dataesr/scientific-paragraphs-categorization huggingface dataset:
S2 ). statistical analysis. Graphs and data were analyzed with GraphPad Prism version 6. 00 (GraphPad software, La Jolla California USA, www. graphpad. com) and R (R Core Team 2017, https://www. R-project. org/). The non-parametric Mann-Whitney test was used to compare data from different conditions. Stacks were analyzed using the Amira, Imaris and Fiji 28 software programs.
Acknowledgements We thank the members of our groups for discussions and interest in this project. The authors wish to acknowledge the  TRI-Genotoul , the electron microscopy (CMEAB) and photonic microscopy ITAV facilities. The authors thank  Elisabetta Andermarcher  for expert manuscript editing. This work was supported by the  CNRS ,  Université Paul Sabatier ,  Région Midi-Pyrénées, la Ligue Nationale Contre le Cancer (comité de la Haute-Garonne)  and  ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Sciences & Health)  within the framework of the  Cancer Plan.
5°C min -1 , 15°C min -1 , 17. 5°C min -1 and 20°C min -1. Based on these dilatometric curves and assuming isotropic shrinkage, the sample density can be calculated as a function of the temperature from the shrinkage value, by using the following equation: = (1) where ρ(T) is the instantaneous relative density (g. cm -3 ), ρArchi the final density measured by Archimedes's method in absolute ethanol (g. cm -3 ), St the instantaneous vertical shrinkage (µm), Sf the final vertical shrinkage (µm) and hf the final height of the sample (µm). The X-Ray Diffraction (XRD) measurements were performed on a Theta-Theta PANalyticalX'Pert Pro diffractometer with a CuKα (λ=1. 54059 Å) radiation. The Rietveld method using Fullprof program was used for the structural refinement. The microstructural characterizations were carried out using a scanning electron microscope (SEM Zeiss Supra 55), equipped with an Energy Dispersive Spectroscopy (EDS) System.
Genotyping using SSR and SNP markers The panel was genotyped using 13 neutral SSR markers as described previously  [24]  and 609,658 SNPs from the Axiom™ J. regia 700K SNP array uniformly distributed over the 16 J. regia chromosomes  [9]. The quality control steps were performed using "PLINK 1. 9" software. [25]. Poly High Resolution (PHR) and No Minor Homozygotes (NMH) SNPs were filtered using stringent thresholds: SNP call rate (> 90%), minor allele frequency (MAF > 5%), and redundancy in the genome (SNP probes aligning in duplicated regions). Finally, 364,275 robust SNPs (59. 8% of the total number of SNPs) were retained for the following analyzes.
Structure analyzes and core collection construction Principal Coordinate Analysis (PCoA was used to determine the patterns of structure among the 150 accessions. Dissimilarities, based on allelic data, were calculated with 10,000 bootstraps, and transformed into Euclidean distances using a power transformation of 0. 5. PCoA was performed using "DARwin 6. 0. 14" software  [26] , supplemented by "scatterplot3d" R package for 3D visualization. As linked SNPs can account for too much in the population structure variance, particularly in linkage disequilibrium (LD) regions  [27] , a pruned subset of SNPs was also used for the PCoA. This filtering was completed using "PLINK 1. 9" software, keeping only the SNP with the higher minor allele frequency, and based on a threshold of r²=0. 2 (command --indep-pairwise 50 5 0.
2). A subset of 100 SNPs, number similar to the number of SSR alleles, randomly selected, was also tested using "PLINK 1. 9" software (command --thin-count 100) to compare PCoA results. Genetic structure of our panel was also investigated using two types of analyses depending on the markers. Bayesian model-based analysis using "STRUCTURE 2. 3. 4" software was implemented for the SSR markers  [28]. To identify the best number of clusters (K), ten runs were performed by setting K from 1 to 7. Each run consisted of a length of burn-in period of 100,000 followed by 750,000 Markov Chain Monte Carlo (MCMC) replicates, assuming an admixture model and correlated allele frequencies. The ΔK method  [29] , implemented in "STRUCTURE harvester"  [30]  was used to determine the most likely K. Using SNP markers, sparse non-negative matrix factorization algorithm was implemented using "sNMF 2.
