dataesr/scientific-paragraphs-categorization huggingface dataset:
As shown in Figure  4A , the African American individuals who carry the minor allele rs1131446-T, marking HLA-B*57:03, carry simultaneously most of the 44 SNP minor alleles which are in strong LD (median is 37 SNPs carried out of 44). Reciprocally, each one of the 44 SNP minor alleles are carried by most HLA-B*57:03 carriers in both Cs1 and Cs2 groups (Figure  4B ). Figure  3B  presents the mRNA expression pattern in PBMCs for the 44 SNPs, as determined by the GTEx database (see methods). Importantly, a very similar pattern to the one observed for the SNPs of the HLA-B*57:01 haploblock (Figure  3A ) was found, with the same differentially transcribed proteins and non-coding RNAs (Figure  3B  and Supplementary Table  2B ), including MICB, HLA-B, HLA-S, ZBTB12, PSORS1C3, HCG27, and XXbacBPG181B23.
Participant phenotypes and case/ control matching In this study, we used genotyped data from the International Collaboration on HIV-1 Genomics (ICGH). The ICGH project, initiated in 2012 with the support of the National Institutes of Health, aimed to consolidate genomic datasets from HIV-1 infected individuals worldwide. The consortium comprised 26 cohorts of seropositive subjects genotyped on diverse platforms, representing four continents (US, Europe, Australia, Africa). Genotypes for uninfected control individuals were obtained from three participating centers, the Illumina genotype control database and the Myocardial Infarction Genetics Consortium (MIGen)  (9, 28). before centralizing all the data for combined analysis. However, to ensure consistency, additional quality control measures were implemented once the data were submitted, as described in the initial publication of ICGH  (9).
Each dataset underwent preliminary quality control procedures All the individual cohorts contributing to the ICGH effort obtained ethical approval from their respective country institutions. The initial publication of ICGH in 2013 focused on susceptibility to HIV-1 infection (9) and included a comparison of 6,300 seropositive individuals with 7,200 uninfected controls of European descent. A subsequent publication in 2015 investigated the genetic association with viral load setpoint among the 6,300 seropositive subjects of European descent  (9). In these studies, the cohorts were categorized into six groups of matched cases and controls based on genotyping platforms and geographic origin. The present study focuses on two groups of Elite Controllers (EC) from the European cohorts, characterized by a viral load of less than 1,000 copies/mm3.
Genotypic data for these groups were generated using three genotyping arrays: Illumina 550, Illumina 650, and Illumina 1M. Prior to analysis, we conducted standard quality control (QC) procedures on each set (CTR1/ill1 and CTR2/ill2) to ensure the use of clean data. Specifically, we filtered out monomorphic or rare variants with a minor allele frequency (MAF) less than 1%, as well as structural variations such as insertion-deletions. We also excluded sites with a missingness rate above 0. 02 or a Hardy-Weinberg equilibrium (HWE) p-value lower than 10-6. Moreover, we examined the consistency between recorded sex information and sex inferred from the X chromosome, assessed heterozygosity and homozygosity rates, and verified the level of relatedness by calculating identity by descent (IBD). A similar QC process was separately conducted for the Cs1 and Cs2 groups of African American descent, who were genotyped on the Illumina 650 and 1M platforms.
Imputation Before imputation, additional quality control (QC) steps were applied to the two sets of European descent (ill1/CTR1 and ill2/ CTR2). These QC measures utilized the checkbim steps of the McCarthy Group Tools  (30) , which involved removing single nucleotide polymorphisms (SNPs) with A/T and G/C alleles if the minor allele frequency (MAF) exceeded 40%. SNPs with discordant alleles, those with more than a 20% difference in allele frequency, and SNPs not present in the 1000 Genomes reference panel were also excluded. These QC steps ensured the utilization of highquality data for the subsequent imputation process, enabling accurate results. For ill1/CTR1, a total of 387,495 common SNPs were imputed, while for ill2/CTR2, 271,572 common SNPs were imputed. The imputation process followed a specific protocol: phasing was performed using Eagle2.
