dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability Spatial weather data used for this work are publicly available online from Copernicus Climate Data Store and National Centers for Environmental Prediction. Event observation data are available from GBIF: https://doi. org/10. 15468/dl. n2agbd; https://doi. org/10. 15468/dl. cc6bbn; https://doi. org/10. 15468/dl. jeq9wa and https://mushroomobserver. org.
Cloning, expression, and purification of full-length SNV L K124A The L gene of SNV (GenBank accession: KT885044. 1) was chemically synthesized (GenScript). The gene was amplified and the point mutation K124A as well as a C-terminal StrepII-tag were inserted by PCR. The gene was cloned into a modified pFastBacHT B vector using the NEBuilder HiFi DNA Assembly kit (New England Biolabs). After transformation of the resulting plasmid into DH10EMBacY Escherichia coli cells containing a bacmid as well as a transposase-encoding plasmid, recombinant bacmid was isolated and transfected into Sf21 insect cells for the production of recombinant baculovirus. Protein expression was carried out in Hi5 insect cells. Harvested cells were resuspended in lysis buffer (50 mM HEPES(NaOH) pH 7. 0, 500 mM NaCl, 10% (w/w) glycerol, 2 mM dithiothreitol), supplemented with 0.
Cryo-EM data processing and model building Movies were realigned using Motioncor2  [41]  and then imported in cryoSPARC 3. 3. 2  [42]. After CTF estimation, micrographs that showed an absence of ice or contamination by crystalline ice were manually removed (2337 micrographs were kept). The Blob picker utility with search diameter between 100 and 190 Å was used to pick 883,096 particles. Binned extracted particles were subjected to two rounds of 2D classification to clean the dataset. SNV L K124A particles were re-centered and re-extracted with a box size of 360x360 pixels 2 without binning (514,913 particles extracted). The CryoSPARC ab initio reconstruction utility was used to generate an initial model that was subsequently refined using the non-uniform refinement utility. Next steps were performed in Relion 4.
Acknowledgments We want to thank  Stephan Gu ¨nther  for his support throughout the entire project. We would also like to thank  Harry Williams  for helpful discussions and brainstorming interpretation sessions and  Tomas Kouba  and  Benoı ˆt Arragain  for discussions and feedback. We thank the  cryo-EM Facility  staff at the  CSSB in Hamburg  as well as the  IBS in Grenoble  and the  Wilhelm und Maria Kirmser-Stiftung. IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA). This work also used the platforms of the Grenoble Instruct-ERIC center (ISBG; UAR 3518  CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology (PSB ) supported by  FRISBI  ( ANR-10-INBS-05-02 ). The  ISBG ; UAR 3518 was also supported by  GRAL , financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche)   CBH-EUR-GS  ( ANR-17-EURE-0003 ).
Bank (EMDB) with the following accession codes: PDB ID 8CI5, EMD-16670. The AlphaFold2 model of the CBD was deposited in ModelArchive with the ID ma-l64m5 (https://www. modelarchive. org/doi/ 10. 5452/ma-l64m5). PLOS PATHOGENS Sin Nombre virus L protein structure and function PLOS Pathogens | https://doi. org/10. 1371/journal. ppat. 1011533 August 7, 2023 2 / 23.
They were positively detected (above the quantification limit) in more than 90% of the tested pregnant women. Their concentration in biological fluids tested among the highest within the family. Because only BPA was monitored in the INMA-Sabadell cohort and due to its ban for use in food packaging in France, we deliberately chose to replace it with its main substitute, BPS. It must be underlined that other European regulations on phthalate plasticizers have led to the use of phthalates alternatives, the most known being di(2-ethylhexyl) adipate (DEHA). However, we could not substitute DEHP with DEHA since DEHA is a non-phthalate plasticizer. We also could not add DEHA in the mixture since biomonitoring data of DEHA in a large European cohort of pregnant women were not available at the time the project was conceived.
This repartition of nine levels across the ovary is considered enough to be representative of the ovary. Follicular populations will be assessed on nine hematoxylin and eosin-stained sections per ovary via classical slide lecture. The slides will be scanned and analyzed using software that allows synchronized visualization and displacement of analyzed and adjacent sections. To avoid counting the same follicle several times, we will only count the follicles with a visible oocyte nucleus if they were not clearer on the adjacent section. Follicles will be classified from primordial to antral stages, and atretic appearance will also be considered. The obtained data will be analyzed as total raw count, density and total estimation in the whole ovary. In order to decipher potential differences among follicular populations, cellular proliferation will also be quantified on nine ovarian sections through Ki67 staining.
