dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this study, the algorithms and the implementation framework of Sen2Like (S2L), which is an open-source tool initially developed for the harmonization and fusion of S2 and LS8 data to produce Analysis Ready Data (ARD), are presented and discussed. Several issues such as geometric processing, AC, cloud mask, BRDF processing, etc. , in S2 and LS8/LS9 data are addressed in the paper. The S2L tool supports several processing steps dedicated to data harmonization and data fusion with respect to the S2 mission as a reference. The S2L algorithm is able to work and adapt in different domains, such as spatial, spectral and temporal. As the airborne and spaceborne remote sensing instruments have different characteristics, the adaptation of the processing steps per sensor is essential in order to provide a consistent and accurate multi-temporal data stack and to enable the generation of dense reflectance time series.
The validation of the ARD shows that the quality of input data is preserved and the inter-calibration between different sensors is improved. The validation activities provided the opportunity to characterize the dataset's consistency and accuracy and demonstrated that the quality information should be included as part of the delivered products. Improved quality information facilitates the use of multi-temporal data. By providing dense time series at fine spatial scale, the S2L tool aims to be helpful for the fine modeling of change and close monitoring with the integration of new missions. The study also concludes that AC should be mission-specific. Based on the preliminary analyses with the hyperspectral data obtained from the DESIS, it was proven that the S2L framework is able to process the products of any other MS or hyperspectral optical data at the L2A level.
g. , for the AI4EO Initiative). As an example, the deep learning models implemented for the LULC classification and employing multi-mission datasets can benefit from the increased data quality with high image radiometric coherence. A newly launched study on the Worldstrat dataset  [70]  reported that an increase in revisit times of Sentinel-2 images would be highly beneficial for this purpose. It can be envisioned that this requirement can be fulfilled with VCs to some extent.
Crystallographic algorithms and computer programs play significant roles in materials' characterization. A software package for the quantitative characterization of crystallographic texture, under axial symmetry conditions, is presented. The proposed methodology is intended for use with both electrons and high-energy synchrotron X-rays. Three different programs are introduced. Anaelu and Grazing are based on the Rietveld modelling approach. Dianne follows the Bunge's symmetrized spherical harmonics method. A SrTiO3 model sample is proposed as a hypothetical study case.
Then he (she) models, with the help of the computer, the direct pole figures and the (one-or two-dimensional) XRD pattern and performs a fitting process leading to a refined description of the investigated polycrystal. -The Bunge method  [2] : The user converts a 2DXRD pattern into a set of direct pole figures (PFs), represents the pole figures as Legendre polynomials' expansions, calculates the inverse pole figure spherical harmonics expansion coefficients and synthesizes the desired IPF. The work that has been done in relation to the Rietveld approach consists of the integration as a software package of the Anaelu  [3]  and Grazing  [4]  codes. The contribution to the Bunge methodology resides in the creation of the new Dianne program to invert direct pole figures by means of symmetrized spherical harmonics expansions.
The maximum value of the distribution, R 0 , is determined by a normalization condition. In fiber textures, the direct pole figures are obtained from the inverse one by applying the fundamental Equation of fiber textures, Eq. (  4 ), P h (φ) = 1 2π 2π 0 R(φ, ψ)dψ. ( 4 ) The presented mathematics have been systematized in the Grazing program. This program covers grazing incidence and Bragg-Brentano cases. Texture model is based on Eq. (  3 ). The modelled diffractograms are based on the theoretical intensities obtained with Eq. (  1 ). Input data consists of crystal structure, preferred orientation vector h 0 = [h 0 , k 0 , l 0 ] and IPF full width at half maximum (FWHM). Based on mentioned data, Grazing obtains intensities without textures and applies the correct modulations for each method.
For Bragg-Brentano method, the inverse pole figure, Eq. (  3 ), modulates the intensities of peaks. In grazing incidence, the intensities' modulation is produced by the direct pole figures, calculated by Eq. (  4 ). Grazing is a dual language software. It utilizes the Fortran CrysFML library as a primary source for powder difractogram obtention, and Pyside2 as his graphic user interface (GUI) framework. Figure  1  shows a representative screen shot from the Grazing GUI. Anaelu is currently developed on Python and Fortran. It utilizes the CrysFML library, similar to Grazing  [5]. The majority of Anaelu math is devised on Fortran. The current graphic interface is based on QT framework, particularly on the Pyside2  [6]  library. Its current graphic interface, which is compatible with Linux and Windows operative systems.