Stratification To assess patterns of population structure in the ill1/CTR1 and ill2/CTR2 datasets, a principal component analysis (PCA) was conducted. The analysis aimed to quantify the genetic ancestry of the participants. A set of 510,420 informative SNPs for ill1/CTR1 and 523,850 informative SNPs for ill2/CTR2 was selected for ancestral origin determination. To mitigate the influence of linkage disequilibrium, pruning was applied using an r 2 threshold of 0. 3, a sliding window size of 50, and a step size of 5. Regions on chromosomes 6, 8, and 17 with high linkage disequilibrium were excluded from the analysis. The results of the PCA confirmed the homogeneity of both the ill1/CTR1 and ill2/CTR2 datasets, consistent with the findings reported in the first ICGH publication  (9).
Association testing by meta-analysis For the ill1/CTR1 and ill2/CTR2 datasets, a logistic regression analysis comparing Elite Controllers (ECs) and controls (Ill1 vs CTR1 and ill2 vs CTR2) was conducted using SNPtest  (35). The analysis focused on 35,552 variant dosages from the Major Histocompatibility Complex (MHC) region under an additive model. To account for population structure and minimize potential confounding effects, the first five principal components (PCs) were included as covariates in both analyses. Following the individual dataset analyses, a meta-analysis was performed using GWAMA software  (36)  to combine the p-values from each dataset. The meta-analysis aimed to identify significant associations that satisfy the following conditions: a combined pvalue less than or equal to 5.
MAFcv3 = MAF of the SNP in EC with 100 copies/ml< viral load< 1000 copies/ml. MAFctr = MAF of the SNP in controls (uninfected). Following the identification of 2,626 significant SNPs from the meta-analysis, we further examined the subset of SNPs that exhibited an increase (or decrease) in minor allele frequency (MAF) of more than 30% in EC2 compared to EC3 and controls. Out of the 2,626 significant SNPs, 379 SNPs met this criterion, indicating a substantial increase (or decrease) in MAF in EC2. To work with a more manageable set of representative SNPs, we performed a clumping analysis using the program PLINK (37) on these 379 SNPs. The clumping analysis grouped together SNPs with an r 2 correlation of 0. 8 or higher and located within a physical distance of 200,000 base pairs. This clustering process resulted in a final set of 54 representative SNPs that could be used for further analysis.
Computation of the LD in various subgroups To identify SNPs in linkage disequilibrium (LD) with SNP rs1131446, which serves as a marker for HLA-B*57:03 in African Americans, we employed the software PLINK  (37)  to conduct LD analysis on the genotypes of our cohorts. PLINK utilizes the maximum likelihood method to estimate LD coefficients, including the correlation coefficient (r²) and normalized LD coefficient (D'), between pairs of SNPs  (38). To establish appropriate threshold values for LD analysis, we referred to values observed in the European haploblock with SNP rs2395029, the reference marker for HLA-B*57:01 in Europeans. Based on this, we selected a threshold of an r² correlation coefficient greater than 0. 2 and a D' coefficient greater than 0. 8 with rs1131446 in African Americans.
For each SNP, it was also interesting to investigate the number of subjects carrying its minor allele in the various groups: the control group CTR, the elite control group EC (as well as EC2 and EC3), among the carriers of HLA-B*57:01 in Europeans or among the carriers of HLA-B*57:03 in African Americans (CTR_HLA-B*57:03, EC2_HLA-B*57:03, CS1_HLA-B*57:03, CS2_HLA-B*57:03). The HLA-B*57:01 carriers are those who have at least one G for SNP rs2395029, and the HLA-B*57:03 carriers are those who carry at least one T for SNP rs1131446 (since it is a good proxy of HLA-B*57:03 with r 2 >0,8). For each SNP, we thus computed a score in a given subgroup, corresponding to the global percentage of subjects wearing its minor allele, as presented in equation  (3). Biological exploration of the SNPs with genetic annotation (Annovar) We annotated the 379 SNPs from the European haploblock and the 44 SNPs from the African haploblock using Annovar (39) on dbSNP (avSNP150)  (40)  and the RefGene gene database  (41).
Acknowledgments The authors are grateful to all the patients and medical staff who contributed to the collection of the cohorts. The authors thank  Stuart Z. Shapiro  ( Program Officer, Division of AIDS, National Institute of Allergy and Infectious Diseases ) and  Stacy Carrington-Lawrence  (Chair of  Etiology and Pathogenesis ,  NIH Office of AIDS Research ) who accompanied the creation of the International Collaboration on the Genomics of HIV (ICGH). The authors are also grateful to all the scientists who participated to the  ICGH.