0" software, available as a function of the "LEA" R package  [31]. This software presents a fast and efficient program for estimating individual admixture coefficients from large genomic data sets, and produces results very close to Bayesian clustering programs such as "STRUCTURE"  [32]. The choice of the best number of clusters (K) is based on a cross-entropy criterion implemented in "LEA" R package. For SSR and SNP markers, thresholds of 0. 8 and 0. 7 for admixture coefficient, respectively, were chosen to consider one accession as admixed. Then, the genetic relationships between the 150 accessions were also assessed by the Neighborjoining method  [33]  using "DARwin 6. 0. 14" software. The Unweighted Neighbor-Joining option was used to build the trees. In addition, core collections were constructed with a sampling intensity of 33% (n=50/150), using two methods: (i) the "maximum length sub tree" function  [34]  of "DARwin 6.
0. 14" software, which looks for a subset of accessions minimizing the redundancy between them, and limiting if possible the loss of diversity (the diversity here is expressed by the tree built), and (ii) the "entry-to-nearest-entry" method  [35] , implemented in "Core Hunter 3" software  [36] , which looks for a subset of accessions as different as possible from each other, avoiding selecting a few clusters of similar accessions at the extreme ends of the distribution. The number of alleles retained in one core collection was estimated.
Similar results were observed with the "entry-to-nearest entry" method with also 32/50 accessions in common between the data sets based on SSR or SNP markers. Moreover, the consistency of the results between core collection construction methods was checked for both markers. Using SSR markers, 37/50 accessions are in common between the two methods, and 43/50 accessions using SNP markers. We estimated the number of alleles retained in each core collection. For SSR markers, we retained 88. 57% and 94. 29% of the 105 total alleles found within the entire collection, using the "maximum length subtree" and the "entry-to-nearest entry" methods, respectively. Using SNP markers, we retained 99. 99% and 99. 98% of the 728,550 total alleles found, with the same methods, respectively (Table  2 ).
Comparison of three sets of SNPs for PCoA assessment In addition, for PCoA assessment, we compared the entire set of 364,275 SNPs with a subset of SNPs filtered for LD, with a threshold of r²=0. 2 (Figure  6a ). We then retained 24,422 SNPs, or 6. 7% of the entire set. Interestingly, the results found for both datasets are strongly similar, with the scatterplots well distinguished, according to the K=2 results (Figure  6b ). We still distinguish the French accessions from the Californian accessions within the group B. The main difference is that the variance is better explained by the first three axes using the LD-pruned set (18. 90% vs. 14. 91%). By comparing the entire set with a random subset of 100 SNPs, a range comparable to the total number of SSR alleles, we found that the scatterplots are less well defined, but still in agreement with K=2 results (Figure  6c ).
Moreover, the four corecollections constructed kept at least 88. 57% of the allelic diversity. In parallel with the preservation of the allelic diversity, it is also necessary to take into account the phenotypic variability. The INRAE walnut germplasm collection contains some accessions with unusual traits, such as weeping branches, laciniate leaves or purple foliage, which may be used for ornamental purposes. Interestingly, the accession with weeping branches 'RA 1195' is in the four core-collections, and the accessions with laciniate leaves or purple foliage are in three of them. Based on chronological phenotypic data available  [43]  and new data acquired, we also looked if the core-collections contain a high or small range of the variability of some important traits. Let us take the example of a trait related to the phenology, crucial for climate change adaptation, the budbreak date.
Acknowledgments We want to thank the  Fruit Tree Experimental Unit of the INRAE in Toulenne and the Prunus/Juglans Genetic Resources Center  for the maintenance of the collection and for helping us to collect the samples. We acknowledge the  BioGEVES laboratory  for DNA extraction and SSR genotyping, and ThermoFisher for SNP genotyping. Then, the CTIFL, holder of the project " INNOV 'noyer", in partnership with the  INRAE of Bordeaux , want to thank the " Région Nouvelle-Aquitaine", and "Cifre" convention of "ANRT" (Agence Nationale de la Recherche et de la Technologie). It is also important to note that the project is supported by the " Agri Sud-Ouest Innovation " competitiveness cluster. Finally, we would like to thank the late  Eric Germain , former head of the breeding program at the  INRAE of Bordeaux  from  1977  to 2007.
MATerIAlS And MeTHOdS donor specifications SVF from patients with SSc was obtained from the cell biobank (n°CD-2011-1331 attached to the Scleradec II clinical trial : ClinicalTrials. gov NCT02558543). SVF from HD was obtained from surgical residues of adipose tissue after liposuction for cosmetic purposes. The materials and methods are described in the online supplementary text.