F1 Offspring at Feto-Placental Stage This part of the FEDEXPO project aims to assess the feto-placental development as well as the placental structure and function. Numerous parameters for feto-placental biometrics can be measured during GD14 and GD28 ultrasounds  [41]. At GD14, we will measure parameters regarding the fetus (body perimeter, body width and length, head length, biparietal diameter, cardiac frequency) and the placenta (placental volume, blood perfusion using three indices as previously described  [42] ). At GD28, a 2D and 3D quantitative Doppler will also add data on, respectively, umbilical and cerebral blood as well as feto-placental vascularization. After the GD28 ultrasound, the gestations will be terminated. Feto-placental units will be recovered, weighed and dissected.
Statistical Analysis Statistical analysis will be performed using GraphPad Prism for Windows (GraphPad Software, San Diego, CA, USA). An unpaired t-test and one-way or two-way analysis of variance (ANOVA) will be used to compare quantitative data between two or more groups, if data sets follow a normal distribution. In that respect, a Shapiro-Wilk test will be performed beforehand, as well as checking for the heteroscedasticity of variance. If the conditions are not met, data will be analyzed via non-parametric Mann-Whitney and Kruskal-Wallis tests. Regarding the data from the progenies of the exposed animals, litter and sex effects will be taken into account. Qualitative data will be compared using a Chi2 test or Fisher exact test. A p-value below 0. 05 will be considered significant.
This approach allows simulating the most extreme events, without having to consider most "normal" events. So far, this approach has been based on single model experiments  9, 10  or constrained by reanalysis data  11. Both studies focused on presentday or pre-industrial conditions. Therefore there is an urgent need to apply this type of method to scenarios of climate change, in order to evaluate worst cases in near future climate conditions. We consider mean daily temperature (TG) and determine the warmest period of 15 days (in Ile-de-France) in July-August, for each year (TG15d). This length (15 days) is motivated by the duration of Olympic games. The goal of this paper is to estimate worst case scenarios of TG15d heatwaves in the Paris area in the decades around 2024, and outline the synoptic features of those events in order to understand whether their causes are unique.
2 ). The record 2003 value is exceeded in the three "intense" SSP scenarios, although not in SSP1-2. 6, with this IPSL model simulation. The highest values for TG15d can occur with any scenario when considering the CMIP6 ensemble (not shown). These results with SWG simulations from the IPSL model data (boxplots in Fig. 2a-d ) show that the temperature increase of the most extreme events between 1951-2000 and 2001-2050 exceeds 3. 5 °C in three SSP scenario simulations (from SSP2-4. 5, SSP3-7. 0, and SSP5-8. 5). More than half of the SWG heatwaves with analogs in 1951-2000 stand above the upper half of the TG15d values in 2001-2050 (above the median), but not the outliers (Fig. 2a ). The value observed in 2003 (in ERA5) is not reached with SWG simulations from the IPSL model and analogs in 1951-2000.
Data and bias correction We use the simulations of 14 CMIP6 models (out of 31 models)  14  (see Supplementary Table  1 ) and four shared socio-economic pathways (SSP) scenarios 27 (SSP1-2. 6, 2-4. 5, 3-7. 0 and 5-8. 5). The criterion to retain these models is their availability on an Earth System Grid Federation (ESGF) server for the four SSP scenarios on daily increments. A data quality check is also performed on the available files. The historical simulations cover 1850-2014. The scenario (SSP) simulations cover 2015-2100. We extract the model gridpoints that cover continental France. For simplicity, we take one simulation for each model (some models provide ensembles) and visually check that this choice samples the overall spread of all simulations. The multivariate bias correction method R 2 D 215 is used to correct the bias of the marginals and the dependency structure of the temperature and precipitation, with respect to the SAFRAN reanalysis  28  ( ≈ 8 km of horizontal resolution).