2D diffraction, program Anaelu Program Anaelu offers an approximation to a bidimensional Rietveld. It requires a model with an initial texture and leads, with an adjustment between calculated and experimental bidimensional diffractograms, into a complete characterization of the polycrystal considered. For the interpretation of bidimensional diffractograms, Anaelu utilizes the 2D pattern to show the intensity distribution along the so-called Debye rings. The modulation of intensities is governed by the PFs, as shown in Eq. (  2 ). The calculated PFs are calculated from the proposed IPF with the fundamental equation of fiber textures, Eq. (  2 ). Parameters' refinement is required for the quantitative 2D Rietveld.
Texture analysis, the Bunge approach. 2D diffraction, program Dianne F l (h i ) = π 0 P hi (φ) P1 (φ)sin(φ)dφ, (5) F l (h i ) = 2 2l + 1 l m=-l C m l Y m l (h i ), (6) R(φ, ψ) = Lmax l=0 l m=-1 C m l Y m l (φ, ψ). ( 7 ) The determination of the inverse figures becomes more accurate by increasing the degree L max of the spherical harmonics expansion. Same goes for the number of direct figures necessary to work as a function of L max. Symmetrized spherical harmonics  [7] , significantly reduce the number of required direct pole figures, by taking advantage of crystal symmetry. Dianne is based on Python. It calculates the Legendre polynomials and the spherical harmonics by using the Scipy library  [7].
Conclusions The presented software package allows two paths for the characterization of axial textures, starting from 1D or 2D diffraction experiments. The Grazing -Anaelu programs apply 1D and 2D Rietveld approaches. These consist on proposing an initial combination of crystal structure and texture, followed by a IPF → PFs → XRD calculation and refinement. Dianne systematizes the Bunge methodology: Experimentally measured pole figures are inverted via a symmetrized spherical harmonics algorithm. The application of the proposed software package to an illustrative case of study has been exposed. The consistency of the programs' system has been shown.
Light-cone data from the simulations have been generated using a parallel ray-tracing algorithm that has integrated more than 1 billion geodesic equations without the use of the flat-sky or Born approximation. Results. Catalogues and maps with relativistic weak lensing that include post-Born effects, magnification bias (MB), and redshiftspace distortions (RSDs) due to gravitational redshift, Doppler, transverse Doppler, and integrated Sachs-Wolfe-Rees-Sciama effects are publicly released. Using this dataset, we are able to reproduce the linear and quasi-linear predictions from the Class relativistic code for the ten power spectra and cross-spectra (3 × 2 points) of the matter-density fluctuation field and the gravitational convergence at z = 0. 7 and z = 1. 8. We find a 1-30% level contribution from both MB and RSDs to the matter power spectrum, while the fingers-of-God effect is visible at lower redshift in the non-linear regime.
(2016b)  developed a numerical GR N-body code in the weak-field limit without AMR. Adamek et al. (2019)  and  Lepori et al. (2020)  implemented a ray-tracing algorithm similar to that of  Reverdy (2014)  and  Breton et al. (2019) , which solves the geodesic equations without relying on the Born approximation; it was used to investigate several relativistic effects on the weak-lensing convergence power spectrum  (Lepori et al. 2020) , the matter power spectrum multipoles  (Guandalin et al. 2021) , and the cross-correlation power spectrum between the matter-density and lensing convergence  (Coates et al. 2021). Here, we describe and publicly release the numerical data generated from the RayGalGroupSims (which stands for Raytracing Galaxy Group Simulations, hereafter RayGal) simulation suite 1 , a set of simulations specifically designed to study the relativistic effects on the clustering of matter observables.
