dataesr/scientific-paragraphs-categorization huggingface dataset:
To eliminate anisotropy in the stress tensors from the NVT calculation, the isothermal-isobaric ensemble (NPT calculation) was employed on the quenched glass structures for an additional 20 ps. In agreement with experimental observations, structural analysis of the atomic configurations show 4-fold C-O coordination starting to appear at pressures higher than 58 GPa. The amount of 3-and 4coordinated C-O are almost equal at 106 GPa, which correspond to an experimental pressure of ~80 GPa (Fig. 1a  and Supplementary Fig. 1 ). At 145 GPa, the highest pressure considered here, 4-coordinated C-O becomes dominant. Snapshots extracted from the NPT MD trajectories were used in the computation of the XRS spectra (Fig. 1a ). BSE spectral calculations. All spectrum simulations were performed based on structural snapshots from the ab initio molecular dynamics simulation trajectories described in the previous sub-section using the Bethe-Salpeter Equation (BSE) method as implemented in the OCEAN code (Obtaining Core level Excitations using Ab initio methods and the NIST BSE solver)  54, 55.
Acknowledgements We acknowledge the  ESRF  for providing synchrotron radiation and technical support at ID20, ID15a, and ID15b beamlines. We acknowledge the helpful support of the  Sample Environment Service-HP lab. C. Henriquet  is gratefully acknowledged for providing technical support at beamline ID20. We are thankful to  C. Cavallari  and  M. Apollo  for support during the first XRS experiment. We acknowledge  R. Rüffer  and  A. Chumakov  for providing the diamonds used in the experiments. MiW thanks the support by the  National Natural Science Foundation of China  ( 51701180 ,  U1802254 ,  51871201 ). Part of the work was performed with the support of Project  MIUR -Dipartimenti di Eccellenza TECLA, Department of Earth and Environmental Sciences, University of Milano-Bicocca.
Data availability Experimental and simulation-derived datasets are publicly available in EarthChem repository (https://www. earthchem. org/). The unique identifiers (DOIs) are assigned to datasets collected at different beamlines as follow: i) ID15a 10. 26022/IEDA/112756, ii) ID15b 10. 26022/IEDA/112757 and 10. 26022/IEDA/112758, iii) ID20 10. 26022/IEDA/ 112759 and 10. 26022/IEDA/112760, iv) simulations (RDF) 10. 26022/IEDA/112761.
Theoretical modeling The theoretical properties of iron-bearing clusters in dravitic tourmaline have been investigated using density functional theory (DFT) with the generalized gradient approximation (GGA) to the exchange-correlation functional as proposed by Perdew, Burke and Ernzerhof (PBE)  (Perdew et al. , 1996). The modeling was done as in  Fuchs et al. (2022)  using a plane-wave and pseudo-potential scheme, as implemented in the PWscf and PHonon codes from the Quantum ESPRESSO package  (Giannozzi et al. , 2009 ; http://www. quantum-espresso. org, last access: 12 February 2023). The ionic cores were described by optimized norm-conserving pseudo-potentials from the SG15 ONCV library  (Hamann, 2013; Schlipf and Gigy, 2015). The wave functions and the charge density were expanded in plane waves with 80 and 480 Ry cutoffs, respectively.
Acknowledgements. Calculations have been performed using the high-performance computing (HPC) resources of the  SACADO MeSU Platform at Sorbonne Université  and the HPC resources of IDRIS under the allocations  910820  and  907320  attributed by  GENCI  ( Grand Équipement National de Calcul Intensif). Review statement. This paper was edited by  Giovanni B. Andreozzi  and reviewed by three anonymous referees.
Code availability. PWscf and PHonon codes (Giannozzi et al. ,  2009)  are available at http://www. quantum-espresso. org/. The pseudo-potentials (Schlipf and Gygi, 2015)  are available at http://www. quantum-simulation. org/potentials/sg15_oncv/. Structural drawings in Figs. 1 and 5 have been made using CrystalMaker ® (https://crystalmaker. com/) and VESTA (https:// jp-minerals. org/vesta/en/; Momma and Izumi, 2011) software, respectively. Spectral fitting has been performed using the Fityk program (https://fityk. nieto. pl/, last access: 22 December 2022, Wojdyr, 2010). Data availability. Relaxed theoretical structures of the models are provided in the Supplement. Specific requests should be addressed to Etienne Balan (etienne. balan@sorbonne-universite. fr). Supplement. The supplement related to this article is available online at: https://doi.
