dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Acquisition and Processing XRF-XRD combined measurements were performed by the SOLSA combined instruments (Figures  1  and 2 ) allowing for the simultaneous measurement of XRF and XRD signals. X-ray fluorescence spectra were acquired using a Mo micro-source radiation and an Amptek X-123 (AMPTEK INC, Bedford, MA, USA) SDD (Silicon Drift Detector) detector placed over the sample to ensure the measurement of lighter elements down to Z = 12 (Mg). Data were collected for a quantitative XRF model from 0. 1 to 15 keV. X-ray powder diffraction data were acquired in asymmetric mode using Co Kα average radiation (Kα 1 = 1. 78900 Å and K α2 = 1. 79289 Å) and equipped with an INEL curved position sensitive detector which spans the entire 5 ≤ 2θ ≤ 120 • range simultaneously. For this diffractometer, the instrumental function was also defined using yttrium oxide (Y 2 O 3 ) standard powder following the analytical procedure described in  [7].
This instrument's configuration is accompanied by the use of a modular sample holder (Figure  1 ) capable of accommodating samples of different shapes, thus allowing the combined XRF-XRD analysis to be carried out on both prepared samples (e. g. , a fine powder) and on samples with little or no preparation (e. g. , coarse granulometric fractions, a cutting, a rock), as long as the surface to be analysed is flat. The combined XRF-XRD processing was performed using the LUXREM software v0 developed during the SOLSA project. The combined analysis method presented in Figure  2  is based on two powerful new approaches using state-of-the-art methodology. The first, Full Pattern Search Match (FPSM), searches for phases and chemical elements  [8]  using the Crystallography Open Database (COD) for mineral identification  [9].
SEM-EDS-Based Automated Mineralogy SEM-EDS-based automated mineralogy analyses were performed on polished sections using the QEMSCAN ® system at Eramet Ideas. Acquisitions were made on an FEI Quanta 650F SEM platform with two Bruker Xflash 30 mm 2 silicon drift energy dispersive spectrometer X-ray detectors. The iMeasure v. 5. 2 software was used for data acquisition and iDiscover v. 5. 2 software was used for spectral interpretation and data processing. The PMA (Particle Mineral Analysis) measurement mode was used to collect X-ray data every 2. 5 µm across the surfaces of the polished sections, with a total of 2000 counts per spectrum.
Results The intrinsic strength of the coupled XRD/XRF methodology is the mutual re-enforcement during data processing, where the chemistry helps refine mineral proportions and vice versa. A comparative approach using well established techniques for both elemental and mineral quantification allows for the comparison of the performance and quality of the data and signal processing obtained with the SOLSA system and the LUXREM data reduction software. It must be noted that there is no per se mineral quantification standard. The standard method for the QA/QC of the mineral proportion with Qemscan ® is performed with respect to the fused-bead XRF geochemical analysis and the recalculated assay generated from the mineralogical data base. 3. 1. Chemical Quantification: SOLSA vs. Fused-Bead WD-XRF Figure  3A , presents the distribution of the same samples analysed using conventional fused-bead XRF as well as SOLSA XRF.
Mineral Quantification, SOLSA Coupled XRD-XRF vs. Qemscan ® Mineral proportions calculated with the SOLSA equipment are presenting a good agreement with mineral proportion calculation from Qemscan ® with a distribution of th datapoints near the 1:1 line representing the ideal reconciliation (Figure  3B ). Discrepancie between the two methods were anticipated due to the use of two different databases, on using the chemical composition of the pixels, while the other using the crystallographi and fundamental properties of physics to perform advanced Rietveld processing. The dif ferences between TiO2 polymorphs quantification could provide an understanding of how minerals would be reported in the different mineral separation circuits of the mine pro cessing plant. In addition, mineralogical investigation at the crystal scale with SOLSA might provide a better methodology to understand ilmenite alteration and the subsequen consequences it might have on its separability using high intensity magnetic fields.