In particular, the light-cone data have been generated using a ray-tracing algorithm that solves the geodesic equations with the gravitational potential evaluated along the different AMR levels of the N-body solver  (Reverdy 2014; Breton et al. 2019; Breton & Fleury 2021 ). Thus, these numerical datasets benefit from a large dynamical range of simulations with a large volume and a high spatial resolution without the need to rely on the Born approximation or other limiting assumptions. In  Breton et al. (2019) , the light-cone data from the RayGal simulation of a Λcold dark matter model (hereafter ΛCDM) were used to perform a study of the asymmetry of the halo cross-correlation function due to the relativistic effects. In this work, we introduce the complete simulation suite, including data from the simulation of a non-standard dark energy model, and present a case study application of the dataset.
e. galaxy-galaxy lensing) by 20%. By coupling the signal between different redshift shells, relativistic effects boost the correlations between lensing-matter clustering observables. This is the case for the correlation between the matter-density field and the convergence evaluated at the same redshift as well as the correlation between the matter-density field and the convergence measured at a redshift smaller than that of the density field. The cosmological evolution shows that the relativistic contributions to the 3×2 points statistics make it promising probe of dark energy and modified gravity. The paper is organised as follows: We describe the RayGal simulations and the associated numerical datasets in Sect. 2;.
Model Ω m σ 8 w ΛCDM 0. 25733 0. 80101 -1. 0 wCDM 0. 27508 0. 85205 -1. 2 Notes. The columns from left to right give the values of the cosmic matter density, Ω m , the linear root-mean-square fluctuations smoothed on the 8 h -1 Mpc scale, σ 8 , and the redshift-independent equation of state, w. In all models the baryon density is set to Ω b = 0. 04356, the density of the relativistic species to Ω r 0. 00008, the curvature to Ω k = 0, the reduced Hubble constant to h = 0. 72, and the slope of the primordial spectrum to n S = 0. 963. we present a case study of the imprint of relativistic effects on weak-lensing observables using the RayGal data in Sect. 3; and finally we discuss the conclusions in Sect. 4.
It is also worth noticing that, in principle, light propagates on the null real light cone and not on the null FLRW light cone (i. e. at a given spatial location the gravitational field can be different for different light rays because of the variation in the potential during the time delay between light-ray arrivals). While in this article we neglect this effect in our ray tracing, very advanced studies could in principle account for this very subtle effect by interpolating between the two cells for each individual light ray. In RAMSES, each task from the MPI library writes a binary file that corresponds to a segment of the Peano-Hilbert space filling curve. Since these numerous raw binary files are not user 2 https://bitbucket. org/rteyssie/ramses/src/master/ friendly, they are rewritten as a small number of HDF5 files (with the tools provided by pFoF 3  ).
RAYGAL data: Snapshots, light cones, relativistic catalogues, and maps RayGal data are divided into four categories: snapshots, FLRW light cones, relativistic catalogues, and maps. Most of the RayGal data are available on the website of the COS team at LUTH 5. Snapshot data, corresponding to particles at a given time, consist of: (i) snapshots of all particles at a given time (position, velocity, and identity), (ii) all particles within halos (position, velocity, and identity), and (iii) the properties of all the halos (identity, number of particles, most-bounded particle posi-tion, centre of mass position, mean velocity, maximum radius, moment of inertia, circular velocity, velocity dispersion, angular momentum, kinetic energy, self-binding energy, density profile, and circular velocity profile).
The FLRW light-cone data (corresponding to particles and cells near the past FLRW light cone) consist of: (i) the particle light cone, all particles just before or just after the past null light cone (position, velocity, redshift) and (ii) the gravity light cone, all gravity cells just before and just after the past null light cone (position, potential, gravitational field, redshift). These snapshot and light-cone data are built from the simulations and post-processing as described in Sect. 2. 2. Relativistic catalogues, corresponding to sources as seen by the observer and accounting for relativistic effects such as weak lensing and RSDs, consist of: (i) the halo catalogue (un-lensed and lensed angular positions, background, and perturbed redshifts (from Eq. (  4 ) to Eq. (  9 )), weak-lensing distortion matrix, and halo mass), and (ii) the particle catalogue, a random subset of dark matter particles (same properties as for the halo catalogue, except mass).
