dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluating the impact of HGT in NAPs evolution Some of the topologies shown in the phylogenies of NAPs (Fig  5 ) strongly disagree with the eukaryotic species tree (Fig 2 ), and hence would require a large number of ancestral paralogues and differential paralogue losses to be accounted by a strictly vertical inheritance scenario. In general (and with the exception of nrt2, see below), there is usually one copy of NAP genes per genome (see Table A in S1 Supporting information). Therefore, we did not find any a priori reasons to hypothesize that the number of copies could have been greater in the ancestral genomes. To evaluate potential cases of HGT, we performed AU tests  [27]  (see all tested topologies and AU-test results in Table C in S1 Supporting information), as well as additional phylogenetic inferences excluding conflicting taxa and increasing the taxon sampling by incorporating orthologues from the taxon-rich Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP) dataset  [28]  (MMETSP trees, see Materials and methods section).
In contrast, a closer relationship to Ochrophyta would be expected if the transfer was from Oomycota to Ichthyosporea. We thus evaluated if the exclusion of either Ichthyosporea or Oomycota could clarify the direction of the transfer by performing topological tests on two reduced datasets, each one excluding sequences from one of the two groups. In the dataset without Ichthyosporea, tests included the best topology and two alternative topologies constraining (i) the monophyly of Oomycota + Ochrophyta and (ii) the monophyly of Oomycota + C. limacisporum. For the second reduced dataset the same procedure was carried out but with Ichthyosporea instead of Oomycota. For NAD(P)H-NIR, the Oomycota + Ochrophyta topology was rejected in the dataset excluding Ichthyosporea (p-AU 0. 0163). However, the equivalent topology in the dataset without Oomycota (i.
e. Ichthyosporea + Ochrophyta) was not rejected (p-AU 0. 0721). In the case of the NRT2 reduced datasets, none of the topologies were rejected (see Table  C  in S1 Supporting information). In conclusion, the results, specifically from NAD(P)H-NIR, altogether provide more support for the scenarios proposing a transfer from Ichthyosporea to Oomycota (H1, H4 and H8). However, the fact that other scenarios were not rejected in NRT2 and the lack of concordance between the best topologies inferred for the reduced datasets (S11 and S12 Figs and S16 and S17 Figs) indicate limitations in the phylogenetic signal to provide conclusive support for any of the proposed scenarios. EUKNR. The distribution of euknr was found to be very similar to that of nrt2 (S2 Fig). Euknr is present in most photosynthetic and non-photosynthetic organisms for which we inferred the capability to assimilate nitrate (Figs  2  and 5 ).
and S18 Fig). Notwithstanding the overall weak support of the phylogeny, we found three unexpected and well-supported relationships between distantly related taxa. Firstly, Oomycota sequences branch as the sister-group to the ichthyosporeans C. fragrantissima and S. arctica, as in the NRT2 and NAD(P)H-NIR trees (UFBoot 95%). This strongly indicates that a transfer of the whole pathway occurred between Oomycota and Ichthyosporea. Secondly, there is a clade that comprises several distantly related fungal sequences as well as a sequence from Acanthamoeba castellanii (Amoebozoa). However, sequences from these fungal taxa are also found in another clade that includes the A. nidulans sequence of bona fide nitrate reductase activity (named Anid_NaR in the euk_db dataset)  [37]   [38]. Moreover, there is indirect experimental evidence suggesting that the A.
While Cryptophyta and Glaucophyta are poorly represented in our dataset, the absence of clusters in Streptophyta (includes land plants) is remarkable since NAPs are found in the 9 sampled genomes of this group (Table A in S1 Supporting information). We found that >64% of the detected clusters include the whole pathway, nrt2, euknr and either NAD(P)H-nir or Fd-nir. In Ochrophyta, the only eukaryotic group with taxa showing both nitrite reductases in the same genome, we found clusters comprising nrt2 or euknr and either Fd-nir or NAD(P)H-nir, but never both (Fig  2 ). In agreement with the gene distribution, clusters with Fd-nir are only found in autotrophs. While clusters with NAD(P)H-nir are also found in autotrophs, in particular in two Ochrophyta (Stramenopiles, SAR) and one Myzozoa (Alveolata, SAR) species; they are mostly distributed along osmotrophic taxa from Oomycota and Labyrinthulea (Stramenopiles, SAR) and from Fungi and Teretosporea (Opisthokonta) (NAP clusters with NAD(P)H-nir hereafter abbreviated as hNAPc, for "heterotrophic NAP clusters").