Both methods provide a similar mineralogical assemblage of the samples providing good con fidence that the technique could soon be integrated with other mineralogical and geo chemical datasets in use at the mine. 3. 2. Mineral Quantification, SOLSA Coupled XRD-XRF vs. Qemscan ® Mineral proportions calculated with the SOLSA equipment are presenting a good agreement with mineral proportion calculation from Qemscan ® with a distribution of the datapoints near the 1:1 line representing the ideal reconciliation (Figure  3B ). Discrepancies between the two methods were anticipated due to the use of two different databases, one using the chemical composition of the pixels, while the other using the crystallographic and fundamental properties of physics to perform advanced Rietveld processing. The differences between TiO 2 polymorphs quantification could provide an understanding of how minerals would be reported in the different mineral separation circuits of the mine processing plant.
Concluding Remarks These are the first preliminary results obtained with coupled XRD-XRF on a reallife practical case of mineral processing optimization at the GCO mine in Senegal. The combined instrument demonstrates its potential to acquire mineralogical and chemical data simultaneously from the same sample. Results could be further improved by database adjustments to better fit the signals or sample preparation procedures, while the counting time of the acquisitions could also be increased to improve statistics. The acquisition of signals from various types of powder materials, either mill or as-is with different particle size distributions, allowed for the testing of these effects on both the acquisition and processing of the signals coming from the combined instruments. It also contributed to the creation of a site-specific mineralogical database for heavy mineral sands.
Climatic Data Estimating top-of-atmosphere radiative forcing resulting from changes in surface albedo requires meteorological data of seasonal incoming shortwave radiation (SW in ) at the surface and of atmospheric transmittance (T A ). We also considered the effect of precipitation on CC emergence with a consequence on RFα. The ERA-5 products (  [34] , available at a global scale (https://cds. climate. copernicus. eu, last access 1 April 2021), at an hourly time step and at a spatial resolution of 1/4 • (ERA5, i. e. , solar radiation) or 1/10 • (ERA5-Land, i. e. , precipitation) were used. The T A was estimated for each grid point on a daily basis as the ratio between SW in and the incoming solar radiation at TOA (R TOA ). Finally, in order to assess the CC albedo effect when snowfall occurs, the snow depth from the ERA5-Land product was also used.
ECOCLIMAP Classification To define the areas and periods of potential CC introduction, the ECOCLIMAP classification  [35, 36]  was used. This database provides the fraction of crop types according to their photosynthesis (i. e. , C3 or C4) as well as the fraction of bare soil all over Europe and at a spatial resolution of 1 km. The proposed method aims at simulating CC introduction between two cash crops (either summer or winter crops). However, crops are usually distinguished according to their growth period (i. e. , winter or summer crops) rather than according to their photosynthesis types. As in Carrer et al. [26] , a correction factor was thus defined for each country from the fraction of C3 plants in ECOCLIMAP and the fraction of winter crops of the 2011 Eurostat database (https: //ec.
[20] , who developed a framework integrating DayCent and the largest soil survey dataset available in Europe: LUCAS  [40]. Soil property information from the LUCAS dataset and management information from official statistics were used as input to run the DayCent model, which allowed estimation of the amount of CC biomass that could be introduced into the soil and subsequent SOC accumulation. Moreover, using the dataset available in Post et al. [33] , we defined an equation linking SOC (%) and soil albedo: α soil = -0. 052 × ln(SOC) + 0. 17 (1) This equation was then used to estimate the decrease in bare soil albedo following the increase of SOC estimated by DayCent model. It thus leads to an estimation of the radiative impact of soil albedo decrease following CC incorporation in the soil. A change in RF associated with soil darkening is thus available for 85 years of simulation and more than 7000 LUCAS points and was then used to modify the simulated RFα.
[26] , which minimizes the radiative mitigation potential. If CC are used to increase cropland albedo, it may be more appropriate to select species with high albedo, as discussed in Sakowska et al. [41] , where the authors considered a chlorophyll mutant soybean with an albedo of 0. 29. Once the change in surface albedo is estimated, the daily albedo-induced radiative forcing is calculated as: RFα = -SW in × T A × ∆α (5) where SW in is the daily shortwave incoming global radiation and T A is the daily atmospheric transmittance. Since T A is not directly provided in the ERA-5 database, we estimated it as: T A = SW in SW TOA (6).