H&E histological staining revealed a significantly impaired capacity of SSc-SVF to generate vessels compared with HD-SVF (figure  2C , upper left graph). Furthermore, in the implants containing SSc-SVF, the number of Dextran+ perfused vessels was slightly decreased compared with the HD group, without reaching statistical significance (p=0. 24; figure  2C , upper right graph). Interestingly, this impairment of perfusion was also observed by ultrasound Doppler imaging, as indicated by a decrease in the vascularisation percentage (p=0. 11; figure  2C , lower right graph). Taken together, these data demonstrated that SVF from patients with SSc exhibited slightly impaired angiogenic potential.
The quantitative reverse transcription-PCR analysis confirmed significant upregulation of the gene expression of ACE2, Apelin, IDO1 and GDF15 in SSc patients, while that of SAA1 and HAS1 only showed a statistical tendency towards upregulation and downregulation respectively (figure  3B ). To provide a cohesive view of the biological functions associated with the changes in the SSc-SVF gene expression profile, we conducted a gene ontology analysis using the DAVID database. The upregulated genes showed a strong association with the positive regulation of cell proliferation as well as angiogenesis and vasculogenesis regulation, whereas the categories enriched among the downregulated genes were associated with the regulation of cell proliferation and migration, positive regulation of angiogenesis and the inflammatory response (figure  3C  and online supplementary tables 4 and 5).
The contribution focuses on indoor of buildings and more specifically on rooms. The overall acquisition of a building can be performed accurately and fast with a laser scanner. In order to assign thermal information to the point cloud obtained from the laser scanner, each room is acquired with a thermal camera. One should note that the segmentation of a point cloud into rooms was already proposed in  Macher et al. (2017). The paper will be composed of four main parts. The fundamental concept of measurements of an infrared camera will be first presented. The acquisition of a dataset with a thermal camera and a laser scanner will be then described. Next, the different calibrations of both infrared and digital cameras will be detailed. Finally, the developed processing chain will be explained.
CREATION OF A THERMAL POINT CLOUD Based on the RGB images, IR images and the point cloud issued from the laser scanner, a processing chain was developed so that thermal and geometric information can be combined. The processing chain is detailed in this section and the first result is shown and discussed. The dense point cloud obtained with RGB images is not enough accurate to create a model based on it. The aim is to texture the laser scanning point cloud with the IR images. To do so, the image-based point cloud and the laser scanning point cloud were aligned manually in CloudCompare software (EDF R&D).
Processing chain One should note that the scaling of the image-based point cloud combined with alignment of these two point clouds will be automated in future works. The image-based point cloud is used as a reference so that the laser scanning point cloud can be inserted in Metashape and textured by IR images. Once the laser scanning point cloud is imported in Metashape software, the texturing is finally considered. The texturing of the point cloud was first tested but Metashape do not take into account the transparency of the image parts with no IR information and black colour is applied. Thus, a mesh of the laser scanning point cloud was created as illustrated Figure  5b. The point normals have to be oriented from the exterior to the interior of the room to perform properly the texturing.
CONCLUSION AND FUTURE WORKS The investigation of a thermal camera, which has the ability to take thermal and RGB images simultaneously, was proposed for the combination of thermal and geometric information in indoor of buildings. First tests were conducted for a room. The developed processing chain is based on RGB images, IR images and a point cloud issued from laser scanning and allows creating a mesh textured with IR information. Results are promising since the IR images taken with the thermal camera can be used to texture laser scanning data by exploiting RGB images. However, a few improvements will be proposed in future work. Some steps of the processing chain are still partially manual and their automation will be studied. The compass information of the thermal camera will be exploited to improve the automation of RGB images orientation.
Acknowledgements. MC thanks  Laurent Verstraete  for very useful discussions about the DustEM model and  Mederic Boquien  for discussions about extinction prescriptions. MC thanks the  Centre National d'Etude Spatial  for its financial support. We acknowledge financial support from the  "Programme National de Cosmologie and Galaxies" (PNCG)  funded by  CNRS/INSU-IN2P3-INP ,  CEA  and  CNES, France , from the  ANR  under the contract  ANR-15-CE31-0017  and from the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the  "Investissements d'Avenir" French government  programme managed by the  ANR.
