dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other hand, comparing the performance of the U-Net and the cycleGAN model, we observed that the U-Net has better performance. Although the cycleGAN model has two discriminators, without the supervision of paired input-output data it was not able to learn robustly and segmentation performance got the lowest values for the evaluation metrics compared to the other two models. Figure  5  shows visualization results of these networks for each modality. Table  1  shows the quantitative results in terms of mean and standard deviation of the evaluation metrics for each created dataset (see Section 2. 2) and each modality. Based on our quantitative results, for the ADC modality, the best results were obtained when the model was trained on the dataset combining raw and GNA images. Training the networks with the combination of SP, MM and raw images could also improve the learning performance for this modality compared to using only the raw data.
To determine statistical significance of our results a paired t-test was used. The cGAN network achieved p-value < 0. 0001 using the average DSC and average 95% HD values compared to the cyceGAN network in all modalities. To demonstrate the clinical usability of these models and evaluate their generalisability, we tested the models with the public dataset PROMISE12. This dataset contains images captured with different scanners and protocols. This was not used for model training, since its heterogeneity was desired for model testing. The experimental results presented in Table  2  show that the trained networks were able to effectively perform automatic segmentation of prostate tissue with DSC = 0. 75, 0. 72 and 0. 70 using cGAN, U-Net and cycleGAN, respectively which were comparable to the results gained on our clinical study dataset with DSC = 0.
78, 0. 76 and 0. 73 using cGAN, U-Net and cycleGAN, respectively. Table  2  also compares the evaluation results of different prostate tissue segmentation methods, developed based on the recent deep learning approaches. Our trained models performed comparatively although they were trained on limited numbers of data which were artificially augmented by our proposed augmentation approaches. The use of a public dataset for external validation with such a limited loss in accuracy between the internal and external test sets is a significant advantage of our proposed techniques.
[4] conditional generative adversarial model Norfolk&Norwich University Hospital 6. 58 ± 3. 00 0. 73 ± 0. 16 U-Net model used in this work U-Net model our private dataset (Section 2. 1) 6. 11 ± 1. 49 0. 76 ± 0. 10 cycleGAN model used in this work unpaired image-to-image translation(cycleGAN) our private dataset (Section 2. 1) 6. 63 ± 2. 94 0. 73 ± 0. 20 cGAN model used in this work conditional GAN model our private dataset (Section 2. 1) 5. 90 ± 3. 59 0. 789 ± 0. 12 U-Net model used in this work U-Net model MICCAI PROMISE12 10. 07 ± 0. 95 pix 0. 72 ± 0. 03 cycleGAN model used in this work unpaired image-to-image translation (cycleGAN) MICCAI PROMISE12 11. 02 ± 2. 65 pix 0. 70 ± 3. 41 cGAN model used in this work conditional GAN model MICCAI PROMISE12 9. 30 ± 2. 6 pix 0. 757 ± 2. 18.
Material and methods Ethics statement. All our studies were approved by the local Ethical Committee (Conseil d'evaluation ethique pour les recherches en sante-CERES n˚201659) and each participant received a description of the study and signed an informed consent before starting the experiment. All scripts and raw data are available in the OSF project (https://osf. io/x8p4t/). 2. 1. 1 Participants. Participants recruited via Amazon Mechanical Turk (MTurk, http:// www. mturk. com), which offers a large diversity of participants  [16, 17]. The number of participants was fixed a priori based on Chevallier et al. [18] , who reported significant correlational effects of social motivation in samples of 26 participants tested in the laboratory. Knowing that online experiments may be more susceptible to noise due to less controlled settings, we decided to test 60 American participants (28 women), aged 23 to 65 years old (34.
The different simulations are represented by different shades of blue and red. https://doi. org/10. 1371/journal. pone. 0230011. g001 years). Each participant received 2$ for completing the task (which corresponds to the prorated average 6$/hour compensation commonly used on MTurk). 2. 1. 2. Experimental procedure. 2. 1. 2. 1. Rating task. The experiment was programmed on Qualtrics (http://www. qualtrics. com) and lasted approximately 20 minutes. Participants were asked to rate 40 neutral faces (20 woman faces) extracted from the Karolinska database  [19]  on trustworthiness, dominance and threat. Presenting both male and female faces was used as a mean of having larger variations of perceived dominance and perceived trustworthiness as gender is associated with differences in the evaluations on these two traits  [10].