In order to reduce the sensitivity of our estimates to potential inter-annual climatic effects, the present study is based on 10 years of data  (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010). Moreover, the model was modified to increase the simulation period up to 100 years. As no climate change scenario was applied, we replicated the 2001-2010 dataset ten times. As suggested by Brisson et al. [45] , a threshold limiting the emergence of CC below 30 mm of rainfall the month following their introduction was considered. Scenario S CC_extension -The S Carrer_et_al scenario was modified in order to introduce the CC during all the fallow periods when and where possible (not only in the R SW and R WS ). Therefore, CC were also introduced in R WW and R SS when the fallow period lasted for at least 2.
2017). The gamma-ray constraints are considerably relaxed when short activity times of blazars are considered  (Dermer et al. 2011). 40 However, even if blazars have only been active for the past 10 yr, a joint analysis of Fermi-LAT and archival IACT spectra from several sources is still able to exclude B  3 × 10 -16 G for a coherence length of ℓ B  10 kpc  (Ackermann et al. 2018). A comprehensive overview of current constraints is provided by Alves  Batista & Saveliev (2021). Here we perform an updated search for the cascade emission of hard-spectrum blazars. In contrast to previous cascade searches, we use a combination of Fermi-LAT data and observations carried out with the High Energy Stereoscopic System (H. E. S. S. ) based on the full Poisson likelihood for gamma-ray event statistics and calculate pair halo templates with state-of-the-art Monte Carlo simulations.
Cascade Predictions We generate templates for the pair halo for each redshift using the CRPropa Monte Carlo simulation package version 3. 1. 6 43 (Alves Batista et al. 2016a). In the simulation, we include all relevant particle interactions and consider cascade photons arriving with a time delay shorter than a maximum blazar activity time t max. Using a spectral reweighting  (Ackermann et al. 2018 ) and parallel transport (Alves Batista et al. 2016b), the simulations are converted into energydependent sky maps for arbitrary input spectra and angles θ obs between the jet axis and the LOS (see Appendix C for further details on the simulation). 44 These serve as input templates for our data analysis. For simplicity, θ obs = 0°is assumed throughout. For 0 < θ obs < θ jet /2, the templates will be asymmetric.
Discussion The analysis presented here improves on previous analyses of a combination of IACT and LAT data in two ways. First, we use the CRPropa code to generate predictions for the spectral and spatial shape of the halo. As the code traces the full trajectories of the particles, we are not limited by the assumption of, e. g. , small deflections of the pairs in the IGMF, which limited the validity range of the results of  Ackermann et al. (2018). Second, we combine LAT and H. E. S. S. data on the likelihood level and self-consistently include the halo contribution. As a result, we are able to improve on the constraints found by  Ackermann et al. (2018)  by a factor of two for blazar activity times as short as 10 yr. To our knowledge, these are the tightest constraints on the IGMF from gamma-ray observations to date (compare Figure  4 ).
A. 2. Extracting Likelihood Values in the Presence of the Halo In the presence of a halo, it is nontrivial to extract the likelihood values, q B, i i LAT, ( | )   , using the built-in functionality of fermipy. The reason is that the cascade flux depends on the intrinsic spectral parameters of the blazar. Therefore, we loop over the spectral parameters of the central blazar and fix them at each step. Then, we add the corresponding reweighted halo template and extract the likelihood values. More specifically, we start from the optimized ROI model of the LAT data for each source i as described in Section 2. 2. We loop through values of the source-intrinsic spectral index, Γ int = Γ int,0 + ΔΓ int , where Γ int,0 denotes the best-fit value from LAT data (see Table  1 ) and ΔΓ int ranges from -0.
Within the propagation module of CRPropa, we set the tolerance parameter, which determines the numerical precision, to 10 -9 and set the minimum step length of the simulation such that time delays τ down to 1 yr can be resolved. In order to reduce the computational time, we stop tracing particles if their energy drops below 0. 1 GeV or if their total propagating distance becomes larger than 4 Gpc. We inject monochromatic gamma rays with energies ò equal to the central energies of bins Δò between 1 GeV and ≈31. 6 TeV using 4 bins per decade. The value of the maximum energy is limited by the available computational resources for the simulations. As shown in Section 3. 2, the intrinsic blazar spectra will tend to have lower cutoff energies so that this choice does not affect our results. The time delay τ of the cascade gamma rays is calculated through cτ ≈ (d -D), where c is the speed of light.