ACKNOWLEDGEMENTS We thank  Soulivanh Thao  for helping handling CMIP6 datasets. The authors acknowledge the support of the  INSU-CNRS-LEFE-MANU  grant (project  DINCLIC : D. F. ), the grant  ANR-19-ERC7-0003  (BOREAS: D. F. ) and the grant  ANR-20-CE01-0008-01  (SAMPRACE: P. Y. , C. C. ), a  CEA-ANDRA- EDF R& D grant  (COSTO: P. Y. ), the " Explore2" French  project (Y. R. , M. V. ), the  French program  LEFE (COESION: M. V. , D. F. ). This work has received support from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101003469  (XAIDA: P. Y. , D. F. , F. P. , M. V. ). R. N. was supported by a doctoral grant from  CEA.
Data availability: ERA5 reanalysis temperature data is available from the Climate Explorer tool https://climexp. knmi. nl/start. cgi. CMIP6 data without bias corrections (temperature, S. L. P. ) is available from the Earth System Grid Federation (ESGF) node https://esgf-node. ipsl. upmc. fr/projects/cmip6-ipsl/. Bias-corrected data (temperature, S. L. P. ), S. L. P. analogs, and SWG simulations are available upon request to the corresponding author (P. Y. ).
Data processing The Griggs apparatus uses an external load cell to measure the force applied to the sample column. The displacement of the σ 1 piston is measured using a direct current displacement transducer with a resolution of 1 μm. Experimental data are digitally recorded at a sampling rate of 1 Hz and processed by the open source MATLAB program RIG (http://mpec. scripts. mit. edu/peclab/software/). Piston displacement was corrected for apparatus compliance while the force was corrected for friction based on a set of calibration tests. The samples thin by ~40% in the high strain experiments; we assume that the thinning is continuous with increasing piston displacement. The mechanical data are corrected for shear zone thinning and forcing block area change with increasing shear strain, as well as variations in confining pressure.
, 1990). However, based on the mechanical data from  Mares and Kronenberg (1993)  it is unclear whether there is directional anisotropy in muscovite (Fig. 15 ). The mechanical data for samples oriented 45 • to the basal plane (001) with slip directions of [310] and [010] provide a good approximation of the average stress for all of the muscovite mechanical data from  Mares and Kronenberg (1993)  (Fig. 15 , Supplementary Fig. S11 ). Therefore, we use the dataset from experiments with [310] and [010] slip directions to define a power-law flow law for muscovite, with parameters of n = 25, Q = 45 kJ/mol, and A = 2e-40 MPa -n s -1 (see supplementary material). In addition, we use the same mechanical data to develop a new exponential flow law for muscovite incorporating a stress squared term (e.
Acknowledgements L. T. would like to thank  Renee Heilbronner ,  Rudiger Kilian ,  Bettina Richter , and  Sina Marti  for their assistance with learning and operating the CIP-related programs as well as  Luiz Morales  for his assistance on the SEM. L. T. would also like to thank  Andreas Kronenberg  for his discussions on muscovite and biotite rheology and  Caroline Seyler  for sharing code related to the rheological mixing models in Fig. 18. Willy and  Pascal Tschudin  are also acknowledged for making the petrographic thin sections of the samples. Elisabetta Mariani ,  Jun Muto , and  Virginia Toy  are acknowledged for their helpful and constructive comments that improved the manuscript.
| Geometry and Mesh Preoperative Computed Tomography Angiography (CTA) images of two stenosed femoral arteries were obtained with ethical approval at CHU de Rennes and informed consent from participants: one with a dominant calcified atherosclerosis, while the other with a lipidic plaque. The CTA datasets (of 0. 7 mm slice increment and thickness) were processed using the open-source softwares ITK snap and 3D slicer to generate models corresponding to each diseased artery. After selecting the desired regions of interest, the lumens were segmented using the snake active contour method implemented in ITK-snap. 27 With relatively high capabilities in absorbing x-rays, calcifications were also easily identified and segmented based on their high CT densities by thresholding. Then, subtract Boolean operations were applied in order to generate the lipidic plaque and the vessel walls, which are generally not clearly visible on CTA images.
