dataesr/scientific-paragraphs-categorization huggingface dataset:
The main contributions of this article are summarized as follows: (1) Through the improvement of the network proposed by Isola  [17] , we propose a Generative Adversarial Network based on image in-painting to realize the effective estimation of the pixel values at the locations covered by the laser line in the multispectral image; (2) The proposed network can effectively extract the laser line in the multi-spectral image; (3) Through adding certain U-Net-like structures to the generator of GAN, the proposed network can produce stable results; (4) Based on the proposed network and Fan's  [16]  algorithm, we achieve accurate 3D reconstruction using a multispectral image with a laser line. The remaining of this article is organized as follows: Section 2 introduces the related work; Section 3 introduces the network structure, related parameters and training process of the Generative Adversarial Network proposed in this article; Section 4 introduces the rendered image dataset used in this article and the results of the rendered images and the real images; Section 5 concludes this paper.
Training The lack of data makes difficult to train the network with real objects. Inspired by recent advances that use synthetic data for training the deep networks, we also train our network with synthetic images rendered using the ShapeNet dataset. The dataset contains 55 common object categories with about 51,300 unique 3D models. We render the 3D models based on a script on GitHub (https://github. com/panmari/stanford-shapenetrenderer, accessed on 27 November 2017), which can render models to 2D images at dif-  SqueezeNet-like strategy The SqueezeNet-like strategy was used to get more finegrained features. The strategy contains two convolutional layers, and a concatenation layer, the parameters "kernel_size" of the convolutional layer was set to 1 * 1 and 3 * 3, respectively. Feature map scaling strategy The Feature map scaling strategy was used to acquire more stable outputs.
Training The lack of data makes difficult to train the network with real objects. Inspired by recent advances that use synthetic data for training the deep networks, we also train our network with synthetic images rendered using the ShapeNet dataset. The dataset contains 55 common object categories with about 51,300 unique 3D models. We render the 3D models based on a script on GitHub (https://github. com/panmari/stanford-shapenet-renderer, accessed on 27 November 2017), which can render models to 2D images at different angles with Blender. We have improved the script so that it can generate 2D images at different angles of view for the same target illuminated by the red, green and blue light sources and a laser source. The loss function was based on conventional l2 residual minimization and that with l1 residual minimization, and their sum was optimized using the Adam Optimizer  [36].
Dataset 4. 1. 1. Rendered Image Dataset We use the Blender Python API to render a total of 13,977 models of five categories in ShapeNet, such as bus, car, airplane, ship and train, into multispectral images with laser lines, multispectral images without laser lines, and laser images. The settings were as follows: the resolution of the rendered images was set to 256 * 256; the origin of the coordinate system was set to the center of these models; the lamp type was set to "spotlight", and their beam were set to 60 degrees, the initial position were set to be at 0 degrees, 120 degrees and 240-degree, the optical axis points to the origin and can move along the ring as a whole at 15 degrees per time; 501 red spotlights were set to simulating the effect of a laser illumination, while their beam was set to 1 degree, the position moved along a straight line, and the color was set to (1,0,0) or (0,1,0), representing red or green laser, respectively.
-figure supplement 1C). To better characterize the dynamics of RA signaling in cardiomyocytes, we performed time-course and dose titration experiments in cultured cells. First, we treated primary cardiomyocytes isolated from RARECreER T2 ; mTmG hearts with 10 nM RA for different periods of time (8-48 hr) and found that GFP + cells could be detected as early as 28 hr, with peak levels at 48 hr. Next, we treated cardiomyocytes with increasing doses of RA (0. 1-1000 ng/ml) for 48 hr and quantified the number of GFP + cells. Treated cardiomyocytes showed a dose-dependent response to RA (Figure  2I  and Figure  2 -figure supplement 1D). Strikingly, GFP + cells could also be detected with very low doses of RA (0. 1 nM -1 nM) (Figure  2I  and Figure  2 -figure supplement 1D). Taken together, these data demonstrate that cardiomyocytes are highly responsive to exogenous RA treatment at physiologically relevant levels.
