect sizes UKB 492 SNPs and 1 proxy 2 SNPs F-statistic <10 Effect sizes IC-RHR 484 SNPs and 2 proxies.
Genome-wide meta-analysis of resting heart rate We performed a meta-analysis of RHR GWAS using 99 cohorts consisting of up to 351,158 individuals, which from here on will be referred to as the International Consortium of Resting Heart Rate (IC-RHR). Second, we performed a GWAS on 484,307 individuals from the UK Biobank. These large cohorts were meta-analyzed to include up to 835,464 individuals, in whom 30,458,884 directly genotyped and imputed autosomal variants were analyzed (Supplementary Data 1, Fig. 1a ). The meta-analysis revealed 493 independent genetic variants in 352 loci. Out of these 493 independent genetic variants, 68 were outside previously identified RHR-associated loci and 67 of those were internally replicated (Fig. 2a , Supplementary Data 2)  7, 8, 10. Out of the 425 genetic variants inside previously identified RHR-associated loci, a total of 376 were internally replicated.
051 ± 0. 002, suggesting little evidence of genomic inflation (Fig. 2c ). The QQ plots for the UK Biobank GWAS and IC-RHR meta-analysis are shown in Supplementary Fig. 1. The genomic control lambda's, LD-Score intercepts and the attenuation ratio statistics suggested no inflation due to non-polygenic signals  11, 12. Single nucleotide polymorphism (SNP) heritability of RHR, as calculated by LD score regression, was estimated to be 10%. A polygenic score weighted by the effect sizes of the IC-RHR explained 5. 33% of the variation in RHR in the UK Biobank. A Chow test indicated the absence of strong differences between participants with a history of any cardiovascular disease or use of RHR-altering medication versus participants without such a medical history (Supplementary Data 4). Genetic correlation analyses were performed and we observed significant correlations with anthropometric measurements, pulse wave reflection index, and physical activity measurements (Supplementary Data 5).
b Analyses performed to evaluate RHR-associated genetic variants and to gain further insights into the underlying biology. c Schematic presentation of the two-sample Mendelian randomization analyses of genetically predicted RHR on mortality and cardiovascular diseases. Effect sizes were taken from the IC-RHR data to test the associations with mortality and cardiovascular diseases in the UK Biobank. Effect sizes were taken from the UK Biobank to test the association with coronary artery disease and myocardial infarction in the CARDI-oGRAMplusC4D cohort, atrial fibrillation in the AFGen cohort, and any, ischemic, cardio-embolic, large artery and small vessel stroke within the MEGASTROKE consortium. BMI body mass index, GWAS genome-wide association study, HRC Haplotype Reference Panel, IC-RHR International Consortium for Resting Heart Rate, MB megabase, N sample size, Neff effective sample size, PC principal components, RHR resting heart rate, SNPs single nucleotide polymorphisms, QC quality control, 1000G = 1000 Genomes.
Query of dbNSFP. The dbNSFP database (version 3a ) was queried to obtain functional prediction and annotation of all potential nonsynonymous genetic variants  70. The dbNSFP database contains information on multiple prediction algorithms and conservation scores further detailed elsewhere  70. eQTL analyses. Colocalization of multiple expression quantitative trait loci (eQTL) was performed using SMR and HEIDI analyses (version 0. 710)  13  in data repositories from GTEx V7  71  , GTEx brain  71  , Brain-eMeta eQTL  72  , and blood eQTL from Westra  73  and CAGE  74. Colocalization analyses were performed to test whether the effect size of the RHRassociated variants on the phenotype is most likely mediated by gene expression  13. eQTL genes were considered as candidate causal genes if they achieved a significance after Bonferroni correction for the amount of eQTLs tested (P < 0.
Each cluster was named according to the name of the most central gene set as identified using the Affinity Propagation method. Identified meta-clusters were compared to the clusters found in the study of Eppinga et al. and were determined to be new if not a single cluster within the meta-cluster had been identified before. Clustering was performed using DEPICT software in python 2. 7  75  and visualization using Cytoscape 3. 8. 0  76. Tissue enrichment. DEPICT was used to find enriched tissues using the settings described above  36. Enriched tissues were further investigated by performing conditional analyses to provide evidence for an independent association with RHR. The following formula was used: T cond = Z t À ρ ts Z s ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 À ρ ts 2 p ð1Þ Here, Z t is the maximum Z-value of all tissue Z-values, Z s a vector of all tissue Z-values, and ρ ts the correlation between the tissue of Z t and the tissue of Z s 77.
Effect sizes from the ICBP-plus consortium were obtained from the meta-analysis of the UK Biobank and ICBP-plus, after subtracting the effects from the UK Biobank (for further details, see the section "Meta-subtract of blood pressure traits"). An overview of these studies is provided in Supplementary Data 16. We searched for proxies (LD > 0. 8) in case variants could not be found within the outcome datasets.
Study-specific details and methodology of the 99 cohorts of the IC-RHR are provided in Supplementary Data 1. Several software programs were used for the analysis, including mach2qtl  83  , minimac  84  , minimac2  85  , IMPUTE2  86  , GEEPACK  87  , ProbaABEL  88  , and MMAP  89  , for which version details per cohort are provided in Supplementary Data 1. A fixed effect meta-analysis using the inverse variance method in METAL was performed on all 99 cohorts, including up to a total of 351,158 individuals  90. Genomic control was applied at the study level by correcting for the study-specific lambda. All genetic variants were excluded if they had poor imputation quality score (Info < 0. 3) and effective sample size (N eff ) < 25 for the genetic variants computed as sample size × Info × 2 × minor allele frequency (MAF)×(1-MAF).