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Initial funding for the International Collaboration for the Genomics of HIV was provided by the NIH Office of AIDS Research. This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract HHSN26120080001E. MT is recipient of a fellowship from the Foundation FundaMental. The Laboratory GBCM acknowledges the program Mećeńat-Santéof Mutuelles AXA for funding its research. The funders had no role in study design, data analysis, decision to publish, or preparation of the manuscript.
All the PNG simulations are run with the ΛCDM cosmological parameters shown in Table  1. They follow the evolution of 512 3 dark matter particles from z = 127 down to z = 0. The following simulation products are publicly available: particle data at z = 0. 0, 0. 5, 1. 0, 2. 0, and 3. 0, and the power spectrum and bispectrum measurements. See https://quijote-simulations. readthedocs. io/en/latest/png. html for more details.
Non-Gaussian Initial Conditions To run non-Gaussian simulations, we use the method developed in  Scoccimarro et al. (2012)  to generate initial conditions that have primordial non-Gaussianity, and we implement several small changes to the public code released by  Scoccimarro et al. (2012). Our updated code is available at https://github. com/dsjamieson/2LPTPNG. Here, we briefly review this method and refer the reader to  Scoccimarro et al. (2012)  for more details. Generating initial conditions with local non-Gaussianity is straightforward. First, the modes of a Gaussian primordial potential field, Φ(k), are generated in Fourier space from the input power spectrum. This field is then inverse Fourier transformed to real space. The real space field is squared, mean-subtracted, scaled by the desired amplitude, f NL local , and then added back to the original potential.
N-body Evolution After generating the initial conditions, we follow their gravitational evolution down to z = 0 using the TreePM code GADGET-3  (Springel 2005 ). As stated above, we use the same parameter settings as the original QUIJOTE simulations. Halos are identified through the Friends-of-Friends (FoF) algorithm  (Davis et al. 1985)  with a value of the linking length equal to b = 0. 2.
Conclusions In this work, we have presented and validated a suite of Nbody simulations that contain primordial non-Gaussianity: QUIJOTE-PNG. These augment the extensive QUIJOTE suite, matching the random seeds, configurations, and data formats of the previously run QUIJOTE simulations. This allows for consistent and seamless joint analyses of available information from the primordial universe and cosmological parameters. The simulation data, including particle data at z = 0. 0, 0. 5, 1. 0, 2. 0, and 3. 0, along with the power spectrum and bispectrum measurements at z = 0, are publicly available. 7. The constraining power of the bispectrum as a function of scale compared to the information available in the primordial field. The dashed lines denote the constraining power in the primordial potential (without any marginalization), the solid lines denote the constraining power at z = 0.
The authors are very grateful to  Oliver Philcox ,  Daan Meerburg ,  Simone Ferraro ,  Shirley Ho ,  David Spergel , and  Yin Li  for their advice on numerous aspects of our analysis. G. J. , M. L. , and M. B. were supported by the " Combining Cosmic Microwave Background and Large Scale Structure data: an Integrated Approach for Addressing Fundamental Questions in Cosmology ," funded by the  MIUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN)  Bando 2017 -grant  2017YJYZAH. D. K. is supported by the  South African Radio Astronomy Observatory (SARAO)  and the  National Research Foundation  (Grant No. 75415 ). B. D. W. acknowledges support by the  ANR   BIG4  project, grant  ANR-16-CE23-0002  of the  French Agence Nationale de la Recherche ; and the  Labex ILP  (reference  ANR-10-LABX-63 ) part of the Idex SUPER, and received financial state aid managed by the  Agence Nationale de la Recherche , as part of the program Investissements d'avenir under the reference  ANR-11-IDEX-0004-02.
The  Flatiron Institute  is supported by the  Simons Foundation. L. V. acknowledges  ERC (BePreSySe , grant agreement  725327 ),  PGC2018-098866-B-I00 MCIN/AEI/ 10. 13039/501100011033  y  FEDER "Una manera de hacer Europa," and the "Center of Excellence Maria de Maeztu   2020-2023 " award to the  ICCUB  ( CEX2019-000918 -M funded by  MCIN/AEI/10. 13039/501100011033 ).