(F) Scheme illustrating pattern of ALDH1A2 production and RA activity in the myocardium during mid-late stages of cardiac development. Image created with Biorender software. Epi = epicardium, My = myocardium, End = endocardium. Data information: Scale bars mosaics: 100 µM, Close ups: 40 µM. For all experiments at least three hearts were analyzed and representative hearts shown. ). Moreover, RAKO mice exhibited higher mortality rates (Early death: RAKO 4 of 14 vs. CTL 0 of 11) before analysis at 6 days post MI (Figure  5 -figure supplement 2D, table). Thus, dampening the RA response prior to MI leads to increased cardiomyocyte apoptosis and mortality, suggesting RA signaling plays a protective role in damaged heart muscle. Figure 3 continued A previous study using exogenous RA supplementation in mice subjected to ischaemia/reperfusion determined that the RA pathway reduced apoptosis in damaged hearts by modulating the MAP kinase pathway.
However, more detailed analyses such as FACS analysis with multiple markers or single-cell RNA sequencing would have to be carried out in order to confirm these findings. It would also be interesting to investigate if these same cell-types also produce ALDH1A2 after MI. software. Dotted line represents significance threshold equivalent to p < 0. 05. Several canonical RA targets (Rarb; Cyp26a1; Cyp26b1; red) are significantly upregulated. Genes involved in cardiac repair such as Transglutaminase 2 (Tgm2) (red) and Angiotensin converting enzyme 1 (Ace1) (blue) are also significantly altered. DESeq analysis of genome aligned reads was performed using proprietary Genomatix software. Only the top 500 genes were included in the volcano plot analysis. Blue dots represent downregulated genes. (C) Gene ontology (GO) analysis of biological processes using Gorilla software.
3D reconstructions and analysis were performed using Imaris software. Immunofluorescence, histological analysis, and sirius red staining For IF experiments, tissues were fixed overnight in 4 % paraformaldehyde, progressively dehydrated and embedded in paraffin. 5 µM thick sections were rehydrated, boiled in a pressure cooker for 2 min with Antigen Unmasking Solution (Vector laboratories) and blocked in PBS solution containing 10 % normal donkey serum and 3 % BSA. All antibodies were applied overnight at 4 °C at the concentrations listed in the antibody table (see Supplementary file 1). Secondary antibodies were diluted 1:400 and applied at room temperature for 1 hr. TUNEL stainings were performed as IF experiments with the TMRRed In situ dell death detection kit (Roche). Imaging was performed with a motorized Axio Imager Z1 microscope (Zeiss) coupled with an AxioCam MRm camera (Zeiss), and images were processed with AxioVision LE and ImageJ.
The atomic states, chemical compositions and valence band data were determined by X-ray Photoelectron Spectroscopy (XPS) and performed using a PHI Versaprobe 5000 apparatus with monochromated Al K␣ 1 X-rays (energy of 1486. 6 eV, power of 50 W and X-ray spot diameter of 200 m). Experiments were realised after sputtering of the CTB01, CTB01S and HJ19-01S samples in order to remove the major part of the thin layer of oxide from atmospheric contamination. Sputtering was done with argon ions of 500 eV for 5 min for the thin films (incidence angle of 45 • ). For the heterojunction, the XPS data were collected after each 60 s of etching time. In this condition, the sputtering rate of standard silicon dioxide is around 2 nm/min. Adventitious carbon from atmospheric pollution is used as internal standard for energy calibration (1s level at 284.
8 eV). During measurements, the residual pressure of the analysis chamber was maintained below 10 -7 Pa. Spectra were processed with the Casa software package and the ionisation cross-sections from Landau model were used in order to quantify the semi-empirical relative sensitivity factors. The crystallographic structures of samples was investigated by X-Ray Diffraction (XRD) and performed with a Bruker D8 DISCOVER (CuK␣ radiation source = 1. 5406 Å). The size of nanocrystals was estimated from the Scherrer formula  [17] : D hkl = 0. 89 / B hkl cos Â where is the X-ray wavelength used, B hkl the full width at half maximum and Â the Bragg angle of the studied peak. The Raman spectroscopy (Raman) was performed with a custom-built epi-confocal microscope equipped with a 40× objective lens (0. 6 NA, Nikon).
The cells were finally resuspended in PBS and stored at 4 • C until microscopy imaging. Fixed cells were deposited on thin agarose pads (1% molecular-biologygrade agarose in PBS) molded on glass slide and observed under cover slips with a Zeiss Axio Observer Z1/7 microscope using the ZEN software, version 3. 0. The objective was Plan Apochromat 63x/1. 40 Oil Ph 3 M27. The exposure time was 200 ms for mCherry mPlum HXP (Ex: 587 nm, Em: 610 nm) and 20 ms for phase contrast.