Our finding of HGT events involving taxa from these two groups but also from Teretosporea and Labyrinthulea extends the potential scope and importance of this mechanism in the acquisition of metabolic features associated to an osmotrophic lifestyle  [21]. These hypothetical scenarios proposed for the origin of nitrate assimilation in Opisthokonta disagrees with the evolutionary scenario hypothesized from previous results  [14]. In particular, it was proposed that a stramenopiles lineage leading to Oomycota could have transferred a NAP cluster to Dikarya (Fungi), while in our trees with an updated taxon-richer dataset, Oomycota branches as sister-group to Ichthyosporea within the Opisthokonta + Stramenopiles clade (Fig  5 ). To evaluate whether the discrepancies with previous studies are due to differences in the taxon sampling, we constructed trees excluding all sequences from Teretosporea and Labyrinthulea, which were not available in previous analyses.
Interestingly, in the absence of these two groups, we recovered the Oomycota (Stramenopiles) sequences branching as sister-group to Fungi for both NRT2 and NAD(P)H-NIR phylogenies with a reduced dataset (S23 Fig and  S24 Fig, respectively ). In agreement with the H1 and H8 or H4 scenarios, we propose that in the absence of Labyrinthulea and Teretosporea, Oomycota (Stramenopiles) branched as more related to Fungi (Opisthokonta) than to Ochrophyta (Stramenopiles) because Oomycota would have received the NAP genes from Opisthokonta, in particular from Ichthyosporea. Notwithstanding, the support for the proposed scenarios is susceptible to change with the addition of further taxa, given the dependence of HGT inference to the taxon sampling used  [7].
Phylogenetic screening of NAPs An updated database of 174 eukaryotic proteomes (euk_db) was constructed (January 2017), using predicted protein sequences from publicly available genomic or transcriptomic projects  [68] [69] [70]. The complete list of species, with the corresponding abbreviations, is available in Table A in S1 Supporting information. The phylogenetic relationships between all the sampled eukaryotes were constructed from recent bibliographical references  [71] [72] [73] [74] [75] [76] [77] [78] [79] [80] [81] [82]. Protein domain architectures from all euk_db sequences were obtained with PfamScan (a Hidden Markov Model [HMM] search-based tool) using Pfam A version 29  [83]. A database of prokaryotic protein sequences (prok_db) was constructed from Uniprot bacterial and archaeal reference proteomes (Release 2016_02)  [70]  with the aim of detecting potential prokaryotic contamination in euk_db as well as to investigate the prokaryotic origins of eukaryotic NAP genes.
For each NAP, we followed a multi-step procedure in order to maximize both sensitivity and specificity in the orthology assignation process (see S2 Supporting information for detailed information about the particular strategy followed for each NAP; available in (doi. org/10. 6084/m9. figshare. 6462311. v1). The overall strategy consisted first in identifying potential NAP family members in euk_db with BLAST (version 2. 3. 0+) [84]  and HMMER (version 3. 1b1)  [85]. For BLASTP searches, we queried the databases using the NAP sequences from Chlamydomonas reinhardtii and A. nidulans  [18] , downloaded from Phytozome 11  [86]  and NCBI protein databases  [68] , respectively [BLASTP: -evalue 1e-5, only non-default software parameters specified]. For HMMER searches [hmmsearch], we used the HMM Pfam domains MFS_1 for NRT2, Oxidored_molyb and Mo-co_dimer for EUKNR, and NIR_SIR for NAD(P)H-NIR and Fd-NIR.
The candidate sequences retrieved from the BLASTP and hmmsearch analyses were submitted to cdhit (version 4. 6)  [87]  [-c 0. 99] to remove repeated/very recent paralogues (i. e. redundant sequences). We used the non-redundant candidate sequences to detect potential prokaryotic homologues in prok_db [-evalue 1e-5], to use them as outgroups to eukaryotic sequences and/or to detect potential euk_db contaminant sequences during the phylogenetic analyses. The non-redundant candidate sequences and the captured prokaryotic homologs were submitted to an iterative process in which we recursively performed phylogenetic inferences with the sequences non-discarded in the previous steps until we reached a set of bona fide NAP family members. The criteria to discard sequences in each step was mainly phylogenetic, but also assisted with functional information of each candidate sequence, predicted from their Pfam domain architecture and from their best-scoring BLASTP hit [-evalue 1e-3] against the SwissProt database [70] (downloaded on July 2016).