Case study: Configuration, definition, angular cross-spectrum evaluation, and analytical predictions As already mentioned, in this work we present a case study application of the RayGal light-cone datasets to the analysis of relativistic effects on the angular cross-spectra. In particular, we correlate the key quantity of clustering (i. e. the matter overdensity, δ) and that of lensing (i. e. the gravitational convergence, κ) using Healpix maps. It is worth noting that these are not directly observable quantities (such as the galaxy overdensities or the galaxy ellipticities), nonetheless they are the central quantities in clustering and lensing studies from which one can deduce the correlation between observables. The goal here is not to focus on specific observables for a given survey, rather to provide general results about the imprint of relativistic effects on the density and lensing angular power spectra and cross-spectra.
The non-linear regime begins near ∼ 500, corresponding approximately to k ∼ 0. 2 h Mpc -1 at z ∼ 1, where P(k) is known to deviate from linear theory at the ∼10% level (see e. g. ,  Rasera et al. 2014 ). As we calibrated the P(k) used in the analytical predictions to that from the RayGal simulation, the agreement still holds in the non-linear regime between = 500 and = 5000. We halt at = 5000, which corresponds approximately to k ∼ 2 h Mpc -1 at z = 1, where finite resolution effects in our simulation (and possibly baryonic effects in the Universe) can no longer be neglected. Since the maximum we consider is of order of N side , we also expect the aliasing effect to be small. The green curve shows the cross-spectrum C δ 1 δ 2 ( , z 1 = 0. 7, z 2 = 1. 8), which is completely negligible in real space both in numerical data and analytical prediction.
Acknowledgements. We acknowledge financial support from the  DIM ACAV of the Région Île-de-France , the  Action fédératrice Cosmologie  et structuration de l'univers as well as the  JSPS  Grant  L16519. This work was granted access to HPC resources of TGCC/CINES through allocations made by  GENCI  ( Grand Équipement National de Calcul Intensif)  under the allocations  2016-042287 ,  2017-A0010402287 ,  2018-A0030402287 ,  2019-A0050402287  and  2020-A0070402287. A. T. acknowledges the support from  MEXT/JSPS KAKENHI  Grant Nos. JP17H06359 ,  JP20H05861 ,  JP21H01081 , and  JST AIP   Acceleration Research Grant  No. JP20317829 , Japan. We thank  E. Di Dio  for pointing out the "hidden" Class options to tune,  S. Prunet  for help on Mpgrafic and Polspice,  R. Teyssier  for help on Ramses,  B.
A transcriptional target gene signature of an FXR agonist, obeticholic acid  (23) , was also suppressed, suggesting that patients with high-risk PLS-NAFLD prediction may be candidates for obeticholic acid treatment. Despite no prognostic association with histological inflammation grade (Table  S2 ), PLS-NAFLD encoded diverse inflammatory pathways. This highlights limitation of gross histological evaluation of inflammation (semi-quantitative scoring of lymphocyte infiltration) and the importance of characterizing immune cell subsets and their functional status in predicting long-term HCC risk over up to 11 years. We next determined cellular sources of the high-risk PLS-NAFLD based on a meta-analysis of four human hepatic single-cell RNA sequencing (scRNA-seq) datasets (108,855 cells in total) representing all major cell types anticipated in healthy to NAFLD-affected cirrhotic livers (Table  S5 )  (24) (25) (26) (27) (28).
We identified 31 single-cell clusters (SC01 to SC31) based on similarity of transcriptome pattern (Fig. 2C , Fig. S3A , Table  S6 ). As expected, a uniform manifold approximation and projection (UMAP) plot showed clustering of the cells by cell type, not by dataset (Fig. 2C , Fig. S3B ). Among the liver cell clusters, we confirmed the presence of NAFLD-associated cell types such as CXCR6+PD-1+ CD8 T cells  (29)  and TLR4+ Kupffer cells (Fig. S3C )  (30) , some of which were associated with the high-risk pattern of PLS-NAFLD. The high-risk PLS-NAFLD score defined by average relative expressions of signature genes  (31)  was mainly associated with immune cell clusters, whereas the low-risk score was mostly associated with the endothelial cell clusters (Fig. 2D ). Within the mononuclear phagocyte (MP) cluster, high-and low-risk scores were enriched in distinct subclusters (for example, high-and low-risk genes in clusters SC29 and SC10, respectively) (Fig.