Protein analysis, detectability, and quality control We chose to analyse the plasma samples using an affinity proteomics approach. While targeted methods, such as the Olink PEA approach, are inherently biased towards the subset of proteins that are measured, we attempted to maximise the possibility for discovery by measuring as many proteins as possible. Hence, we used the recently launched version of Olink's Explore I and II panels, which includes 2949 proteins (Supplementary Data 1). Out of this set, 2213 (75%) could be detected in >50% of the samples when judging their normalised protein expression levels (NPX) above the limit of detection (LOD) (Supplementary Fig. 1 , Supplementary Data 1). The ranges per protein varied between 0. 17 NPX and 9. 27 NPX (Supplementary Fig. 2 ). For data analyses, proteins >25% detectability were included.
1  | Flowchart of study design, data analyses and main results. A total of 2929 unique proteins (2949 assays) were measured using the Olink PEA Explore assay in plasma samples taken from 598 women. Protein levels were correlated with baseline clinical characteristics using linear regression. Genetic association analysis of protein levels was performed which led to the identification of 812 cis-pQTL for 737 proteins, which were used in Mendelian randomisation analysis of breast cancer in the Breast Cancer Association Consortium (BCAC) case-control meta-analysis of breast cancer risk, followed by replication in independent studies of breast cancer risk. To follow up on significant proteins, the genetic signals for protein levels and breast cancer risk were visualised and evaluated using Mirror plots and were also tested for causal relationships with established and emerging breast cancer risk factors, also using Mendelian randomisation.
Association between plasma protein levels and clinical characteristics To examine observational relationships between protein levels and clinical characteristics of the KARMA women, we regressed each measured protein against seven factors (age, alcohol consumption, number of births, body mass index (BMI), hormone replacement therapy (HRT), peri-and post-menopause and current smoking). All associations are shown in Supplementary Data 3. A total of 684 proteins were associated with BMI and 459 proteins were associated with age (Fig. 2 ). Several of the observed associations have previously been described such as higher plasma levels of leptin and fatty-acid binding protein 4 (FABP4) with increasing BMI  24  , higher Follicle stimulating hormone (FSHB) in post-menopausal women and higher placental alkaline phosphatase (PLAP) levels in smokers  25.
Identification of cis-pQTL To identify genetic instruments for the downstream causality testing using MR, gene variants within a range of 1 Mbp up and downstream of genes encoding each of the 2929 unique proteins were tested for association with levels of the corresponding protein. Significant associations (Bonferroni corrected for the number of independent variants tested at p < 2. 77 × 10 -4 , Supplementary Fig. 5 ) were observed for a total of 812 independent variants (R 2 < 0. 1) across 737 proteins, henceforth referred to as cis-pQTL (Supplementary Data 4). Most of the pQTL were observed for proteins on Olink Explore I panel (n = 523) but several pQTL were also observed for Explore II proteins (n = 289). Some of the cis-pQTL showed effect sizes well above 1 standard deviation, including the nucleotidase NT5C (missense, Pro68Leu, MAF 3%), acylphosphatase (ACYP1) (~7 kbp upstream of gene, MAF 1.
LRRC37A2 showed MR evidence for later age at menarche and earlier age at menopause in two independent outcome datasets, and also for higher IGF-1 levels (Supplementary Data 2). CD160 showed nominal MR evidence for an aetiological role lower age at menarche. To summarise, the MR analysis showed that genetic elevation of CD160, DNPH1, LAYN, LRRC37A2 and TLR1 associated with breast cancer risk, and with similar effects on ER+ and ER-cancers.
Systematic search for drugs targeting CD160, DNPH1, LAYN, LRRC37A2 and TLR1 To investigate if any of the five proteins identified in the present investigation had been previously explored as drug targets, we Mendelian randomisation analysis on breast cancer risk in the BCAC study was performed by modelling exposure to genetically higher plasma levels of 730 proteins with at least one cis-pQTL. The Y-axis shows the -log10 p-value of the Wald-score or IVW and the X-axis shows the beta-estimates of the MR result for each protein that was tested. performed a systematic search across several databases, including NIH Pharos Consortium, IUPHAR/BPS Guide to Pharmacology, DrugBank and ClinicalTrials. gov. With the exception of LAYN, targeted by Hyaluronic acid, none of the proteins were registered as known drug targets  29.