2016). Another potentially important systematic uncertainty is the difference in the absolute energy scales of H. E. S. S. and the LAT. For the LAT, the error on the energy scale is of the order of a few percent  (Ackermann et al. 2012) , whereas the reconstructed energies of gamma rays observed by IACTs have an uncertainty of typically 10%  (Hofmann et al. 2000). We can allow for a relative shift in energy between the two instruments by adding a scaling parameter s in our fitting procedure, such that the model fluxes fitted to H. E. S. S. data are changed to f (E) → f(E(1 + s)). We constrain s to lie within ±0. 2. Repeating the fits for all sources, we find that for low magnetic fields s tends to its lowest allowed value. This minimizes the cascade emission, as the H. E. S. S. data are effectively shifted to lower fluxes.
Acknowledgments The support of the  Namibian authorities and of the University of Namibia  in facilitating the construction and operation of H. E. S. S. is gratefully acknowledged, as is the support by the  German Ministry for Education and Research (BMBF) , the  Max Planck Society , the  German Research Foundation (DFG) , the  Helmholtz Association , the  Alexander von Humboldt Foundation , the  French Ministry of Higher Education, Research and Innovation ,  the Centre National de la Recherche Scientifique  ( CNRS/IN2P3  and  CNRS/INSU) , the Commissariat à lénergie atomique et aux énergies alternatives (CEA), the  U. K. Science and Technology Facilities Council (STFC) , the  Irish Research Council (IRC)  and the  Science Foundation Ireland (SFI) , the  Knut and Alice Wallenberg Foundation , the  Polish Ministry of Education and Science , agreement No.
Each of these studies investigates aspects of the SARS-CoV-2 infection specific to the population analyzed, such as incidence of infection and identification of specific risk factors. Moreover, 4 patient registries, comprising aggregated data from more than 2 million people, joined the project for additional studies regarding population-level analysis. The studies also assess the effect of the SARS-CoV-2 vaccination. Among the prospectively collected variables, there are the type and timing of administered vaccines, postvaccination side effects, humoral and cell-mediated response to vaccination at pre-established time points, and breakthrough infections (BI), defined as a SARS-CoV-2 infection occurring despite the protection elicited by the vaccination. The impact of different viral variants on vaccine effectiveness has also been evaluated.
Moreover, 4 patient registries, comprising aggregated data from more than 2 million people, joined the project for additional studies regarding population-level analysis. The studies also assess the effect of the SARS-CoV-2 vaccination. Among the prospectively collected variables, there are the type and timing of administered vaccines, post-vaccination side effects, humoral and cell-mediated response to vaccination at preestablished time points, and breakthrough infections (BI), defined as a SARS-CoV-2 infection occurring despite the protection elicited by the vaccination. The impact of different viral variants on vaccine effectiveness has also been evaluated. The main key methodological strengths of the project are the harmonized data collection across different cohorts and countries and the technical infrastructure of highperformance computing (HPC) facilities for processing data.
Finally, the typical limitations of a cohort study, such as inadequacy for issues with long latencies, the need for a large population to generate enough data points, and the need for extended follow-up periods for the outcome to manifest, are much less relevant in a pandemic context, strengthening the study design of the ORCHESTRA project. With this premise, ORCHESTRA described the immunogenicity of the COVID-19 vaccination and its protective effect against severe disease, both in healthy  [10, 11]  and fragile subjects, rapidly after the introduction of vaccines  [18, 19]. Other studies have reported results that are in line with ORCHESTRA. For example, in one cohort study conducted in Switzerland after the emergence of Omicron  [26] , individuals with natural immunity had 15 times lower antibody titers than vaccinated people, and less than half of them showed neutralization activity.