Simulation able to handle such complexity is a very helpful tool to ease the design of optimal NDT/SHM methods and to accurately interpret experimental results. To be fully operational, a simulation tool must not only handle the complexity, but also be both accurate and computationally efficient enough to be used intensively. At CEA, the development of such simulation tools for various NDT/SHM techniques has been the subject of researches for decades for developing models, for implementing and validating them and subject of software developments (the CIVA simulation platform; see  [2]  for modules relative to methods involving GW) to make these tools usable by NDT engineers and technicians. Piezoelectric as well as EMAT transducers are currently modelled in CIVA. For EMAT, the current (commercially available) version of CIVA deals with nonmagnetic materials.
At this stage, the model for EMAT radiation in ferromagnetic media  [3, 4]  is used to predict the various electro-magneto elastic sources (bulk dynamic magnetization and Lorentz forces to be predicted together with dynamic magnetic surface traction and bulk magnetostriction strain). These sources depend on magnetization and magnetostriction curves specific of the material considered. These curves can be either measured or predicted. Here, Daniel's theory  [13]  is used to predict them. These predictions are made within CIVA module dedicated to eddy-current testing simulation, allowing easy computation of the electromagnetic static and dynamic fields involved in the EMAT source model, for arbitrary geometry of coil, arbitrary permanent magnet. From these electromagnetic static and dynamic fields, the bulk elastodynamic source are predicted  [4].
Thus, the study was carried out in 2019 at five different locations around the city (two cultural centers, two sports centers, and a language center), to reproduce similar recruitment conditions in both Spain and France. For each country, an equal number of participants was recruited at higher and lower levels of education (30 in each group). A higher level of education was defined as having a university degree, while a lower level of education indicates participants without a university degree. As demographic characteristics were obtained after data collection, over-recruitment was necessary in each city to ensure this specific distribution of participants. Gender and age balance were ensured by random selection of participants to be excluded when a subgroup (age or gender) was overpopulated.
Questionnaire A two-way ANOVA was performed for each quantitative variable (household composition, scores for belonging to the taste of childhood, and the role of pulses in cultural traditions), including as fixed factors: country (France or Spain), level of education (high or low), and interaction between the two. When significant differences were detected, a multiple paired comparison ad hoc Tukey test was performed. For the qualitative variables in the questionnaires (country, age, gender, and education), Chisquare cell-per-cell tests were performed for country (FR -ES), and education level (high -low). All the analyses used the XLSTAT for Windows software (Addinsoft, France, version 2018-1).
Acknowledgment The authors are grateful for the warm welcome and collaboration at all the places where this study took place, in Dijon and  Girona , especially the director of the  Social Centre -MJC Maladiere :  Dominique Vercherand  and all staff members. We thank  Owolabi Saïd Yessoufou  and  Marc Ferres  for their help with data collection. Carmela Chateau-Smith  is particularly acknowledged for English proofreading.
SNP Filtering Initially, we ran the population module of Stacks v2. 4 requiring a locus to be genotyped at a frequency greater than 80% in at least 8 out of 10 sampling sites, while deleting SNPs with more than 50% of heterozygosity. Then, stringent quality filters were applied to the dataset (see Supplementary Table  1 ) using PlinkQC, the R version of PLINK v1. 9  (Chang et al. , 2015). This procedure consisted of i) discarding individuals outlying missing genotype frequency >25% and those outlying three SD away from the mean rate of heterozygosity ii) dropping SNPs with missingness rate >5% and SNPs with minor allele frequency (MAF) <1%. Following this step, SNP error rate between replicates ranged from 0. 009 (OURS) to 0. 03 (SC) with an average rate of 0. 01. Also, individual relatedness was checked using the KING-robust kinship estimator  (Manichaikul et al.
, 2010) , implemented in the Bigsnpr v1. 3. 0 R package  (Privé et al. , 2018). We applied a KING-relatedness cutoff of 0. 354 to discard duplicate samples and up to a third degree of relationship. Finally, SNP clumping was performed using plink to remove associated SNPs (r 2 > 0. 3) using the MAF as a statistic of importance. At the end of this filtering process, the dataset comprised 11,067 SNPs among 229 individuals, with a genotyping rate of 98%.