Zhu et al. demonstrated an absence of association with breast cancer for genetically elevated levels of C-reactive protein  45  and Shu et al. reported a wider MR analysis, instrumenting 1469 proteins using Somascan-based pQTL in the INTERVAL cohort, of which genetic instruments for 26 proteins were found to be associated  45, 46. Bouras et al. instrumented 47 inflammatory cytokines and reported that genetically increased levels of CXCL1 and decreased levels of MIF associated with breast cancer  47. Of the 28 proteins previously reported in breast cancer MR studies, our study included post-QC data on 22 proteins and a cis-pQTL was identified in our study for five of them (RELT, ENG, TFPI, ISLR2, SCG3). We replicated cis-pQTL reported for RELT, ENG and SCG3 in ref. 26, which were based on the Somascan protein assay, but were unable to replicate pQTL reported for TFPI, ISLR2 (Supplementary Data 4).
In the Olink Explore protocol, the target sequence is amplified in a double PCR reaction and purified before the NGS. The sequence data is processed and normalised to produce Olinks relative quantification unit Normalised Protein eXpression (NPX). The produced DNA signal functionally works as a proxy for the protein levels present in the sample. Further details on the Olink Explore protocol and internal quality control are available in the Supplementary Methods 1 document.
Protein QTL mapping Genome-wide genotyping in the KARMA study was performed using the Illumina iSelect or Oncoarray arrays, followed by imputation using the Wellcome Trust Sanger Institute imputation service using the 1000 genomes phase 3 as reference. Standard quality control was applied as previously described. Variants with a minor allele frequency <0. 01 were filtered out prior to analysis. The final dataset included 9087 million variants. Proteins >75% of NPX values below LOD were filtered out before the pQTL analysis, yielding a total of 2476 proteins in the analysis. Values below LOD were included. The pQTL discovery analysis was performed using an additive model with adjustments for age, BMI and 10 genetic PCs in PLINK 2. 0. To preserve statistical power for pQTL identification, only variants within a 1 mega-base pair window of the protein-coding gene were tested for association with respective circulating protein levels.
Mendelian randomisation analysis We performed Two-sample MR using the R package TwoSampleMR  48, 49  (https://mrcieu. github. io/TwoSampleMR/) to test for proteins with a potential causal role in breast cancer. Independent cis-pQTL (r 2 < 0. 001) were used as instrumental variables (IV), and GWAS of breast cancer risk from the BCAC consortium were used as outcome, which included data from 122,977 breast cancer cases and 105,974 controls. In the case of a single independent IV Wald Ratio was applied, otherwise, inverse-variance weighted estimates were reported. The threshold for statistical significance was defined as (7. 5 × 10 -5 ) to account for multiple testing. The replication analysis was performed in a meta-analysis of FinnGen R9 and the UK-biobank, which included 25,807 cases and 355,307 controls.
Acknowledgements We thank all the participants in the Karma study and the study personnel for their devoted work during data collection. We also want to acknowledge the participants and investigators of the FinnGen study. The data handling and analysis were enabled by resources provided by the  Swedish National Infrastructure for Computing (SNIC) , partially funded by the  Swedish Research Council  through grant agreement no. 2018-05973. We also acknowledge funding from the  Swedish Research Council  (Grant  2022-00584 ), the  Swedish Cancer Society  (Grants  22 2207 ,  19 0267  and  20 0990 ) (P. H. , K. C. ), the  Stockholm County Council  (Grant  20200102 ) and the  Karolinska Institutet's Research Foundation  (Grant  2018-02146 ) (P. H. , K. C. ) as well as from the  Stockholm County Council  ( FoUI-954555 ,  FoUI-978540 ) (P.
Data availability The cis-pQTL GWAS summary-level data generated in this study have been deposited in the Zenodo data repository under accession code https://doi. org/10. 5281/zenodo. 8387905, with the URL https://zenodo. org/record/8387905. The individual-level phenotypes, genotypes, and biospecimen from the KARMA study are available under restricted access, as personal data are protected by the European Union General Data Protection Regulation legislation and the Swedish Ethical Review Authority, but can be requested according to the process described at URL https://karmastudy. org/contact/data-access/. For previously published GWAS studies on breast cancer risk and risk factors, the summary-level data are available at https://bcac. ccge. medschl. cam. ac. uk/bcacdata/ or at the MRC IEU OpenGWAS database [https://gwas.