The faces were presented simultaneously with a word indicating which trait participants had to rate the face on, e. g. , dominance or trustworthiness. Participants had to give their ratings as soon as the face appeared on the screen. In order to ensure that participants had processed the face before placing their cursor on the scale to give their response, reaction times inferior to 200 ms were discarded (percentage of excluded trials: 0. 58%; mean reaction time before data cleaning: 4. 62 ± 0. 20 s). For each participant, only faces with evaluations of trustworthiness, dominance and threat given with a reaction time superior to 200 ms were analysed. No participant was excluded. Analyses were performed on at least 75% of the faces for all the participants (N = 60). 2. 1. 4. Analyses. To investigate the effect of social motivation on threat evaluations, we ran a mixed linear regression on threat ratings, taking social motivation, ratings of dominance and ratings of trustworthiness as predictors and participants' ID as a random factor.
Data cleaning and analyses. As in Study 1, trials with reaction times inferior to 200 ms were discarded for reflecting an incomplete processing of the face (percentage of excluded trials: 0. 08%; mean reaction time before data cleaning: 3. 76 ± 0. 23 s). Analyses were performed on at least 95% of the faces for all the participants (N = 60). The exact same analyses as in the previous study were conducted to measure the influence of the motivation for social bonding on social preferences. As in Study 1, ratings were centred around the middle of the scale and motivation for social bonding scores were reverse-coded and transformed into z-scores.
1 coming from 20 maximally distinct source identities were randomly selected from the face database available on Todorov's website (http://tlab. princeton. edu). Four variations of each identity were used (extremely dominant, extremely submissive, extremely trustworthy and extremely untrustworthy; Oosterhof & Todorov,  [10] ), which enabled us to control for participants' ability to recognize faces from these four categories. These avatars were automatically generated by Oosterhof and Todorov  [10]  based on the evaluation of the faces from Karolinska database by more than 300 participants. Importantly, multiple studies from different research teams have shown that these avatars can reliably be used to evoke different traits. As the sample size for this study was divided by two compared to Studies 1 and 2, the number of faces was doubled in this experiment to obtain the same total number of trials as in the previous studies.
Procedure. The experiment was programmed on Qualtrics. 16 avatar faces similar to those presented in Study 4 (generated via FaceGen 3. 1 software and controlled for both trustworthiness and dominance; Todorov et al. ,  [25] ) were used in this experiment. The 16 faces corresponded to every possible combination of dominance and trustworthiness in a range of -3 to +3 points with an increment of 2 points (i. e. -3, -1, +1 and +3)  [10]. The faces were regularly spaced on both the dominance and trustworthiness dimensions. In each presented pair, the faces were 2 to 6 points different from each other on at least one dimension. This resulted in 120 pairs of faces. Each trial began with a central fixation cross presented for 300 ms, then the two faces were presented simultaneously. Participants had to select their preferred face and had up to 2 seconds to answer by pressing "e" for the face on the left and "p" for the face on the right (Fig  2A ).
All authors are supported by the  Institut d'E ´tudes Cognitives (FrontCog ,  ANR-17-EURE-0017 ,  ANR-10-LABX-0087 IEC  and  ANR-10-IDEX-0001-02 PSL ) https://anr. fr/The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Methods Dataset. We compiled a dataset of extinct and extant crocodylomorph endosseous labyrinths, using computed-tomography scanning. Our extinct (fossil) sample (18 specimens) includes all thalattosuchians with wellpreserved skulls that were accessible to us (13 specimens, including 4 teleosauroids, the basal metriorhynchoid Pelagosaurus typus, and 8 metriorhynchids), two basal "sphenosuchian"-grade taxa, Protosuchus haughtoni, Eopneumatosuchus colberti, and the neosuchian Shamosuchus djadochtaensis. These taxa span much of the evolutionary history of crocodylomorphs, from the Late Triassic to Early Cretaceous, and include a series of fossil outgroups that polarize the primitive conditions for Thalattosuchia, our main clade of interest (Fig. 2 ). To this dataset, we added 14 extant crocodylians for comparative purposes, including members of the 3 major extant lineages (Alligatoroidea, Crocodyloidea, and Gavialoidea) (SI Appendix, Table  S1 ).