Outlier Loci We searched for loci potentially influenced by selection, to build a neutral dataset used to infer population structure and connectivity. The putatively non-neutral (selected) loci were analyzed separately to test for adaptation to local conditions (see Supplementary Material 2). We applied three tests to minimize the number of false positives loci assumed to be under selection. First, we used the F ST method described in  Martins et al. (2016)  and implemented in the LEA R package  (Frichot et al. , 2014; Frichot and François, 2015). This method starts by defining the optimum number of populations with ancestry estimates  (Frichot et al. , 2014). F ST values are then converted to z-scores and significant outliers are detected after controlling for a False Discovery Rate (FDR) of 0.
05. Secondly, we used the Pcadapt v. 4. 3. 1 R package  (Luu et al. , 2017). Pcadapt analyses the population structure by PCA, then it estimates to what extent each SNP is related to the first K principal components to identify outlier loci. Here, outlier tests were performed from the first six principal components, according to the percentage of explained variance for each PC. P-values were then computed by making a Gaussian approximation for each PC and by estimating the standard deviation of the null distribution. Finally, P-values were corrected at 0. 05 FDR. The third approach used here, implemented in Bayescan v. 2. 1, estimates directly the probability that each locus is subject to selection using a Bayesian method  (Foll and Gaggiotti, 2008). The test was run with default settings (5,000 iterations, 10 thinning intervals, 20 pilot runs-5,000 iterations each, 10 prior odds).
Population Genetic Variation Spatial population structure was assessed with putatively neutral SNPs at fine (one site, two depths), local (six sites) as well as regional scales (ten sites). Two complementary approaches were used. First, a principal component analysis (PCA) was performed by singular value decomposition (SVD) of the centered genotype matrix, as implemented in Bigsnpr v. 1. 3. 0 and Bigstatsr v. 1. 2. 3 R packages  (Privé et al. , 2018). The missing genotypes were imputed according to the mean allele frequencies using the function snp_fastImputeSimple before performing the PCA. Second, the function sNMF of the LEA R package  (Frichot et al. , 2014; Frichot and François, 2015)  was used to estimate individual ancestry proportions. SNMF was run with 5,000 iterations and 10 repetitions for K value from 1 to 15.
The best K value was defined using the cross-entropy criterion (see Supplementary Figure  1 ). Genetic differentiation was measured by calculating pairwise F ST values  (Weir and Cockerham, 1984)  between populations using the Genepop v. 4. 7. 5 R package  (Rousset, 2008)  and performing an exact test of genic differentiation  (Raymond and Rousset, 1995)  using the Markov chain method with default parameters, then a combination of all tests across loci (Fisher's method) was performed for each population pair. Genetic diversity within populations was estimated using Genepop by estimating the average observed heterozygosity (H o ), expected heterozygosity (H e ), and F IS  (Weir and Cockerham, 1984). We tested for differences in H e between populations with a pairwise Wilcoxon test adjusted for multiple comparisons (Bonferroni adjustment).
Individual Assignment Analysis Individual assignment tests were used to test for contemporary gene flow and admixture between shallow (ARM20) and deep (ARM40) populations of Armoire, as well as to investigate the potential source population(s) for the colonization of ALT. Assignment tests were conducted using the Assigner v. 0. 5. 7 R package  (Gosselin et al. , 2016)  and the implemented gsi_sim function  (Anderson et al. , 2008). We used the Training, Holdout and Leave-one-out (THL) method to avoid strong grading bias related to the fact that the selection of informative loci and population assignment is performed from the same set of individuals  (Anderson, 2010). Specifically, individuals were distributed into two sets: a training set (including 50% of individuals per site), a holdout-set (including the other half of individuals per site).
These analyses were standardized by considering the minimal sample size (i. e. , 16 individuals). The selection of informative genetic markers was performed from the training dataset, thus identifying 14 sets of neutral SNPs ranked based on decreasing overall F ST values (50, 100, 200, 500, 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, and all SNPs). Finally, the individual assignment was performed using separately the 14 sets of SNPs for the holdout set of individuals, and confidence intervals were obtained by bootstrapping individuals (i. e. , 16 individuals per population were randomly chosen 10 times).