Correlation between NAPs distribution and feeding strategies We constructed phylogenetic profiles for each NAP gene family: vectors with presence/ absence information (coded in "1" or "0", respectively), with every position of the vectors corresponding to a certain species sampled in our euk_db dataset. These vectors were then used to quantify the correlation between the distributions of the different NAPs by computing the inverse of the Hamming distance between each pair of phylogenetic profiles. We also quantified the correlation between the distribution of the different NAPs and the distribution of the different nutrient acquisition strategies in eukaryotes. For that, we classified eukaryotes into 'Autotrophs/Mixotrophs' (i. e. strictly and facultative autotrophs) and 'Non-autotrophs'. 'Nonautotrophs' (i.
Trees representing the alternatives topologies were constructed also with IQ-TREE, using the same alignments and evolutionary models and constraining the topologies with Newick guide tree files [-g option]. For bootstrap support assessment, we used the ultrafast bootstrap option (1000 replicates) because it was shown to be faster and less biased than standard methods  [93, 94]. For model selection, we used ModelFinder, already implemented in IQ-TREE  [95]. Moreover, and given that some eukaryotic groups were poorly represented due to the lack of genomic data, we constructed additional NAP trees incorporating orthologues from the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) dataset  [28]. We queried the reference NAP protein sequences against all the MMETSP transcriptomes [BLASTP: -evalue 1e-3].
MMETSP NAP orthologues were identified from the aligned sequences by means of Reciprocal Best Hits (RBH)  [96]  and best-scoring BLASTP hit against SwissProt database [-evalue 1e-3]. Whereas some contamination may be expected from the MMETSP dataset, this does not influence the deduced presence of NAP genes in the eukaryotic groups (Fig  1 ), as to establish the latter we only employed the curated and comprehensive euk_db dataset.
Comprehensive screening of NAPs in prokaryotes We used the bona fide eukaryotic NAP sequences to capture potential prokaryotic orthologues of nrt2, NAD(P)H-nir and Fd-nir. First, we queried those sequences against prok_db with BLASTP [-max_target_seqs 100, -evalue 1e-5]. Protein domain architectures were annotated with PfamScan, and those with clearly divergent architectures were discarded. The remaining prokaryotic sequences were aligned with eukaryotic NAPs using MAFFT [mafft-einsi]. The alignments were trimmed with trimAl [-gappyout] and the phylogenetic inferences were done with IQ-TREE [ultrafast bootstrap 1000 replicates, best model selected with ModelFinder]. Prokaryotic sequences were taxonomically characterized by aligning them against a local NCBI nr protein database (downloaded on November 2016), and only hits with more than 99% of identity and query coverage were considered [BLASTP: -task blastp-fast].
Construction of sequence similarity networks Sequence similarity network of full length EUKNR. The EUKNR protein sequences were aligned against a database including euk_db and prok_db [BLASTP: -max_target_seqs 10000, -evalue 1e-3]. Aligned sequences were concatenated with the EUKNR and redundant sequences were removed with cdhit before being aligned all-against-all with BLASTP [-max_-target_seqs 10000, -evalue 1e-3]. We used Cytoscape (version v. 321)  [87]  to construct a sequence similarity network from BLAST results, represented using the organic layout option. In the network, each aligned protein correspond to a node. Nodes are connected through edges if the corresponding sequences aligned with a lower E-value than the threshold value. A relaxed E-value threshold would lead to an over-connected network, with edges connecting very divergent proteins.
MAFFT [mafft-einsi], trimAl [-gappyout] and IQ-TREE [ultrafast bootstrap 1000 replicates, best model selected with ModelFinder] were used for phylogenetic inference. Sequence similarity network of EUKNR Cyt-b5 domain. The regions of the A. nidulans and C. reinhardtii EUKNR corresponding to the Cyt-b5 Pfam domain were aligned against a database including euk_db and prok_db [BLASTP: -max_target_seqs 10000, -evalue 1e-3]. Non-redundant and non-EUKNR sequences were concatenated with the two EUKNR Cyt-b5 sequences and an all-against-all alignment was performed [BLASTP: -max_target_seqs 10000, -evalue 1e-3]. Sequence names were modified to include information of their protein Pfam domain architectures  [PfamScan]. A sequence similarity network was constructed with Cytoscape and represented with the organic layout option (as with full length EUKNR), removing self-loops and duplicate edges and using an E-value threshold of 1e-17.