Our study showed that PLS-NAFLD can be modulated in a short time period (in several days to weeks) by potential medical interventions reducing HCC risk, suggesting that PLS-NAFLD might serve as a surrogate endpoint to estimate their long-term prognostic benefit in clinical trials. This concept is already incorporated in our ongoing clinical trials using the etiology-agnostic PLS/PLSec as companion biomarkers (NCT02273362, NCT04172779). Although PLS/PLSec-NAFLD showed promising prognostic capability, there are several limitations. First, the number of events was small in the HCC-naïve cohorts (the tissue validation set 1 and serum validation set) as typically observed in clinical studies, which may obscure accurate magnitude of risk difference between the high-and low-risk groups. Nevertheless, the absence of HCC incidence in low-risk patients supports the capability of PLS/PLSec-NAFLD to identify patients with negligible HCC risk.
Statistical analysis Categorical and continuous variables were tested by Fisher's exact test and Wilcoxon rank-sum test, respectively. Time-to-event analyses were performed using the Kaplan-Meier method and uni/multivariable Cox regression modeling. Proportional-hazards assumption was confirmed by using cox. zph function in survival R package (Table  S8 ). The matched case-control series was analyzed by conditional logistic regression model (Fig. 5D ). Estimated hazard ratios were adjusted by the Firth's correction using coxphf R package  (36)  when no event occurred in a patient group and therefore hazard ratios could not be reliably calculated. To evaluate the robustness of our signature's association with prognosis, we built multiple multivariable models with clinically known confounding variables (Table  S3 ).
Data and materials availability: All data associated with this study are in the paper and supplementary materials. All data are publicly available at the NCBI GEO (accession number, GSE193084). Raw data from figures is in data file S1. The R codes for Gene Set Enrichment Index based on single sample-based signature enrichment analysis (eseach algorithm)  (11)  and TexSEC are available at 10. 5281/zenodo. 6519385. Of note, data with patient names and other identifiers cannot be shared.
Representation learning powered by deep neural networks has opened up extraordinary opportunities to develop all-purpose models transferring knowledge across systems and tasks. After a major breakthrough in natural language processing  [14, 71] , the concept has been transferred to proteins through protein language models (pLMs)  [58, 17, 4, 26]. pLMs learn the fundamental properties and mechanisms of natural protein diversity by reconstructing some masked or the next amino acid(s), given their sequence context, at scale. They exhibit exciting potential for a broad range of protein-related problems  [40, 62, 43, 44, 56, 78]. Beyond sequence information, self-supervised learning-based approaches have leveraged the protein and protein complex 3D structures available in the Protein Data Bank (PDB)  [5]  for fixed-backbone protein design  [2, 28, 11] , for predicting protein stability  [6, 77] , and for assessing the impact of mutations on protein-protein interactions  [42].
DLA applies 3D convolutions to locally oriented residue-centred cubes encapsulating atomic-resolution geometrical and physico-chemical information  [52]  (Fig. 1A ). In this work, we expanded this framework by combining self-supervised representation learning of 3D local interfacial environments (Fig. 1B ) with supervised learning of ∆∆G bind exploiting both structural and evolutionary information (Fig. 1C ). DLA-Mutation only takes as input two cubes, corresponding to the environments around the wild-type and mutated residues, respectively, and directly estimates ∆∆G bind. Beyond prediction, we used the learned representations to investigate the extent to which the environment of an interfacial residue is specific to its type and physico-chemical properties (Fig. 1D ). DLA-mutation code and models are freely available to the community at http://gitlab.
Databases We computed the ground-truth ∆∆G bind values from SKEMPI v2. 0  [31] , the most complete source for experimentally measured binding affinities of wild-type and mutated protein complexes. We restricted our experiments to the data produced by the most reliable experimental techniques, namely Isothermal Titration Calorimetry (ITC), Surface Plasmon Resonance (SPR), Spectroscopy (SP), Fluorescence (FL), and Stopped-Flow Fluorimetry (SF), as done in  [70]. We selected a subset of 2 003 mutations associated with 142 complexes, referred to as S2003 in the following. To provide ssDLA and DLA-Mutation with input protein-protein complex 3D structures, we created and processed two databases, namely PDBInter and S2003-3D. PDBInter is a non-redundant set of 5 055 experimental structures curated from the PDB.