dataesr/scientific-paragraphs-categorization huggingface dataset:
The 10 most discriminant preferred 5mers in haloarchaea were GC-rich, as expected (Table  1 , Additional file 4). To identify discriminant 5-mers according to the growth temperature, we removed all Halobacteria representatives from the dataset and classified the remaining elements into two categories: elements with growth temperatures below 80 °C (weak mesophiles to extreme thermophiles) and those with growth temperatures above 80 °C (hyperthermophiles to extreme hyperthermophiles). For archaeal cells, hyperthermophiles and nonhyperthermophiles separated quite well based on PCA and PLS-DA (Additional files 12 and 13). The 10 most discriminant 5-mers identified by PLS-DA all had significantly different frequencies between the two groups (Mann-Whitney-Wilcoxon test, p < 0. 01, Additional file 14).
The signal for hyperthermophily was much weaker overall than that for halophily. In addition, most hyperthermophiles in our dataset were from the orders Desulfurococcales, Thermoproteales and Thermococcales. The few others (e. g. , some Sulfolobales and Methanococcales members) tended to be located within the lower-temperature group, as assessed by PCA. It is therefore not clear whether the identified discriminant 5-mers constitute a general signature for hyperthermophilic archaea.
Codon frequencies influence 3-mer and 5-mer profile distributions It has been previously shown that amino acid usage and codon frequencies vary according to environmental conditions, particularly for archaea and extreme environments  [29, 35, 40, 41]. Since the proportion of coding regions is high in archaeal genomes, it is likely that their 5-mer composition is somehow correlated with the codon frequencies. To evaluate this hypothesis, we focused only on the genomes for which the positions of coding regions were available in public databases, namely 238 out of 239 archaea and 288 out of 345 archaeal viruses and plasmids, in our dataset (Additional file 2). We first compared, for halophiles and hyperthermophiles, the 10 most discriminant 3-mers of the wholegenome sequences to their 10 most discriminant codons (Table  2 ).
Megaplasmids and other mobile elements from Halobacteria have 5-mer profiles distinct from those of Halobacteria cells The class Halobacteria comprises exclusively halophilic archaea that thrive in high-salt environments. We focused specifically on the sequenced mobile elements of Halobacteria members, which are numerous and diverse  [25, 26, 48, 49]. Our dataset comprised 53 cellular Halobacteria genomes, as well as 118 plasmids and 36 viruses of hosts from the orders Halobacteriales, Haloferacales, and Natrialbales (Additional file  19). A particularity of Halobacteria is the abundance of megaplasmids, considered here as plasmids longer than 150 kb (51 represented in our dataset), and of large plasmids, with sizes ranging from 100 to 150 kb (23 represented in our dataset). The 44 other plasmids had sizes ranging from 1.
1 kb to 96 kb. There is currently a scientific debate on the nature of megaplasmids. Indeed, some of them encode essential genes and could hypothetically be currently evolving into chromosomes  [50]. In our dataset, 5 distinct elements were classified as second chromosomes according to public databases. Associated with the Haloarcula or Halorubrum genus, these elements had sizes ranging from 288 kb to 526 kb. Using PERMANOVA, it appeared again that the genomic GC content and the taxonomic family together explained an important proportion of the 5-mer profile dissimilarity variance of extrachromosomal elements, namely, 55. 52% (Additional file 20, D 5_mobile_halo ~GC%*Family). By contrast, the taxonomy of the host explained only a very limited proportion of the variance, 5. 28%, consistent with the loss of phylogenetic signal from the hosts within the class Halobacteria (Additional file 20, D 5_mobile_halo ~Host order*Host genus).