Our dataset includes only adult and subadult specimens. For specimen details, see SI Appendix. Ecological Categories. Our dataset includes species belonging to three ecomorphological or habitat groups: terrestrial, semiaquatic, and pelagic (i. e. , fully aquatic). All extant crocodylians are semiaquatic, as extensive observational data show that they move between the land and nearshore aquatic habitats  (36). The habitats of extinct species were assigned based on a combination of osteological and geological (environmental) data (SI Appendix, Table  S2 ). Data Assembly. Skulls were CT scanned at various facilities, so the scanners and scanning parameters vary (SI Appendix, Table  S1 ). Some scans were sourced from the online databases Morphosource (https://www. morphosource. org/) and Digimorph (http://digimorph.
org/). Bony labyrinths were segmented from the scans using Materialise Mimics 19. 0 and 20. 0, using the livewire and lasso tools. We segmented right labyrinths and retained them for the numerical analyses (see below); if these were not preserved, then the left labyrinth was segmented and mirrored. A recent study found no significant bilateral variation in the inner ears of wild turkeys  (37) , justifying this procedure. We also show that left-right asymmetry is minimal in extant crocodylians (SI Appendix, Figs. S9  and S10 ). Geometric Morphometric Analysis. For each 3D rendered endosseous labyrinth model, we placed two series of semilandmarks along each of the semicircular canals, one on the internal surface and one on the external surface (SI Appendix, Fig. S1 ), using the IDAV Landmark software  (38).
The landmarks were digitalized using the digit. curves() function in the geomorph 3. 1. 2 package  (39)  in RStudio  (40)  to evenly spaced semilandmarks, 11 on the internal surface and 12 on the external surface. Landmarks on the internal surface of the canals were treated as closed structures, and those on the external surface as open structures. Subsequently, we applied Procrustes superimposition to minimize the effects of size and orientation. Multivariate Analyses. We subjected the Procrustes-corrected geometric morphometric landmark dataset to principal component analysis in geomorph 3. 1. 2, which assimilates data from all landmarks and reduces them to a set of PC scores that summarize the labyrinth shape of each species, and allow the species to be plotted in a morphospace. We used PERMANOVA to test whether different habitat groups (terrestrial, semiaquatic, pelagic) are significantly separated from each other in the PCA morphospace using the pairwiseAdonis() function in vegan 2.
5-3  (41). We calculated the mean shape for each habitat group in morphospace, along with the extreme shapes on the ends of each PC axis. We also performed a canonical variate analysis in Morpho 2. 6  (42)  to test the ability of the PC scores to assign individuals to known ecological categories. Phylogenetic Comparative Methods. For the following phylogenetic methods, we utilized a consensus phylogeny of Crocodylomorpha based on the latest iteration of the Crocodylomorph SuperMatrix Project  (43) (44) (45). The relationships of the species in our dataset are generally well resolved, but a few taxa are labile in recent phylogenetic analyses. Thus, as a sensitivity analysis, we repeated phylogenetic methods on a set of alternative phylogenies. The results were generally identical to the results gleaned from our consensus phylogeny, and we report the details in the SI Appendix.
In all phylogenetic comparative analyses, the trees were time scaled using strap 1. 4  (46)  and zero-length branches were extended using the "equal" method  (47). We tested for phylogenetic signal in the PC scores with Pagel's lambda (λ), using phytools 0. 6 in R  (48). A λ value close to 1. 0 indicates strong phylogenetic signal, with correlation between species equal to the Brownian motion expectation that phylogeny alone can explain trait changes  (49) , whereas values close to zero indicate no such phylogenetic correlation between species. The λ values are associated with a P value, denoting significance or nonsignificance. We tested the correlations of labyrinth shape, labyrinth size, and habitat, using phylogenetic generalized least square regression in the R package nlme 3. 1  (50) , which accounts for the nonindependence of species due to phylogenetic relationships  (51, 52).
5 ). Optimizations were performed using maximum likelihood using the fastAnc() function in phytools 0. 6 (SI Appendix, Figs. S2-S4 ). We fitted five standard models of trait evolution to the PC scores on the phylogeny: Brownian motion, Ornstein-Uhlenbeck (OU), early burst, Brownian motion with directional trend, and Lambda  (Pagel). The fit of each model was assessed with maximum likelihood and the best supported model was determined by the lowest AICc score, using the R package geiger 2. 0. 6. 2  (53). Further information on the mathematical properties of each model can be found in refs. 54-59. We recognize that interpretation of OU models can be complex and sometimes mimic other models  (60) , but include them here for completeness. Testing whether certain portions of the phylogeny have variable rates of labyrinth shape evolution is challenging, because PC scores and continuous data present problems compared to discrete characters (61).