ICHTHYOP was parameterized to constraint dispersal of non-buoyant particles around the seafloor. Specifically, 1,000 particles were released each day from each of six locations within a radius of 400 m at the depth (±2 m) of the study sites. The computational time step (dt) was set to 400 s and particle position was recorded in the NetCDF output files every 1. 33 h. Pairwise oceanographic connectivity probabilities were defined as P ij = N i-j /N i where N i is the total number of released particles from site i and N i-j the total number of released particles from site i arriving in site j after 30 days of transport time; these probabilities were computed in MATLAB. To take into account the life span of the species, particles recorded at more than 100 m depth were not considered in the computation of P ij.
, 2008) , which turned out to be extremely useful for modeling asymmetric processes, such as ocean currents, and testing their effects on genetic structure (e. g. ,  Benestan et al. , 2016; Xuereb et al. , 2018a; Coscia et al. , 2020). For this purpose, we transformed the mean oceanographic connectivity matrix (Figure  7 ) from Lagrangian simulations into a nodes-to-edges matrix, which records the presence/absence of connectivity link between nodes. Specifically, fifteen connectivity links (i. e. , edges) were reported among the six study sites considered (i. e. , nodes). Edges were weighted by the probabilities of dispersal, and this weighted matrix was then used as input data to compute AEM predictors with the adespatial v. 0. 3. 8 R package. Seawater temperature predictors were computed by extracting the temperature variables of the MARS3D model.
These analyses were carried out using the Vegan v. 2. 5. 6 R package  (Oksanen et al. , 2017)  and repeated twice with the set of 10,755 neutral and 12 outlier SNPs. We considered all independent predictors, with Variance Inflation Factor (VIF) below 10 to avoid multicollinearity effects (pairwise correlations of the initial dataset of 11 environmental predictors were reported in Supplementary Table  2 ). Following the VIF threshold, Min-T and Var-T predictors were excluded in partial and global distance based RDA while the density predictor was only excluded in the whole model. Then, we applied stepwise forward selection to select the best predictors using the ordiR2step R function. The ordiR2step performs model choice solely on the adjusted coefficient of determination (R 2 adj ) and selects the model with the highest one.
Bioinformatic and Genotyping The From the set of 11,067 SNPs, BayeScan detected 39 outlier SNPs, while pcadapt and sNMF identified 895 and 482 outliers, respectively. Twelve outliers were shared between the three outlier tests (Figure  3 ). Furthermore, 312 SNPs detected by at least two of the three methods were excluded from the set of 11,067 SNPs and those remaining (10,755 SNPs) constitute the neutral dataset.
ACKNOWLEDGMENTS We would like to thank the diving unit of  OSU Institut Pythéas , the divers of the  National Institute for Ocean Science (IFREMER) , and the divers of  GIS Posidonie  for sampling Ericaria zosteroides populations, the  Biogenouest genomics core facility  ( Genomer Plateforme Génomique  at the  Station Biologique de Roscoff ) for their technical support, the staff of the  "Cluster de calcul intensif HPC" Platform of OSU Institut Pythéas (Aix-Marseille University, INSU-CNRS)  for providing the computing facilities, and finally  Laura Benestan  for her invaluable support in producing Asymmetric Eigenvector Maps (AEM). Finally, we would like to thank  Michael Paul  for English language proofreading.
FUNDING This project was funded by the  MARFOR Biodiversa/0004/2015  project (http://marfor. eu/),and by the  Agence Nationale de la Recherche , Grant/Award No. :  ANR-18-CE32-0001  ( Clonix2D ). LR also received support from the  GDR Génomique Environnementale  ( GDR3692 ). The project leading to this publication has received funding from the  European FEDER Fund  under project  1166-39417. This work was also funded by a  Ph. D. Fellowship from Région PACA  and the  Calanques National Park.
The secondary endpoints were clinical 5-year biopsy-proved acute rejection and GF. Acute rejection episodes were recorded in medical files if the pathological analysis detected T-cell-mediated rejection (TCMR), borderline (BL), or antibody-mediated rejection (ABMR). To increase the power of our analyses and to take into account patient death, which could be impacted by NA, GF was defined as death, return to dialysis, or retransplantation, whichever came first. These secondary endpoints were retrospectively collected by the same physician (MPR) who was blinded to the adherence status at the time of data collection.