In this work, we propose the first quantitative real-time schemes that will be part of an operational DAS-based EEW system. Early warning is typically achieved by (1) detecting an earthquake, (2) determining its location, (3) resolving the earthquake source parameters (magnitude and stress drop  24  ), and (4) predicting ground shaking intensities, typically peak ground velocities (PGV) and peak ground accelerations (PGA)  25. To the best of our knowledge, these four real-time objectives are yet to be addressed using DAS. Real-time earthquake detection and location may be achieved using either well-established point-sensor-based approaches  [26] [27] [28] [29]  applied to single or multiple DAS channels, or array processing techniques such as beamforming  12, 30, 31. While detection can be achieved with relative ease even with point-sensor -based algorithms  27, 29  , earthquake location poses several challenges that are unique to DAS data  12, 22.
The recorded strain wavefield may not be coherent enough for reliable earthquake location or may be dominated by scattered waves. In addition, the geographical locations of measurements along the fiber need to be calibrated to reduce earthquake location errors. These issues will be considered when devising real-time earthquake location schemes, a subject of subsequent manuscripts. Here, we address the last two objectives: real-time magnitude estimation and shaking intensity prediction. Most operational EEW systems rely on empirical relations for both magnitude estimation and ground motion prediction  32, 33. The robustness of these relations largely relies on the quality, quantity, and magnitude range of available earthquake observations  34. Since DAS is a relatively new seismic measurement technology  9  , current earthquake DAS datasets are insufficient in all aspects to devise robust empirical methods, and a physics-based approach that does not rely on data availability should be developed  24, 35, 36.
(1) d n dt n D(t) = d n-1 dt n-1 ǫ(t)/p x ′ The approaches presented in this manuscript build on the above-mentioned advancements in physics-based EEW  24  and DAS earthquake data processing  37. The potential of the modified strains to ground motions conversion and the new magnitude expression for EEW are examined in conjunction with a theoretical ground motion prediction equation (GMPE)  40. In the following sections, we present and validate a computationally efficient real-time protocol that relies on straightforward analytical formulations for the analysis of DAS recorded earthquakes. Strain-rates are converted to ground accelerations using a real-time adapted slant-stack approach. Then, earthquake magnitudes are estimated via an analytical expression derived using the Omega-squared source spectra model  41, 42.
To demonstrate the merits of the proposed DAS-based EEW schemes, we compiled a DAS earthquake dataset from different tectonic environments. Data were recorded by four different ocean-bottom fibers: two offshore Greece  13, 16, 37, 43  , one offshore France  13, 19, 37, 44  and one offshore Chile (Supplementary Fig. 2 ). The measurements in Greece, France and Chile were conducted using three different interrogator units: a Febus A1 DAS interrogator, an Aragon Photonics hDAS interrogator and an ASN OptoDAS interrogator, respectively (See "Earthquake dataset" in Methods). We analyzed a total of 53 DAS recorded earthquakes that range from magnitude 2 to 5. 7 (Supplementary Fig. 2 ) at hypocentral distances of 17 to 365 km (Supplementary Fig. 3 ). Earthquake metadata are provided in Supplementary Table  1.
Results Using DAS data for magnitude estimation. Ideally, moment magnitude should be estimated using seismic recordings of ground displacements, D, rather than ground velocities, V, or accelerations, A, and the signals should include as much of their low-frequency portion as possible to avoid magnitude saturation  23, 35. Ground displacements can be obtained from well-coupled DAS measurements by integrating strain measurements in time (or double integration of strain-rates) and dividing them by the apparent slowness (n = 0 in Eq. 1)  11, [37] [38] [39]. This conversion approach has been demonstrated by previous studies that considered DAS instrument response  11  and coupling  45. However, the use of DAS converted ground displacements is challenging given the inherently high instrumental noise levels, especially at large distances along long fibers  13, 46, 47.