Code availability We used publicly available software for most of the analyses in the manuscript (as described in the "Methods" section). Access to other scripts and pipelines is provided through GitHub. Protein QTL mapping was performed in PLINK2. 0 (https://www. cog-genomics. org/ plink/2. 0/). Statistical analyses and figures, listed below, were conducted in R version 4. 1. 0. Mendelian randomisation analyses were conducted using the R package TwoSampleMR version 0. 5. 6 (https:// mrcieu. github. io/TwoSampleMR/), regional plots were constructed using R package RACER version 1. 0. 0 (https://github. com/oliviasabik/ RACER). Figures were constructed using standard R or ggplot2 version 3. 4. 0 (https://ggplot2. tidyverse. org/index. html). The scripts and pipelines for quality control, association analyses and Mendelian randomisation are available at https://github.
Over the past five years, there has been a rapidly growing scientific interest in the development of remote sensing methods to map historical shoreline positions from satellite imagery. To illustrate this rapidly growing interest, Fig. 1a  displays the number of publications and citations per year that include both "shoreline" and "satellite" as keywords (database: Web of Science). Since 2018, there has been a steep increase in the number of publications on satellite-derived shorelines as the field has started to leverage satellite imagery to analyze coastal systems at unprecedented regional to global scales  [9] [10] [11] [12] [13]. As for other Earth Science disciplines, the use of satellite remote sensing was facilitated by the advent of Google Earth Engine (GEE)  14  in 2017, a free cloudbased geospatial analysis platform.
" This was retrieved from the Web of Science database with the following query: TI = ("satellite*" AND "shoreline*") OR AB = ("satellite*" AND "shoreline*") OR AK = ("satellite*" AND "shoreline*"), where TI stands for Title, AB for Abstracts and AK for Author's Keywords. b Present methods to automatically map shorelines on optical imagery, divided into 'at pixel resolution' and 'sub-pixel resolution. ' This figure was adapted from  16. The references in bold are evaluated in this study. satellite-derived shoreline observations across different methods and coastal environments. A variety of satellite-based shoreline detection methods are presently available. To extract shoreline observations from satellite imagery, many established SDS algorithms employ different image processing methods, including contouring of a land/water threshold  12, [17] [18] [19]  ; maximum-gradient contouring methods  [20] [21] [22]  ; and soft classification techniques  [23] [24] [25].
In this study, a benchmarking framework was developed to test the accuracy of time-series of satellite-derived shoreline observations obtained from publicly available Landsat and Sentinel-2 imagery against in situ surveys. Four diverse, well-monitored sandy beaches, namely Narrabeen (Australia)  4  , Duck (USA)  32  , Torrey Pines (USA)  7  , and Truc Vert (France)  6  were selected to evaluate 5 different established SDS algorithms, namely CoastSat 17 , SHOREX  33  , ShorelineMonitor 9 , CASSIE  19  , and HighTide-SDS  27. The current paper and its accompanying software focus on the accuracy assessment of SDS algorithms against a set of benchmark datasets and provides an open-source, publicly available, and fully reproducible methodology to test stateof-the-art and future developments in SDS workflows.
Data and methods In this study, four benchmark sites are used to assess the ability of five different SDS algorithms to accurately monitor sandy beaches. The methodology developed to assess the accuracy of SDS observations is presented in the flowchart in Fig. 2. The four sandy beaches of this benchmarking study were selected based on the availability of long-term in situ coastal monitoring datasets that were publicly available. The beach characteristics and location of each site are presented in Table  1  and Fig. 3. Duck, North Carolina, United States, is a microtidal beach, mean spring tidal range (MSTR) of 1. 4 m, located on a barrier island and has been monitored on a monthly to fortnightly basis since 1974  32. Narrabeen, New South Wales, Australia, is a microtidal beach (MSTR of 1. 7 m) located on the east coast of Australia where beach surveys have been conducted monthly to fortnightly since 1976  4, 34.
Consequently, the ShorelineMonitor time-series have the most data as they consistently map one shoreline per month (see number of samples in Table  3 ). In summary, there is a variety in performance of individual algorithms at individual sites, but no one algorithm is more accurate than all others in every situation. Further, there appears to be a greater variability between sites than between algorithms (Fig. 5 ). At Duck, all algorithms (excluding HighTide-SDS) achieved an RMSE below 10 m, and SHOREX was the algorithm that best captured the variability in the shoreline position with a standard deviation error (STD) of 6. 9 m, followed by ShorelineMonitor (STD 7. 9 m), CoastSat (STD 8. 2 m), and CASSIE (STD 8. 9 m). The coefficient of determination (R 2 ), depicted in Fig. 5b , is around 0.