This suggests that the 5-mer composition of newly transferred mobile elements must evolve rapidly, so only recent transfers can be detected by this approach. We then considered more closely the 13 pNOB8-like Sulfolobales conjugative plasmids because in a previous version of the dataset, two pNOB8-like plasmids, namely, pMGB1 and pTC, were located close to Metallosphaera genomes, far from the main pNOB8-like cluster (Additional File 25). This suggested that pTC and pMGB1 could replicate in Metallosphaera archaea, in addition to Sulfolobus. Interestingly, we identified a remnant plasmid very similar to pMGB1 in the genome of Metallosphaera sedula (Fig. 6b ), consistent with this hypothesis. We named this new integrated conjugative plasmid ICEmse, for "Integrative Conjugative Element of M. sedula", and we included it in the dataset.
ICEmse was consistently located in the same cluster as the pTC, pMGB1 and Metallosphaera genomes in the previous dataset version (Additional File 26). In our latest dataset version, the trends were less clear, since Metallosphaera formed a fully separate cluster. Moreover, only pTC grouped with ICEmse (Fig. 5a ) and was detected as an outlier. By contrast, pMGB1 was located in the main pNOB8-like plasmid cluster, but was the outermost element. The PCA result was in good agreement with the host transfer scenario, since pTC, pMGB1 and ICEmse were located roughly at mid-distance between Sulfolobus and Metallosphaera cells (Fig. 6c ). Finally, consistent with the high GC content of Metallosphaera genomes, the pMGB1, pTC and ICEmse genomic GC contents were 39. 6, 41. 4 and 41. 5%, respectively, compared to only 36.
Discussion The influence of their phylogenetic position on the 5mer composition of archaeal cell genomes is clearly visible in our dataset, consistent with the genome-wide importance of short k-mers, which could play a role in speciation and be critical to recombination (reviewed and defended in  [2] ). However, the global topology that we obtained by hierarchical clustering was not fully consistent with the phylogeny of archaea, as detailed in the results section. It could be interesting to evaluate whether more sophisticated methods  [16] [17] [18]  and the use of various k-mer sizes would enable us to obtain a global topology more consistent with the phylogeny of archaea. Whether it could be achieved is, however, uncertain. The fact that we could detect recent HGTs but that several ancient evolutionary connections  [54, 57]  (See figure on previous page.
Their surface is typically enriched in acidic  [42]  and negatively charged residues  [43] , while their core has a moderate hydrophobicity  [43]. Regarding the signature for hyperthermophily, many differences in the methods and datasets could explain the imperfect agreement with previous studies  [44, 45]. Primarily, our information on amino acids is indirect, based on absolute codon frequency analysis, while most cited studies directly focused on amino acid composition. An additional explanation could be that several previous analyses included both archaea and bacteria, whereas we focused exclusively on archaea, mainly on Desulfurococcales, Thermoproteales and Thermococcales. In addition, our dataset includes more sequences, and finally, the statistical methods employed are slightly different.
In particular, Lambros et al. [60]  considered the optimal growth temperature as a quantitative variable, pointing out that most changes in response to growth temperature occur below 60 °C. We therefore may have missed some of the compositional changes that start to occur at lower temperatures. It is, however, interesting that discriminant 5-mers could be identified from our diverse dataset and when considering a high temperature threshold to partition the dataset into two categories. We observed that mobile elements of archaea harbor some specificity in their 5-mer composition compared to their hosts, with two major types of situations. The first corresponds to major compositional differences between the mobile elements and their hosts. Such mobile elements are outliers and do not represent the most frequent cases.
Presentation of the dataset and of the approach Basic information about the genomes included in the dataset is available in Additional file 2, such as the taxonomy, length and GC content of each element. Additional file 4 provides a synthetic view of GC% values across the dataset, according to the taxonomic order of the host and to the type of element; Additional file 27 shows the GC% values according to the Niche and type of element; finally, an analysis of variance (ANOVA) of these GC% values is presented in Additional file 28. We selected 11 taxonomic groups (at the order level) of the domain Archaea (Additional files 19 and 29) for which a significant number of extrachromosomal element sequences were available (plasmids or viruses). For these 11 taxonomic orders, we gathered a total of 589 whole genome sequences of cells, plasmids, viruses and proviruses.