After these exclusions, a total of 19,400,415 and 27,082,649 variants remained available for the UK Biobank and IC-RHR GWAS respectively. Again, a fixed-effects meta-analysis using the inverse variance method in METAL was performed to pool the data from the UK Biobank and IC-RHR up to 835,465 participants using ~30M genetic variants  90. LD score regression software (v1. 0. 0) was used to calculate linkage disequilibrium score regression intercepts and attenuation ratios  11, 12. We corrected for genomic inflation prior to the meta-analysis by multiplying the standard errors with the square root of linkage disequilibrium score regression intercepts in the UK Biobank (1. 132 ± 0. 017) and the IC-RHR (1. 020 ± 0. 010)  11, 12. PLINK (version 1. 9) was used to prune genetic variants in a set of independently associated variants  91.
An independent genetic variant was defined as a genome-wide significant genetic variant in low LD (R 2 < 0. 005) with another genome-wide significant variant within a fivemegabase window. A genetic locus was defined as the most significant variant in a megabase region at either side of the independent genetic variant. A single one-stage replication analysis was performed next. The following criteria had to be satisfied for a signal to be reported as a replicated signal for RHR: 1. the sentinel genetic variant has P < 1 × 10 -8 in the discovery (UKB + IC-RHR) meta-analysis; 2. the sentinel genetic variant shows support (P < 0. 01) in the UKB GWAS alone; 3. the sentinel genetic variant shows support (P < 0. 01) in the IC-RHR meta-analysis alone; 4. the sentinel genetic variant has the concordant direction of effect between UKB and IC-RHR datasets; The sentinel genetic variants were compared with previous loci from previous GWAS of RHR and were determined novel if located outside a 1-megabyte distance of previously RHR-associated loci  7, 8, 10.
Linear regressions on RHR were performed in both populations, using cluster-robust standard errors with genetic family IDs as clusters to account for the relatedness among participants. Exclusions and covariates were similar to those used for the GWAS. Individuals belonging together based on 3rd-degree or closer as indicated by the kinship matrix (kinship coefficient > 0. 0442) provided by UK Biobank received a family ID. A Chow test was used to investigate whether there were significant differences in beta estimates in participants with and without cardiovascular disease or RHR-altering medication  93. The post-hoc quality control was performed using the statistical software STATA 15 (StataCorp LP). Genetics and regression analyses. All of the outcomes assessed in the UK Biobank that are reported in this manuscript have been adjusted for age, sex, the first 30 principal components (PCs) to account for population stratification, and genotyping array (Affymetrix UK Biobank Axiom® array or Affymetrix UK BiLEVE Axiom array).
Genetic variants were not excluded from further analyses if the F-statistic was <10 as this can exacerbate bias by increasing the chance of winner's curse  38. Exposure and outcome summary statistics were then harmonized using the TwoSample MR package  96. Forward strand alleles were inferred using allele frequency information and palindromic SNPs were removed if the MAF was above the recommended setting of 0. 42  96. MR-Steiger filtering was applied to explore pleiotropic effects through the assessment of potential reversed causation. R 2 for both the exposure and outcome were calculated and variants were removed from further analyses if the R 2 of the exposure is significantly lower (P-value < 0. 05) than the R 2 of the tested outcome  97. R 2 for linear traits was calculated as described above  95  , R 2 for binary outcomes was calculated on the liability scale  98.
These associations were assessed using the UK Biobank as an outcome cohort, considering this was the largest cohort with individual-level data available to us. Consequently, we used the independent weights of the IC-RHR meta-analysis to construct a weighted polygenetic risk score of RHR by summing the number of alleles (0, 1, or 2) for each individual after multiplication with the effect size between the genetic variant and RHR. We first calculate residual RHR by subtracting the results of the regression of RHR from the polygenetic score of RHR from RHR itself. Covariates of the regression included age, age 2 , sex, BMI, genotyping array, and PC1-PC30, analogous to the GWAS. Residual RHR, which characterizes the predicted RHR for an individual if their polygenetic score took the value zero, was then divided into 30 quantiles.
6. 3), the Two-SampleMR package (version 0. 5. 3)  96  , and the MR-Lasso source code 103. The multivariable MR analyses were performed using the MVMR (version 0. 3) 107 and MendelianRandomization (version 0. 5. 1) packages 110. Non-linear MR analyses were performed based on previously described methods 105. For the MR on (subtypes of) mortality, we considered a liberal two-sided P-value of P < 0. 05 significant for any of the outcomes using the IVW-MR random effects model. For the MR on cardiovascular diseases, we considered a Bonferroni corrected twosided P-value for the amount of unique outcomes (P = 0. 05/ 12 = 4. 17 × 10 -3 ) to be significant for the main IVW-MR random effects analyses, and a two-sided P-value between 4. 17 × 10 -3 and 0. 05 to indicate suggestive evidence for an association.
A two-sided P-value threshold of P < 0. 05 was adopted for the sensitivity analyses. Meta-subtract of blood pressure traits. We used the MetaSubtract (version 1. 60) package in R to remove the effects sizes of the UK Biobank from the largest blood pressure GWAS's to date in order to obtain the independent effect sizes of the ICBP consortium  19, 111. Effect sizes for the UK Biobank were obtained through linear regression analyses using every RHR SNP available in the UK Biobank as exposure, and systolic, diastolic, and pulse pressure as outcomes. Covariates included age, age 2 , sex, BMI, genchip, and PC1-PC30. Cluster-robust standard errors with genetic family IDs as clusters were used to account for the relatedness among participants. Individuals belonging together based on 3rd-degree or closer as indicated by the kinship matrix (kinship coefficient > 0.