7, 6. 9, and 9. 4 m for CoastSat, SHOREX, and CASSIE, respectively, compared to 8. 2, 6. 9, and 9. 0 m for Landsat, which is perhaps unexpected given the higher resolution of Sentinel-2 imagery. The biases in the time-series are similar for both satellites, apart from SHOREX, where a larger landward bias is observed in the Sentinel-2 data (-12. 0 m versus -4. 8 m on Landsat). At Narrabeen, the accuracy of the Sentinel-2 time-series increases considerably only for SHOREX, from an STD error of 9. 8 m (Landsat) to just 5. 8 m (Sentinel-2), while it remains the same for CoastSat and CASSIE (8. 0 and 9. 6 m, respectively). In terms of biases, both CoastSat and SHOREX display seaward shifts, of 4. 7 and 8 m, in shoreline position between Landsat and Sentinel-2 time-series, respectively. While we cannot isolate the source of this seaward bias between Landsat and Sentinel-2, it could be a result of the higher resolution of Sentinel-2 images, allowing the shallow water region adjacent to the shoreline to exhibit stronger reflectance in the near-infrared band, which consequently pushes the detected waterline farther seaward.
At Torrey Pines (limited   3. Note that the y-axis limits are increased for Truc Vert to accommodate its larger errors. b Coefficient of determination (R 2 ) for each algorithm at each site. The surveyed MSL contour was used to evaluate the SDS time-series. Sentinel-2 data available) and Truc Vert, the overall accuracy and precision does not improve with the increased resolution provided by Sentinel-2. Wave setup correction. One of the sources of error in satellitederived shorelines is the effect of oscillating water levels on the position of the waterline. Time-series of shoreline position (typically based on the instantaneous waterline position) derived from imagery are affected by tide as well as wave setup and wave runup (i. e. , the horizontal excursion of swash). Runup is an oscillatory motion of the waterline driven by the landward propagation of breaking waves and it generally cannot be corrected for on individual satellite images as the phase of specific waves is not known at the instant the image was taken.
However, wave setup, the persistent elevation of nearshore water levels in the presence of breaking waves, can be corrected for using a method analogous to tide correction (i. e. , converting a vertical offset into a horizontal one by assuming a beach slope, see Eqs. 1-2 in the Methods). Here we include wave setup correction to investigate if it improves the accuracy of the SHOREX, CASSIE, and CoastSat instantaneous shorelines. The SDS time-series at each site were corrected using the empirical parameterization of wave setup by Stockdon et al. 41. Hindcasted wave data (needed to calculate wave setup in Eq. 3) were obtained from the closest offshore ERA-5 grid point. Figure  7b  compares the error distributions for the SDS corrected for tide-only and tide-and-wave-setup. The accuracy metrics are reported in Table  5.
Systematic and random errors in the SDS time-series can come from four main sources: georeferencing of the satellite images, image resolution, waterline-detection method, water-level correction. The georeferencing accuracy of each Landsat image is calculated by the data provider  49  , using a database of ground-control points and the RMSE is provided in the image metadata. Hence, it is good practice to mitigate the effect of georeferencing errors by discarding the images with a RMSE larger than 10 m. This issue is more problematic for Sentinel-2 images as only a 'pass/fail' geometric quality flag is present in the image metadata, with 'fail' indicating that the RMSE is larger than 20 m 50. Based on this information, it is generally not possible to exclude images with georeferencing errors of less than, but close to, 20 meters, which we consider to be a relatively high threshold when tracking shoreline changes.
a The SDS time-series for the 3 algorithms that use individual satellite images were used to compare the accuracy of the shorelines derived on Landsat and Sentinel-2 images. The full and hatched boxplots show the horizontal errors associated with the Landsat and Sentinel-2 time-series, respectively, for the 3 instantaneous shoreline algorithms (CoastSat, SHOREX, CASSIE). At Torrey Pines, as the ground truth data only spans to the end of 2016, only 1 year of Sentinel-2 data could be evaluated. The value of the median is indicated, and the whiskers are set at 1. 5xIQR. Note that the y-axis was stretched for Truc Vert to accommodate for the larger errors. b A wave setup correction, based on Stockdon et al. 41  , was applied to the Landsat tidally corrected SDS time-series. The full and hatched boxplots show the horizontal errors associated with the tide-only and tide + wave setup time-series, respectively, for the 3 instantaneous shoreline algorithms (CoastSat, SHOREX, CASSIE).