ACKNOWLEDGMENTS. We thank two anonymous reviewers for helpful suggestions;  Olivier Lambert  and  Travis Park  for discussion of cetaceans;  Graeme Lloyd  and  Manabu Sakamoto  for methodological advice; and  Davide Foffa  for statistical support. We are grateful to  Susannah Maidment  ( Natural History Museum, London ),  Orestis Katsamenis  ( University of Southampton ) and  Philipp Koch  ( Mindener Museum, Germany ) for access to their specimens. This project is supported by a  Leverhulme Trust Research Project  grant ( RPG-2017-167 ) to PI S. L. B. , which funds J. A. S. and  M. T. Y. J. A. S. was supported by the  Carl Gans Fund  to attend the ICVM-19 meeting. J. M. N. is funded by a  Leverhulme Trust   Early Career Fellowship  ( ECF-2017-360 ). Y. H. was supported by a  Humboldt Research Fellowship  from the  Alexander von Humboldt Foundation  and the  ANPCyT  (PICTs  2016-0267 ,  2016-1039 ).
exilis accessions originating from Guinea, Mali, Benin, Togo, Burkina Faso, Ghana, and Niger and 17 accessions of the proposed wild tetraploid fonio progenitor D. longiflora  21  from Cameroon, Nigeria, Guinea, Chad, Sudan, Kenya, Gabon, and Congo for whole-genome re-sequencing (Supplementary Table  6 ). The selection was done from a collection of 641 georeferenced D. exilis accessions  36  with the aim of maximizing diversity based on bioclimatic data and geographic origin. We obtained short-read sequences with an average of 45-fold coverage for D. exilis (range 36-61-fold) and 20-fold coverage for D. longiflora (range 10-28-fold) (Supplementary Data 4). The average mapping rates of the raw reads to the CM05836 reference assembly were 85% and 68% for D. exilis and D. longiflora, respectively, with most accessions showing a mapping rate of >80% (Supplementary Data 5).
Methods Plant material. The plant material used in this study comprised a collection of 641 D. exilis accessions maintained at the French National Research Institute for Development (IRD), Montpellier, France  36, 49. The collection was established from 1977 to 1988. For genome re-sequencing, a panel of 166 accessions (Supplementary Data 6) was selected based on geographic and bioclimatic data. One plant per accession was chosen for DNA extraction and sequencing. Inflorescences of sequenced D. exilis plants were covered with bags to prevent outcrossing. Grains of sequenced plants were collected and kept for further analyses. In addition, 17 accessions of D. longiflora were collected from specimens stored at the National Museum of Natural History of Paris (Paris, France), IFAN Ch. A. Diop (Dakar, Senegal), National Herbarium of The Netherlands (Leiden, Netherlands), and Cirad (Montpellier, France) (Supplementary Table  6 ).
/ID: 10262) from plants grown in a greenhouse with a dark treatment applied during 48 h prior to collecting leaf tissue. Labeling and staining of the HMW DNA were performed according to the Bionano Prep Direct Label and Stain (DLS) protocol (30206-Bionano Genomics) and then loaded on one Saphyr chip. The optical map was generated using the Bionano Genomics Saphyr System according the Saphyr System User Guide (30247-Bionano Genomics). A total of 1114. 6 Gb of data were generated, but only 210. 6 Gb of data corresponding to molecules with a size larger than 150 kb were retained. Hybrid scaffolding between the WGA and the optical maps was done with the hybridScaffold pipeline (with the Bionano Access v1. 4 default parameters). In total, 39 hybrid scaffolds ranging from 550 kb to 40. 9 Mb (total length 657.
Briefly, the initial step using Illumina sequencing data (pre-processing of paired-end and mate-pair reads, unitig assembly, scaffolding and gap closing) was done according the instructions of the TRITEX pipeline [https://tritexassembly. bitbucket. io/]. Integration of 10× and Hi-C reads was done differently. For the 10× scaffolding, we used tigmint v1. 1. 2  52 to detect and cut sequences at positions with few spanning molecules, arks v1. 0. 3  53  to generate graphs of scaffolds with connection evidence, and LINKS  54  for a second step of scaffolding. For the Hi-C data, we used BWA  55  to map the reads against the previous scaffolds and juicer tools v1. 5  56  for the superscaffolding. Preparation of FISH probes and cytogenetic analyses. Libraries of 45 bp long oligomers specific for each fonio pseudomolecule were designed using the Chorus software v1.