Image acquisition and analysis Fluorescent CD31 immunostained images were acquired using a Leica M205FA microscope and Leica software LAS X (objective Â10; field of view [FOV]: 1. 2614 mm 2 ). An in-house macro, incorporated into ImageJ software (https:// imagej. nih. gov/ij/), was used to draw the shape of the CD31-positive vessels, and these images were merged with the associated FITC-albumin images (Fig. 2D ). The number of vessels (CD31þ) and perfused vessels (CD31þ and FITC-albuminþ) were reported per FOV. For the analysis of Ki67, CD4, CD8, CD161/NK1. 1, and CD68 expression, 4 images per tumor (2 central and 2 peripheral), were obtained using a Zeiss Axioplan2 microscope with Visiview software (objective Â20; FOV: 0. 466 mm 2 ). Proliferating Ki67-positive cells were counted among a random selection of 100 nuclei stained with DAPI.
The methods helped identify damage and associated causes (diagnosis, Annex II) in support of water quality management. Examples are the detection of strong antiandrogenic effects in the River Holtemme (Germany) and the identification of the fluorescence dye coumarin 47 as the cause of this effect  [24] , the detection of mutagenicity in the Rivers Mulde and Rhine and the identification of diaminophenazines  [22]  and synergistic effects of aromatic amines with natural alkaloids  [25]  as mutagenicity drivers. These examples may also underline how monitoring (Annex V) with EBM's can help evaluate status and trends. Quality/performance criteria for the benchmarking of estrogenicity bioassays have been recently investigated in an inter-laboratory comparison study  [26]. In a Europe-wide demonstration program supported by SOLUTIONS, the NORMAN network, the Swiss Centre for Applied Ecotoxicology and the Joint Research Centre of the European Commission, the reliability of EBMs for screening of estrogenic compounds was analyzed to harmonize monitoring and data interpretation methods, and to contribute to the current WFD review process.
contigs, using default parameters. The draft genome sequence of O. jilunii strain P8502 is composed of 23 contigs (N 50 , 537,034 bp; L 50 , 4) and 21 scaffolds (N 50 , 643,697 bp; L 50 , 3), for a total of 6,704,032 bp with 42. 1% GϩC content. Annotation using Prokka (version 1. 12) (16) predicted 5,524 genes, including 5,406 proteincoding genes, 3,498 of which (63. 32%) are assigned to clusters of orthologous groups. In addition, 51 RNA genes were detected (6 rRNAs and 45 tRNAs). In addition, 43 resistance genes are predicted, including 3 genes associated with antibiotic resistance (tetracycline). No toxin/antitoxin module or bacteriocinassociated gene could be found.
Results In total, 16,016 individuals from 19 population-based or familybased cohort study and one case-control study were included in the current study. Additional information regarding the population characteristics, genotyping, and imaging methods are provided in the "Methods" section and in Supplementary Data 1-4. Heritability of lobar brain volumes. Using a family-based approach, we found an age-and sex-adjusted heritability (h 2 ) for occipital lobe of 50% (95% confidence interval (CI) 38-62%), for frontal lobe of 52% (95% CI 40-64%), for temporal lobe of 59% (95% CI 49-69%), and for parietal lobe of 59% (95% CI 49-69%) (all: p ≤ 1. 9 × 10 -19 ) (Supplementary Data 5). In comparison, the age-and sex-adjusted heritability estimate for total brain volume was 34% (95% CI 22-46%, p = 8. 8 × 10 -11 ).
This signal overlapped previously associated signals with intracranial volume  16, 17  and is further implicated in bone mineral density  20  , height  21  , waist-hip ratio  22  , and infant length  23. The index variant associated with intracranial volume (rs11759026) and our top variant are in linkage equilibrium (R 2 = 0. 07, D' = 1, p = 0. 0002)  19. We also found suggestive associations between rs11759026 and both frontal (-1. 0 cm 3 , p = 6. 3 × 10 -5 ) and occipital lobar volume (-0. 31 cm 3 , p = 6. 6 × 10 -3 ). Each locus was located in regions that are under epigenetic regulation in brain tissue (Supplementary Data 6) or close to genes or genomic loci associated with Mendelian brain-related diseases. The variant rs2279829 (3q24) is located in the 3'-untranslated region (UTR) of the Zic Family Member 4 (ZIC4) gene and close to the related ZIC1 gene.