The dataset covered 3 and 8 orders of the phyla Crenarchaeota and Euryarchaeota, respectively. It comprised exclusively halophiles, acidothermophiles, hyperthermophiles and methanogens. For each genome, we established a profile consisting of its 5-mer absolute frequencies. To select the k-mer length, a compromise needed to be established: longer k-mers are more informative; however, excessively long k-mers result in data scarcity due to low average counts, leading to artifacts during subsequent statistical analyses. For plasmids viruses, k-mer length of 5 was selected as a good compromise. Indeed, their average genome length in the dataset was 89,814 bases; since there are 4 k distinct possible k-mers, the average counts were 88 per 5-mer (89,814 divided by 4 5 ), which we considered sufficiently representative, and slightly more specific than tetramers.
For cells, although they have a much higher average genome length, we also used 5-mers to compare their profiles with those of extrachromosomal elements. The obtained 5-mer frequency profiles included 1024 proportions (4 5 ) and constituted a highly multidimensional dataset. To gain insight into these complex data, the landscape of these profiles across the dataset was explored with four methods: hierarchical clustering, PCA, PERMANOVA and PLS-DA. PCA aims to project highly multidimensional data on a set of orthogonal axes to visualise them easily while preserving their variance as best possible. PERMANOVA is a generalized form of ANOVA used to analyze the variance of multidimensional values, here the 5-mer profile distance matrix, and relate them to potential structuring factors. Finally, PLS-DA was used to identify the most discriminant k-mers between several categories of genomes, such as genomes from halophiles, versus nonhalophiles.
Genome sequences We collected 534 publicly available whole genome sequences of cells, plasmids, viruses and proviruses (Additional file 2) from the NCBI genome database. We performed a final update on the 7th of August 2018. In addition, we retrieved 28 provirus sequences directly from cellular genome sequences based on literature information  [53, 68, 69]. Finally, we included 26 magrovirus sequences  [70]  available on a specific website (https://github. com/BejaLab/Magrovirus/tree/master/ Supp_files) and the assembly of a Marine Group II archaeon (GCA_003324605). When the mobile elements were not classified into well-defined families, we categorized them according to the taxonomy of their host (e. g. Halobacteriales megaplasmid).
Establishment of profiles based on the sequence 5-mer composition Two types of profiles were established for each sequence based on its 5-mer composition, as described in more detail below. The profiles of the different genomes were then combined across the dataset to obtain two distinct matrices, one for each type of profile. The first type of profile was based on the 5-mer frequencies of the whole genome sequences. The 5-mer counts were calculated with Jellyfish 2. 2. 6 on the INRAE-MIGALE cluster (URL https://migale. inrae. fr/). The obtained count data were imported into R  [71]  (version 3. 4. 2) and transformed into a frequency matrix to obtain normalized data: for each genome, the sum of the 5-mer frequencies was equal to 1. The second type of profile relied exclusively on the coding regions; it reflected the exceptionality of the different 5-mers in the coding regions after correcting for differences in codon composition in the studied genome.
Statistical analyses of the profiles based on 5-mer composition All statistical analyses were performed using R (version 3. 4. 2). PCA were performed with the dudi. pca function of the ade4 package  [73] , on scaled and centered data. We performed PLS-DA analyses with the caret package  [74] , using a 10-times repeated 10-fold cross-validation and the "accuracy" metrics to select the number of components, again on centered and scaled data. Hierarchical clustering was realized with the hclust function from R applied to Euclidian distance matrices with the Ward. D2 method. PERMANOVA of Euclidian distance matrices were conducted with the adonis function of the vegan package  [75] , with p-values computed on 9999 permutations. PERMANOVA assumes that 5-mer profiles respond linearly to changes in the covariates and that the variance of profiles is comparable across conditions of the data.
The p-values were computed by permutations: this nonparametric approach is robust to model misspecification. The wilcox. test function from R CRAN was employed to test the equality of means through Mann-Whitney-Wilcoxon statistical tests. Most plots were prepared with ggplot2 package  [76]. Dendrograms constructed with hclust were exported in newick format and used in the online tool Interactive Tree Of Life (iTOL)  [77]  to construct the tree figures.