9 to 1. 1] for PTA treatments. 4  As proposed in Reference11, in-plane arterial translations (in x-y plane) were allowed at both arterial ends to adapt to balloon radial inflation, with the balloon itself being clamped from both sides. Axial translation and rotation were blocked in the model as well. Moreover, no internal pressure was further considered at the stenosed arterial inner wall under the hypothesis that the CTA data (from which the PS geometries were generated) were obtained at normal blood pressure. The non-linear contact between the balloon and stenosed artery is frictionless and formulated by pure penalty method. ANSYS implicit commercial finite element software (ANSYS ® Academic Research Mechanical, Release 19. 1. ) was used to solve these large-deformation analyses.
They can either be fixed points (attractors of size one), or cyclical attractors. Simulations of mutants consist in modifying logical rules. Knock-out (KO) mutants are simulated by blocking the logical rule of the desired nide to 0, and Knock-in (KI) mutants by fixing it to its maximal level or active at the desired threshold for multivalued nodes (overexpression). The logical model was built using GINsim software and is available in XML format (GINML) on the GINsim Repository (http://ginsim. org/node/253, also provided in Supplementary File 1). All attractors, belonging to either the wildtype and mutant simulations (double mutation JAK_H KI and JAK_MK KI) were sought and characterised using GINsim in conjunction with the CoLoMoTo toolbox  (Levy et al. 2018)  using Python.
Results To increase our understanding of CAKUT disease aetiology, we performed multi-omics analyses on a total of 162 amniotic fluid samples. The omics types include previously published peptidome and proteome data from non-severe CAKUT and severe CAKUT patients, supplemented with a novel miRNome (See Methods section). We applied three different bioinformatics workflows to analyse and integrate this multi-omics data set. The workflows include intrinsic analysis using unsupervised (mixOmics) and supervised (momix) approaches, and extrinsic data analysis based on prior knowledge databases (pathway-level analysis). Each of the three complementary workflows used at least two types of omics data (Figure  1 ). In order to facilitate data integration and analysis, both data and analysis scripts were FAIRified.
Multi-omics integrative analysis with mixOmics As a first approach to analyse the CAKUT multi-omics data, we used mixOmics, combining the miRNome and peptidome data with the mixOmics package  5. This approach identifies common patterns among multiple omics datasets by projecting data into a small number of dimensions, where the number of dimensions or components can be specified. Only the samples that matched between the two omics data sets and were in the training cohort of the peptidome study  6  were used (n=46; 30 non-severe and 16 severe CAKUT cases), due to the nature of the analytic approach in the supervised classification method of the mixOmics package. In the mixOmics analysis, the proteomics data were not used, because there were a limited number of matching samples compared to the miRNome and peptidome data (Figure  1 ).
These mainly include positive correlations for a large number of miRNAs with only three peptides. Only one negative relation was observed between one of the COL1A1 peptides and mir-hsa-6768-5p. For the highest scoring miRNAs and peptides of the mixOmics analysis, a network-based visualisation was performed. This network-based visualisation revealed a large collagen and cytoskeleton cluster (Figure  2C ). Furthermore, unsupervised analysis, shown by heatmap clustering (Figure  2D ), confirmed strong correlations between certain peptides and miRNAs. In conclusion, the mixOmics method indicates an important role for collagen on miRNome and peptidome level. Best place for Table  1  and Figure  2  Joint multi-omics dimensionality reduction analysis In the second strategy, we applied eight different unsupervised joint dimensionality reduction methods on the peptidome, proteome, and miRNome data using the momix notebook  8.
We used the 31 samples (18 non-severe CAKUT cases and 13 severe CAKUT cases) that matched between the three omics data sets. A joint dimensionality reduction method decomposes the omics datasets into omics-specific weight matrices and a joint factor matrix. We ran the dimensionality reduction methods to obtain the two most important factors (k=2). Most non-severe and severe CAKUT patients could be separated by one of these two factors, which segregate the two groups (Figure  3A-C ). To evaluate the methods and choose the most relevant factor, we measured how well the two sample groups could be clustered. For each method and each factor, we used k-means clustering. We ran k-means 1000 times and counted the number of samples that were in the correct cluster in accordance with the clinical diagnosis.