cDNA was quantified using SYBR Green supermix (Bio-Rad) in an iQ cycler and iQ5 Multi-color detection system (Bio-Rad). Primer sequences are shown in Table D in S1 Supporting information. The total reaction volume was 20 μL. All reactions were run in duplicate. The program used for amplification was: (i) 95˚C for 3 min; (ii) 95˚C for 10 s; (iii) 60˚C for 30 s; and (iv) repeat steps (ii) and (iii) for 40 cycles. Real-time data was collected through the iQ5 optical system software v. 2. 1 (Bio-Rad). Gene expression levels are expressed as number of copies relative to the ribosomal L13 subunit gene, used as housekeeping.
Measurements on a controlled experiment of various floating plastic debris items using a handheld hyperspectral radiometer show overall light reflectance decreases, and depths of the absorption bands decrease, with increasing transparency of plastic [52]. Figure  2. Near-infrared short-wave infrared (NIR-SWIR) reflectance spectrum (%), plastic absorption feature locations and satellite detection windows. Thick solid red line is median reflectance spectrum and shadowed area is the standard deviation of all plastics measured (data adapted from  [53] ). Thick horizontal bars highlight the regions of the spectrum with major absorption features (data adapted from  [50] ). Double arrow end lines show the maximum width of the spectral response function and the band names of the Sentinel 2B Multi Spectral Imager (MSI).
Synergy studies between ocean colour radiometry and high spectral resolution have detected harmful algal blooms in coastal areas, thanks to the specific pigment assemblage and its related radiometric signal in the visible part of the spectrum  [89]. Although common spectral features in the visible are not expected from marine plastic debris, changes in intensity of the reflected light could be exploited if in situ quantification and radiometry were available for validation  [85, 90, 91]. Controlled experiments using artificial plastic targets on the sea surface could be used to gain a better understanding together with the construction of a specific dataset from in situ observations combining radiometric and marine plastic debris concentrations. In addition to the potential contamination issues, there is the problem to separate the signal of marine plastic debris from non-debris plastic matter in use.
Acknowledgments: This work is the result of discussions and contributions from the 2017 ESA Workshop on Remote Sensing of  Marine Litter  (30 November-1 December 2017,  ESA-General Studies Programme , The Netherlands) which involved researchers in the field of marine debris and experts from multiple areas of Earth Observation. Thanks to  D. Ashby  for the artwork in Figure 1 andT. Platt for useful comments on the manuscript. We thank the comments of three anonymous reviewers who helped improve this manuscript.
Funding: Support by the  ESA General Studies Programme  to V. M-V. , J. R. C. , P. L. , S. G. H. S. through  OPTIMAL grant is acknowledged  (project No. 4000120879/17/NL/PS ). V. M-V. , S. A. , N. M. and E. v. S. are members of WG 153 supported, in part, from funding provided by national committees of the  Scientific Committee on Oceanic Research (SCOR)  and from a grant to SCOR from the  U. S. National Science Foundation  ( OCE-1840868 ). E. v. S. is also supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  715386 ). S. P. G. is supported by funding from  Forschungsgemeinschaft (DFG, German Research Foundation)-Projektnummer 417276871. A. C. and F. E. were supported by  MIDaS  project ( CTM2016-77106-R, AEI/FEDER/UE ).
Spherical indentation on a copper single crystal The experimental data used in this work was extracted from the spherical indentation test presented in the work of Juran et al. [23]  and is briefly recalled here for the sake of completeness. The copper single crystal was produced by directional solidification using a standard zone melting method based on a horizontal Bridgman-type apparatus. The samples were cut by Electric Discharge Machining (EDM) into parallelepiped-shaped specimens and mechanically polished. A final electro-polishing step removed remnants of mechanical hardening due to prior polishing. The indented surface was {100} oriented along the surface normal. Quasi-static indents were performed using a load-controlled standard Brinell.
Indenter mesh A convergence study on the indenter's mesh size was performed to remove its influence on the predicted residual stress and misorientation fields. The indenter being asymmetrically meshed (with respect to the specimen's symmetry axes), a low indenter mesh density in the contact zone leads to asymmetric fields. Convergence was therefore assessed by characterizing the residual stress field symmetry in the (x,y) plane at z=0 at several depths y using an error criterion defined as: y = 2 N y Ny/2 i=0   σ(x i ) -σ(x N -i ) max i (σ i )   2 , ( 10 ) where N y is the number of data point at depth y. The evolution of y with mesh density at different depths is presented in Figure  16. The figure reveals that the indenter mesh has indeed an influence on the field symmetry for low mesh densities.