Network analyses Gene sharing network data were generated with EGN 1. 0 software  [78]. For this purpose, whole proteomes were downloaded from the NCBI website; the resulting multifasta file was formatted according to the EGN manual's instructions. Blastp  [79]  searches were computed within EGN software, which acts as a wrapper. The EGN parameters were set as follows: e-value threshold of 1e-05, hit identity threshold of 30%, hit coverage of the shortest sequence of 60%, hit coverage of both sequences of at least 30%, minimal hit length of 20 amino acids, best reciprocity threshold of 10%. The EGN results consisted in the number of similar genes shared between each pair of genomes. These values were subsequently normalized by dividing them by the smallest genome length of the concerned pair.
This cell was composed of two half-shells enclosing the fatigue specimen. The test cell was connected to an oil pressure generator (Top Industry, Vaux Le Penil, France) with a maximum capacity of 100 bars. The control system was developed at LISN with the LABVIEW software (v. 2016, National Instrument, Austin, TX, USA). This dedicated CEA (French Alternative Energies and Atomic Energy Commission) in-house software allows fatigue tests to be carried out in two different control modes:.
Numerical simulations of the FABIME2 tests were carried out with the FE Software Cast3M. Different semi-elliptical crack sizes were studied, starting from a solid specimen up to a crack size of 10 mm (dimension of the useful area) (see Figure  10 ). For each crack size, the simulations were performed, taking into account the nonlinearity of the material behavior using an elasto-plastic behavior model stabilized with nonlinear isotropic and kinematic strain-hardening. From the results obtained, the stress normal to the plane containing the crack could be extracted and the decrease of this stress with the size of the crack could be determined. The results for two loading levels are shown in the following figure (see Figure  11 ). Thus, for a surface crack size of 5 mm, the applied stress decreased by 25%.
Study site and sampling This study is based on data from a provenance trial conducted at the Kissoko forestry station (04° 45′ 51″ S, 11° 59′ 21″ E) in the south of Republic of the Congo. Mean annual rainfall is approximately 1200 mm; daily temperature is 25-26 °C in the rainy season (October to May) and 22-23 °C in the dry season (June to September). The average monthly rainfall during the rainy season ranges from 83 to 92 mm, and during the dry season from 1 to 20 mm  (Jamet & Rieffel 1976). Relative humidity is high (85%) with low seasonal variability (2%). The soil is ferralitic and highly desaturated in bases  (Nzila 2001). We conducted the present study in a Eucalyptus provenance trial which included 32 provenances belonging to 29 species (Tab. 1). The seeds used for seedling production were collected from different parts of Australia (North, North East, East and South East coasts) and Indonesia (Fig.
Climate data We used data from the meteorological stations (http://www. bom. gov. au/) at each site where we collected the seeds of eucalyptus species in Australia and Indonesia. The following 18 climatic variables were considered (Tab. S1 in Supplementary material): 1-average annual rainfall (AAR); 2maximum annual rainfall (MAR); 3-minimum annual rainfall (mAR); 4-maximum monthly average rainfall (MMAR); 5-minimum monthly average rainfall (mMAR); 6number of months of rainfall less than 50 mm (NR50); 7-average annual temperature (AAT); 8-maximum monthly temperature (MMT); 9-minimum average temperature (mMT); 10-maximum monthly average temperature (MMAT); 11-minimum monthly average temperature (mMAT); 12-number of days below 40 °C (ND40); 13-number of freezing days (NFD); 14-annual potential evapotranspiration (PE); 15-annual solar radiation (SR); 16-altitude (A); 17-longitude (Long); 18-latitude (Lat).