In another cohort study enrolling HCWs  [27] , triple vaccination and double vaccination with prior or subsequent infection showed the highest neutralization response. Scientific evidence from all these studies can be of relevance both in informing public health vaccination strategies and in addressing vaccine hesitancy. In conclusion, notwithstanding the intrinsic limitations of cohort studies, this type of study represents one of the most feasible designs to explore risk factors, genome associations, public health interventions, long-term sequelae, and vaccination effectiveness in a pandemic situation. Importantly, they can provide early information to design randomized clinical trials and play an essential role in data harmonization among different settings and countries. The ORCHESTRA project highlights how EU-funded research, which by design involves multiple countries and settings, heterogeneous data sources, and multidisciplinary teams, can provide access to multifaceted and extensive data.
Funding: The ORCHESTRA project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101016167. The views expressed in this paper are the sole responsibility of the authors and the Commission is not responsible for any use that may be made of the information it contains. Institutional Review Board Statement: Ethics committee approval has been obtained for the master protocols and for each participating center. Informed Consent Statement: Informed consent was obtained from all subjects involved in the studies.
We acquired B-mode cine-loops with a total of 300 images during 3 entire heart cycles using a Vevo2100 echograph (VisualSonics). We traced the contours of the myocardial wall of the left ventricle (endocardium and epicardium) using the VevoStrain® software (VisualSonics). This method allows us to follow "natural" acoustic markers that are statistically evenly distributed within the tissue, and define 48 points of interest around the left ventricle whose displacement in space can be tracked over time (speckle tracking). For each of the 48 points, we analyzed six parameters, including velocity (point's movement speed), displacement (point's overall change in position), strain (percentage of deformation of the wall compared to its original shape), strain rate (speed at which strain occurs), shear (angular deformation between the epicardium and the endocardium), and shear rate (shear evolution over time).
Positive values of strain are usually regarded as elongation, while negative values translate myocardial shortening. In this study, we considered an increase or a decrease in the absolute value of strain to evaluate net elongation. Since the contraction of the left ventricle is a complex phenomenon involving thickening, shortening, and torsion, we acquired some of these parameters in two perpendicular axes: circumferential (shortening/elongation) and radial (thinning/thickening of the wall, Fig. 2a ). We also evaluated the temporal values for each parameter by taking into account mean values, peak values (amplitude), and the time at which the peak value was reached (time to peak, TTP). Accordingly, 96 parameters were obtained for each of the 48 LV points, representing 4608 dynamic data point per mouse heart.
Statistical analysis was performed using JMP® software (SAS Institute Inc). Nominal logistic regression was applied to all raw data, and stepwise regression was used to identify combinations of parameters to build the receiver operating characteristic (ROC) curve (with positive response indicating fibrosis). Two-way ANOVA was conducted to assess differences between adaptive and fibrotic remodeling in terms of delta strain on adjacent left ventricle regions. Histology and correlation between biological and mechanical parameter: After the echocardiography, mice were terminated by isoflurane overdose, and the hearts were excised, fixed in 4% paraformaldehyde at 4°C for 48 hours, and embedded in paraffin. Six µm-thick cross-sections were obtained from the corresponding parasternal short axis. Areas of fibrosis were identified by Sirius red and Masson's trichrome staining, and regions exhibiting kinetic disorders in the echocardiography loops (hypokinetic, akinetic, or dyskinetic) were used to manually overlay the parameters obtained from the ultrasound images and histology (Fig.
Bioinformatics analysis: The spatial transcriptomic data was visualized using Partek® Flow® genomic analysis software. The Space Ranger 1. 3. 0 algorithm was first applied to align sequences and create a matrix from the spatial barcodes. The resulting data was then superimposed on histology (Fig. 5 ). Spots with insufficient information were filtered out, as well as genes detected in less than 1% of the spots (Supplementary 1d). A SC Transform normalization (Seurat) was applied to account for variable sequencing depth across spots  9. The Seurat3 integration function was then used to normalize inter-sample variability. Principal component analysis (PCA) was performed to classify spots by expression profile, followed by Uniform Manifold Approximation and Projection (UMAP) and graph-based unsupervised clusterization.