1  26  [https://github. com/forrestzhang/Chorus] with the following criteria: -p CGTGGTCGCGTCTCA -l 45 -homology 75 -d 10. The number of oligomers per pseudomolecule was adjusted to ensure uniform fluorescent signals along the entire chromosomes after fluorescence in situ hybridization (FISH). In total, 310,484 oligomers were selected (between 12,647 and 20,000 oligomers per library) and were synthesized by Arbor Biosciences (Ann Arbor, MI, USA). To prepare chromosome painting probes for FISH, the oligomer libraries were labeled directly using 6-FAM or CY3, or indirectly using digoxigenin or biotin. A tandem repeat CL10 with a 314 bp long repetitive unit (155 bp long subunit) was identified after the analysis of fonio DNA repeats with the RepeatExplorer software  57. A 20 bp oligomer was designed based on the CL10 sequence using the Primer3 software  58  labeled by CY3 and used for FISH.
jar [https://github. com/lindenb/jvarkit]. A principal component analysis (PCA) was performed for each fl-LTR-RT family using the R packages vcfR v. 1. 8. 0 66 and adegenet v2. 1. 1  67. Results were visualized with ggplot2 v3. 3. 2  68. The approximate insertion dates of the fl-LTR-RTs were calculated using the evolutionary distance between two LTR sequences with the formula T = K/2μ, where K is the divergence rate approximated by percent identity and μ is the neutral mutation rate. The mutation rate used was μ = 1. 3 × 10 -8 mutations per bp per year  69. Gene annotation. A combination of homology-based and de novo approaches was used for repeat annotation using the RepeatModeler software [http://www. repeatmasker. org/RepeatModeler/] and the RepeatExplorer software  57. The results of RepeatModeler, RepeatExplorer and LTR_retriever were merged into a comprehensive de novo repeat library using USEARCH v.
2. 2. 0 81 to search for the accurately spliced alignments. A de novo prediction was performed using ab initio softwares GeneMark-ES v. 3. 54  82  , SNAP v. 2006-07-28  83  , and Augustus v. 2. 5. 5  84. Three successive iterations of the pipeline MAKER were performed in order to improve the inference of the gene models and to integrate the final consensus genes. Finally, the putative gene functions were assigned using the UniProt/ SwissProt database (Release 2019_08 -[https://www. uniprot. org/]). Synteny and comparative genome analysis. To identify syntenic relationships, BLASTP (E-value 1 × 10 -5 ) was first used to perform all-versus-all protein comparisons between CM05836 and S. italica 28 , Panicum miliaceum  35  , Panicum hallii  85  , S. bicolor 77 , Z. mays  78  , O. sativa 79 , B.
distachyon 86 , H. vulgare 87 , T. aestivum  31  and A. tauschii  88. Then, MCScanX 89 (-m 25; -s 10) was used for pairwise synteny block detection. For all the comparisons, fonio sub-genomes A and B were analyzed independently and only 1:1 relationships were retained (1:2 relationships for tetraploid broomcorn millet and the ancient tetraploid maize, where individual sub-genomes are not clearly phased). To estimate the divergence time, we calculated the synonymous substitution rates (Ks) for each homoeologous and orthologous pair. Briefly, nucleotide sequences were aligned with clustalW  90  and the Ks were calculated using the CODEML program in PAML  91. Then, time of divergence events was calculated using T = Ks/2λ, based on the clock-(λ) estimated for grasses of 6. 5 × 10 -9 92. We used the maximum likelihood for gene-order analysis (MLGO) tool  93  to reconstruct the hypothetical ancestral genome of the Paniceae tribe from syntenic-block data of D.
Briefly, 1. 0 μg DNA per sample was used as input material for the DNA sample preparations. Sequencing libraries were generated using the NEBNext® Ultra II DNA Library Prep Kit following manufacturer's instructions. Libraries were sequenced using an Illumina NovaSeq 6000 system. Mapping of re-sequencing data and variant calling. For quality control of each sample, raw sequence reads were analyzed with the fastqQC tool-v0. 11. 7 and lowquality reads were filtered with trimmomatic-v0. 38  73 using the following criteria: LEADING:20; TRAILING:20; SLIDINGWINDOW:5:20 and MINLEN:50. The filtered paired-end reads were then aligned for each sample individually against the CM05836 reference assembly using BWA-MEM (v0. 7. 17-r1188)  55  followed by sorting and indexing using samtools (v1. 6)  96. Alignment summary, base quality score and insert size metrics were collected and duplicated reads were marked and read groups were assigned using the Picard tools [http://broadinstitute.