Pathway-level analysis We analysed the CAKUT omics data for overrepresented pathways within WikiPathways database  10. From 634 pathways in the database, 38 pathways were overrepresented and had a link between miRNA and protein (or peptide mapped into protein) based on the CAKUT patient data. In these pathways, we found 15 links between miRNome and proteome where both interaction partners are significantly differentially expressed. The PI3K-Akt Signalling Pathway (WikiPathways:WP4172)  11  contained five links between miRNAs and the peptidome or proteome (Figure  4A ). The 10 remaining links between miRNA and proteins are indicated in Figure  4B. The PI3K-Akt pathway also includes certain collagens that had been associated with CAKUT in the original study  6. However, we could not identify any significant links between these proteins and the miRNome.
Discussion Using the output from the mixOmics approach a network was identified related to collagen and cytoskeleton remodelling, consisting of COL3A1, COL18A1, TMSB4X, and COL1A1, and two smaller networks including COL1A2 and COL4A1. In this analysis, we used a supervised classification approach with the mixOmics method of the mixOmics package, which requires matching samples among omics data sets. Since the number of overlapping samples in all sets decreased when the proteomics data were included in the mixOmics-based analysis, we decided to exclude the proteomics data for this specific analysis. MixOmics proposes two approaches, sPLD-DA and PLD-DA. The difference between sPLS-DA and PLS-DA was insignificant, probably because sPLS-DA is expected to be beneficial over PLS-DA for high dimensional data  12.
Additionally, mixOmics analysis allowed the identification of a collagenrelated cluster solely based on the peptidome and miRNome data. The main variance in the data stemmed from a range of miRNAs that could be connected to a handful of peptides (Figure  2B ). Most of these relations were positive correlations, while only hsa-miR-6768-5p and COL1A1_pep30 (ADGQpGAKGEpGDAGAKGDAGPpGP) had a negative correlation. hsa-miR-6768-5p has not been previously identified or predicted to affect COL1A1. While an important role for collagen in CAKUT was previously established  6, 13, 14  , COL1A1 has not specifically been linked to CAKUT. Furthermore, this work highlights potential novel miRNA and peptide relations, which might be most relevant to study to get a better understanding of CAKUT. Unsupervised joint dimensionality reduction analysis with the momix notebook identified the most relevant molecules from the three omics data sets.
This protein is identified by all the three best performing methods due to different peptides (MOFA and RGCCA found the peptide COL4A1_pep1, tICA found the peptide COL4A1_pep2) (Supplemental Table  1 ) and it is associated with kidney diseases  13, 14, 19. Comparing the momix and mixOmics workflows, there is an overlap in the identified molecules of interest, including COL1A1, COL1A2, COL3A1, and COL18A1. In the GO enrichment analysis, we obtained different annotation terms indicating that momix and mixOmics approaches are complementary. The analysis at pathway-level used the molecular interactions of WikiPathways, a pathway database extended with miRNA-target information as a backbone to investigate the interactions of interest. The advantage of this method is that it integrates prior knowledge into the analysis, which is especially important when the signal extracted from the data is low.
Using this pathway analysis method, we identified 15 functional links between significant differentially expressed proteins and the miRNome. The PI3K-AKT signalling pathway hosts five of these interactions between the different omics data sets, making this the most relevant pathway for CAKUT disease progression (Figure  4A ). In addition, it harbours several collagen proteins previously identified by the other methods as well. A role for collagen in CAKUT disease development was previously established  6, 14, 20. The other interactions from i. a. Focal Adhesion (WikiPathways:WP306) or Senescence and Autophagy (WikiPathways:WP28806) pathway, are shown in Figure  4B. The limitation of the pathwaylevel analysis is the dependence on knowledge databases of molecular interactions. Nonetheless, for both pathways and miRNA-target interactions, there are several options regarding analysis.
On the one hand, WikiPathways is an open, community created, and expert curated database  10. The contributions that define the content are dependent on published literature, and the pathways undergo regular curation to be updated with current findings. On the other hand, miRTarBase is a miRNA-target interaction database that provides manually selected, experimentally validated miRNA-target interactions from published literature  21. Integrating analysis methods using these and other databases to cross validate the information measured on patients relevant conclusions for disease research. Altogether, the different bioinformatics strategies and methods presented in this paper offer a complementary spectrum of possible multi-omics analyses, the analysis of rare disease data sets. Notably, most of these methods identified the same (functional) group of genes, with differences on the weighing of correlation statistics or the use of prior knowledge supported methods.