Border dimensions Border effects can induce local variations of the residual stress field, depending on Zone B (Figure  1 ) relative size to the contact radius. The value of f t was therefore computed Figure  17  shows the evolution of f t with Zone B to Zone A dimension ratio 2b R. Border influence on the value of f t is observed for low Zone B dimension values. Convergence can however be observed for borders twice larger than the refined zone. allows to describe all possible crystal orientations. Simulated orientations were chosen by meshing the IPF with tetrahedral elements, using gmsh software  [15] , so as to cover the whole domain as homogeneously as possible. The chosen mesh is represented on Figure  19 (b). Each node of the mesh represents a simulated indentation direction.
In this paper, we attempt to better characterize and constrain the biological identity of this emblematic microfossil using morphological, morphometric, microchemical and ultrastructural analyses of Cerebrosphaera specimens from Spitsbergen and Australia, where large populations of fossils are available for study. We also report for the first time the presence of Cerebrosphaera in Africa. The sample comes from the Bouenza Subgroup, Mayombe Group, West Congo Supergroup, Niari basin of the Republic of the Congo. This discovery strengthens the value of Cerebrosphaera as an index taxon for the late Tonian, with also implications for the Neoproterozoic stratigraphy and paleogeography of the Congo Craton. Finally, we test hypotheses of taxonomic identification by comparing with published and new data from modern and fossil potential analogues.
Light microscopy The diversity of the microfossil assemblages in the Kanpa and Hussar formations from the Lancer 1 and Empress 1A drill cores was studied by the observation of 52 palynological slides with a Zeiss Primo-star light microscope (Cornet, unpubl. MSc thesis), using descriptions available in the literature, especially  Butterfield et al. (1994)  for the Svanbergfjellet Formation,  Cotter (1999)  for the Supersequence 1 of the Officer Basin, and Hoffman and Jackson (1994) for the Bylot Supergroup, Canada. Microphotographs were taken with an Olympus BX51 coupled to an Olympus U-CMAD3 camera, and a Zeiss Axioimager equipped with the Axiovision software and a digital camera Axiocam MRc5, at the laboratory "Early Life Traces and Evolution-Astrobiology", University of Liège, Belgium. Morphometric analyses were done using the measurement tool of the Axiovision software at high magnification.
We can note the variation of the intensity of the colour of specimens coming from the same samples. K, L: Bz2 Formation. K: Entire vesicle showing the cerebroid folding of the wall. L: Vesicle fragment. Scale bar is 20 µm. Cerebrosphaera from the Svanbergfjellet formation are illustrated in  Butterfield et al. , 1994. (fixed at 1150 cm -1 ) for 1 × 1 s running time. This allowed the acquisition of Raman spectra with a 2000 cm -1 detection range and a 4 cm -1 spectral resolution. The software "Renishaw Wire 4. 1" was used to process the spectral data. The baseline subtraction protocol was performed on a truncated spectrum between 1000 and 1800 cm -1. The baseline was subtracted with a third order polynomial fit. Following this data processing, D1-, D2-, D3-, D4-and G-bands were fitted by a decomposition with the protocol described in  Sforna et al.
The CH 2 /CH 3 ratio compares the intensity of the CH 2 antisymmetric stretching with the intensity of the CH 3 antisymmetric stretching. Its value can be directly linked to the length and degree of branching of the aliphatic chains of the biopolymer making up the wall of the microfossil vesicles. A high ratio reflects long and/or unbranched aliphatic chain, a low ratio is the sign of short and/or highly branched aliphatic chains  (Lin and Ritz, 1993). To obtain the precise intensities needed for the calculation, the region comprised between 3000 and 2700 cm -1 was deconvoluted into five bands and fitted using the OPUS 7. 0 software. The ~2955 cm -1 band is assigned to the antisymmetric CH 3 stretching, the ~2920 cm -1 band to the antisymmetric CH 2 stretching, the ~2890 cm -1 to the CH stretching, the ~2870 cm -1 band to the CH 3 symmetric stretching and the ~2850 cm -1 with the symmetric CH 2 stretching  (Lin and Ritz, 1993; Marshall et al.