Methods Study population. The study sample consisted of dementia-and stroke-free individuals with quantitative brain MRI and genome-wide genotypes from 19 population-and family-based cohort studies participating in the Cohorts of Heart and Aging Research in Genomic Epidemiology consortium and the case-control Alzheimer's Disease Neuroimaging Initiative study. In total, 16,016 participants were included, 15,269 participants of European ancestry, 405 African Americans, 211 Chinese, and 131 Malay. We attempted to replicate our findings in 8,789 European ancestry individuals from the UK Biobank, an ongoing prospective population-based cohort study located in the United Kingdom. Descriptive statistics of all populations are provided in Supplementary Data 1. DNA from whole blood was extracted and genome-wide genotyping was performed using a range of commercially available genotyping arrays.
Genotype imputations were performed in each discovery cohort using 1000 Genomes version 1  33  as reference and using the Haplotype Reference Consortium (HRC) version 1. 1 in the replication cohort (Supplementary Data 2). MRI methods. Three-dimensional T1-weighted brain MRI data were acquired by each cohort (Supplementary Data 1). Cohorts in the discovery sample segmented the T1-weighted images into supra-tentorial gray matter, white matter, and cerebrospinal fluid. The methods of image segmentation varied across study cohorts (Supplementary Methods). However, the majority used a previously described kNN algorithm, which was trained on six manually labeled atlases  34  , or in-house imageprocessing pipelines. In each study, MRI scans were performed and processed with automated protocols, without reference to clinical or genetic information.
We studied the total volume (sum of white and gray matter and the left and right hemisphere) of the frontal, parietal, temporal, and occipital brain lobes, adjusted for intracranial volume. Descriptive information of the lobar volumes across the different studies is provided in Supplementary Data 3. Differences in average brain lobar volumes were accepted as differences in MRI acquisition, processing, segmentation, and demographics, which exist over cohorts. As a replication, we used the released volume measurements of 8,789 UK Biobank participants, extracted using the FreeSurfer software version 6. 0, which obtains lobar volumes by adding up regions of interest volumes  35, 36. As only FreeSurfer gray matter volumes were available for this study sample, the replication sample volumes were smaller than the volumes in the discovery sample (Supplementary Data 3).
We then performed two inverse-variance weighted fixed-effect metaanalyses in METAL  39. First, we meta-analyzed all participants of European ancestry, then performed a multi-ethnic meta-analysis including African Americans (n = 405), Chinese (n = 211), and Malay (n = 131). After meta-analyses, genetic variants with a total sample size of <5000 were excluded. We performed conditional analysis on the index variants to determine whether there were multiple independent genome-wide significant variants in a locus using the Genome-Wide Complex Trait Analysis (GCTA) software (--cojo, --p-cojo)  40, 41. Genotypes in the Rotterdam-study (all 6291 individuals of the baseline cohort who were genotyped) were used as reference for this analysis. For loci with a genome-wide significant association (p < 5 × 10 -8 ), we tested for heterogeneity using the I 2 statistic  39.
In a sensitivity meta-analysis, we tested whether the studies using the kNN algorithm had similar effects of the genome-wide significant variants for the studies using the kNN-algorithm in comparison to the other studies. We also searched for candidate genes in the loci using publically available databases for differential expression of the SNPs (eQTL database in GTEx)  42  and HaploReg, an online tool that summarizes the ENCODE database for epigenetic markings and proteins binding to DNA  43. Variance explained by common variants and genetic correlations. The variance explained by all SNPs, or SNP-based heritability, was calculated from summary statistics using LD score regression  44. The percentage of variance explained by all SNPs was determined based on meta-analysis results using the LD Hub  45.
Statistics and reproducibility. Software used for the data analysis of this study: EasyQC (www. genepi-regensburg. de/easyqc), FreeSurfer (https://surfer. nmr. mgh. harvard. edu/), GCTA (http://cnsgenomics. com/software/gcta/), GenABEL (http:// www. genabel. org), GTeX (https://gtexportal. org/home/), HaploReg (https://www. encodeproject. org/software/haploreg/), HASE (https://github. com/roshchupkin/ hase), LD-hub (http://ldsc. broadinstitute. org/ldhub/), LD score regression (https:// github. com/bulik/ldsc), mach2qtl (https://www. nitrc. org/projects/mach2qtl/), METAL (http://csg. sph. umich. edu/abecasis/metal/), Perl (https://www. perl. org/), PLINK (https://www. cog-genomics. org/plink2), R (https://www. r-project. org/), SNPTEST (https://mathgen. stats. ox. ac. uk/genetics_software/snptest/snptest.