Acknowledgements We are grateful to  Pacifique Ntadi  and the CRDPI technical team for field harvesting. We would like to thank the  Conservation Action Research Network (CARN) of the Congo Basin Forest Partnership (CBFP)  for their help to support this study. PhV and CGME conceived and designed the experiments; CBSVL and MPM performed the trials; CGME and GJLP carried out data analysis. CGME took the lead in writing the manuscript and all authors provided critical feedback.
org) of the Cornell Lab of Ornithology (Ithaca, USA), and the Wildlife Sound section of the British Library (sounds. bl. uk). These additional recordings came from an area from Pakistan across the Middle East and Europe to the UK (see Supplementary Table  S2 ). Our recordings were obtained with a portable video camera (Sony HDR-PJ30EDV; Shanghai Suoguang Electronics Co. Ltd. , China) or a digital recorder (JWD DV-58G; JWD Inc. , Shenzhen, China). Soundtracks were extracted from video files by means of Audacity software, and all sound files were converted to mono 44. 1 kHz. We avoided low-quality recordings with either low signalto-noise ratios, or those that had been filtered within the frequency band that we chose for comparisons (200-7,000 Hz).
Sound similarity analysis Hissing displays contain both mechanical sounds and vocal hisses (see Results section). For sound analysis, we chose high-quality tit recordings with clear, long hisses (n ¼ 18, Supplementary Figures S5). From each recording, 3-5 high-quality individual hisses were sampled for each of the recordings (mean ¼ 3. 7, standard deviation [SD] ¼ 0. 64). To perform acoustic cross-correlations, it is important to control for differences in sound duration and amplitude  (Khanna et al. 1997). From each call, we selected a series of randomly chosen 100 ms clips using the analysis software Raven Pro. 1. 5. (Center for Conservation Bioacoustics, 2014) and saved it as individual amplitude-standardized wav files. We made sure that this selection was not overlapped by other mechanical sounds such as those produced by the beak and wings of the bird when startled in the box (see Supplementary Video S1).
Statistical analyses In the case of the bioacoustics analyses, we took several clips (3-5, mean ¼ 3. 76, SD ¼ 0. 62) from each sound recording to increase reliability. However, to avoid pseudo-replication, in each comparison of 2 recordings, we calculated the average of the cross-correlation matrix of all clips, and this single value was used for each comparison. We built random models in R (package "lme4"), considering both recordings being compared as random factors nested within each category, as well as tit species. P values were calculated for fixed effects in mixed models by using the Kenward-Rogers approximation (package "afex" in R). Differences between factor levels were obtained by the "lsmeans" R package, following Tukey's correction for multiple comparisons. The cross-correlation scores were normalized by means of the Box-Cox procedure to maximize normality (package "MASS" in R).
Acknowledgments We would like to thank  Dr. Canwei Xia  from  Beijing Normal University  for help with white noise and hisses recordings, and  Tongping Su ,  Jianping Liu ,  Changzhang Feng , and  Jianwei Zhang  for help with fieldwork. We are grateful to  Matthew Medler  at  Macaulay Library  ( Cornell Lab of Ornithology ) and  Cheryl Tipp  and  Tom Ruane  at the  British Library  for access to recordings. Likewise we thank all individual contributors of recordings to Xenocanto and the  Xeno-canto Foundation  for curating this superb collection.
Patients' Data The study was approved by the institutional review board. It was conducted in compliance with the French Research Standard MR-004 "Research not involving Human participants" (compliance commitment to MR-004 for the Centre François Baclesse number 2214228 v. 0, dated from 07/03/2019) and is registered with the French Health Data Institute under the reference MR 2513060320. All patients received information and none of them expressed opposition to the use of their data. The records of 44 consecutive DTC patients with PRD who underwent a surgical reintervention in the neck from 2006 to 2018 at the Comprehensive Cancer Center in Caen, France, were reviewed. Patients were operated on levothyroxine treatment (n = 35) or after TSH stimulation in case of RAI-guided surgery (n = 9).
This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination for MPS, e. g. , red, green, and blue light, may pollute the laser color. Unlike previous methods, through the improvement of the network proposed by Isola, a Generative Adversarial Network based on image in-painting was proposed, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line. Then these results were substituted into the method proposed by Fan to obtain high-precision 3D reconstruction results. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network.
The overview of the proposed method is shown in Figure  1. 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 multi-spectral 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.