Strain-rates to ground accelerations conversion. The performance of the conversion algorithm (See "Real-time strain-rates to ground accelerations conversion" in Methods) is demonstrated for a magnitude 3. 8 earthquake recorded offshore Chile at a hypocentral distance of 60 km (See map in Supplementary Fig. 2 ) for a single DAS channel at a distance of 103 km along the fiber (Fig. 2 ). Note that direct P-waves are not visible, although P-wave induced scattered-waves are clearly seen (1-6 s in Fig. 2a, b ). The same analysis for the largest earthquake in the dataset, a magnitude 5. 7, is shown in Supplementary Fig. 4 ; for this earthquake, strain-rate amplitudes exhibit some saturation (See Discussion). The real-time slant-stack approach resolves the apparent velocities of the different seismic phases: ~ 4.
However, the results in Fig. 3  show that while the discrepancies between the synthetic earthquake stress drop and that used in Eq. (  7 ) may have a significant impact on magnitude estimation, the effect on ground motion prediction is minimized, and the approach may be reliably used even with a biased stress drop. The effect of stress drop variability will be further examined using recorded earthquakes in the following section. Real-time magnitude estimation and peak ground shaking prediction. The performance of the real-time strain-rates to ground accelerations conversion, magnitude estimation, and ground motion prediction are demonstrated using a composite earthquake catalog of 53 DAS and point-sensor (seismometer and accelerometer) recorded earthquakes from Greece, France, and Chile (See "Earthquake dataset" in Methods, earthquake catalog in Supplementary Table  1 , and maps showing the locations of earthquakes, fibers, and pointsensors in Supplementary Fig.
Thus, in subsequent analysis, we focus on the discrepancies between predicted and observed PGV and PGA as a measure for the algorithms' performance, and provide less attention to the agreement between real-time and catalog reported magnitudes. Magnitude is estimated using several well-coupled fiber segments for each cable. Coupling quality is evaluated by inspecting earthquakes' seismic wavefield along the fiber and identifying sections that display continuous seismic wavefronts and small amplitude variabilities of less than 4 dB  37. DAS data are converted to ground accelerations and an initial magnitude estimate is obtained two seconds following P-wave detection at the first fiber segment, and is continuously updated with increasing data intervals and as the earthquake is recorded at additional locations along the fiber.
5 s from P-wave detection. Similar behavior is observed for the catalog Magnitude 5. 7 earthquake shown in Supplementary Fig. 4d. Magnitude estimates improve with time as seen in Fig. 4a-c  where real-time and catalog magnitudes are compared at 4, 10 and 15 s from the first P-wave detection, for the entire dataset. A comparison between predicted (Eq. 10) and observed (See "Earthquake dataset" in Methods) PGV and PGA at 15 s indicates that the residuals are independent of hypocentral distance (Fig. 4d ,e and Supplementary Fig. 6d, e ) and catalog magnitude (Supplementary Fig. 7d, e ), and that their standard deviations are relatively small, only slightly higher than the optimal values, i. e. , within-event variabilities reported in the caption of Fig. 4. Average within event variabilities, i.
5b ). The time-gain achieved by using the offshore Chile fiber compared to the current point-sensor network is defined here as the difference between the P-wave arrival at the closest fiber segment and at the fourth seismic station, as commonly required by EEW systems  55. This time-gain may be as large as 25 s for earthquakes that occur near the fiber and may even result in early detection and alert issuance for onland earthquakes where point-sensor coverage is sparse (Fig. 5b ). These precious seconds can have a decisive impact on mitigating the risk posed by potentially catastrophic offshore earthquakes. Together with the time-gain for offshore earthquakes, DAS-based EEW can potentially outperform pointsensor-based EEW for several reasons. When implemented on well-coupled fiber segments, magnitude estimates are more reliable since data from many closely spaced DAS channels are averaged, reducing the impact of outliers and smoothing local effects.
The dominance of these scattered waves will pose difficulties for earthquake location, since using scattered P-waves instead of direct P-waves will likely point to the scatterers' locations rather than the earthquake's source. Because P-wave based magnitudes are typically under-estimated (Fig. 2d ), they are not expected to cause false alarms, yet they may be sufficient to surpass predefined alert thresholds. Since for EEW, sensors should be placed at proximity to expected epicenters, the closer the fiber is to earthquake locations, the sooner the high signal-to-noise S-waves are detected and used. For large earthquakes, the lower sensitivity of DAS to P-waves is an advantage because it will limit the saturation of direct and scattered P-waves. Since DAS is an emerging technology, available datasets do not contain sufficient earthquakes whose damage potential is of interest for EEW.