dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioinformatic Analyses Raw sequencing reads were quality checked and trimmed of adapter sequences before they were assembled using the de novo assembly algorithm implemented in the CLC genomics workbench (CLC, Arhus, Denmark). High quality contigs are available under the accession numbers at NCBI (T20: VFEB00000000; T6: VFEC00000000; T5: VFED00000000; T4: VFEE00000000; T2: VFEF00000000; S7: VFEG00000000; S15: VFEH00000000; S12: VFEI00000000; S16: VFEJ00000000) and were annotated using the automated RAST pipeline at http://rast. nmpdr. org. A genome wide phylogeny of the strains sequenced here was based on a distance matrix of pairwise distances between genomes computed by the software andi  (Haubold et al. , 2015). The distances were clustered using the neighbor joining algorithm implemented in the neighbor routine of the PHYLIP software suite available at http://evolution.
Only Sylt sequences were predicted to contain a Glycosyl Hydrolase 3 domain (PF00933, e = 6. 2e -04 ) from position 21 to 96, which was not predicted for any of the Texel sequences. In all strain genomes, OG1907 is localized upstream of genes encoding cusABC, which are predicted to form a copper efflux pump  (Pal et al. , 2017 ; Figure  3B ). The close association to cusABC together with its extracellular localization might therefore suggest that OG1907 encodes an extracellular protein involved in copper homeostasis by a mechanism that remains to be determined. Overall, owing to the insertions in T20 and the high number of substitutions in T2 compared to the similar T4, T5, and T6 sequence similarity among strains was comparatively low along the sequence and only the terminal parts of the protein were reasonably conserved (Figure  3A ).
Accordingly, these substitution patterns resulted in a Direction of selection coefficient of DoS = -0. 065 indicating rather neutral than adaptive evolution between locations. Whole gene estimates of selection can incorporate a mix of sites under purifying selection (dN/dS < 1) and sites under positive selection (dN/dS > 1) that cancel each other out in the overall analysis. This also applied to OG1907 where specific positions in the alignment showed signatures of strong positive selection with dN/dS ratios reaching as high as 8. 48 for position 108 (Figure  3A , red line). Together with a second peak of selection in OG1907 at position 183 of the alignment (matching position 152 of the protein in T2) with a dN/dS ration of 7. 303 these targets of selection were found in the data set only containing Sylt sequences, suggesting that local diversifying selection maintained polymorphism within this population despite of high overall sequence similarities in this group.
Proteins from this family showed significant sequence similarity with proteins from the YeeE/YeeD family of membrane transporters and especially with a transporter of sulfur-containing compounds, PmpA, identified in Serratia sp. ATCC39006  (Gristwood et al. , 2011). This conserved domain (PF04143, e = 6. 9e -10 ) was found in the sequences of all strains used here and the gene was localized in close proximity of a glycosyltransferase, an acetyl transferase, a thiolase and a thioredoxine (Figure  4B ), with the latter two suggesting that this protein family is a conserved transporter of sulfur containing molecules. Large sections of the gene were highly conserved and especially the transmembrane domains of the protein show only little variation between the strains used here (Figure  4A ). Consequently, we detected a low dN/dS = 0.
Position 69 in the first cytoplasmatic domain showed positive selection mainly within the Sylt strains (dN/dS = 2. 77, Figure  4A , red line). Adaptive evolution between locations might have been driven by positions 98 and 99, which showed elevated between location selection with dN/dS ratios of 2. 064 and 2. 120 for the whole data set containing all sequences (Figure  4A , black line). Constraining the data set to sequences from one location revealed purifying selection within the Sylt sequences (dN/dS = 0. 377 and 0. 403) and neutral evolution within the Texel sequences (dN/dS = 1. 106 and 1. 286). This between location selection peak fell into the second periplasmatic domain of the sulfur transporter. Together with the Sylt-specific insertion and the high dN/dS ratio within Sylt sequences in the first cytoplasmic domain this might suggest a potential functional role of these domains in ligand specificity for the transport between the cytoplasm and the periplasm (Figure  4A ).
FUNDING This work was supported by the  DFG Emmy Noether  grant  WE4641/1  given to KW,  ANR  grant  REVENGE  ( ANR-16-CE32-0008-01 ) given to FR,  IFREMER and Région Bretagne  for funding DP, and the  PACES Research Programme  of the  Alfred Wegener Institut -Helmholtz-Zentrum für Polar-und Meeresforschung.
DATA AVAILABILITY Assembled sequences are available from NCBI under the accession numbers: VFEB00000000 (T20); VFEC00000000 (T6); VFED00000000 (T5); VFEE00000000 (T4); VFEF00000000 (T2); VFEG00000000 (S7); VFEH00000000 (S15); VFEI00000000 (S12); VFEJ00000000 (S16).
Membrane stretch FEM modeling In order to calculate the membrane in-plane force T 0 , the parameters listed in Table  4  are used to simulate the membrane stretch process illustrated in Fig. 5. The simulation is performed using Abaqus software. The Ogden parameters identified in section 3. 3 are used in the FEM computations of the hyperplastic material behavior. The rectangular membrane of 10 mm×100 mm×1 mm membrane is modeled using membrane elements. A displacement of 3 and 4 times the length of the rectangular membrane is imposed in both X and Y directions, given 300 mm and 400 mm length, respectively. A quasi-static study is performed in order to estimate the stress σ of the membrane and the resulted thickness t 0 which allows to calculate the in-plane force T 0 =σt 0. Results are listed in Table  5.
Identification of compounds For all cultivars, the quantitative composition was calculated as a relative percentage of peak area and oil components were identified by calculating of their retention indices under temperature-programmed conditions for n-alkanes (C8-C20) and the oil on an RTX5MS column under the same chromatographic conditions. Identification of individual compounds was based on the NIST05 database and confirmed by comparison of their retention indices with authentic compounds or with those reported in the literature. [9] The relative percentage of the oil constituents was determined by normalization from the GC peak areas, assuming identical mass response factor for all compounds: (% Ci = Si/S total ) with (%Ci = percent of analyt/Si = Surface of pic area of Ci/S total = total surface of all pics).
Statistical analysis ANOVA was applied, where values of p ≤ 0. 05 were considered to be statistically significant for verifying the chemical variability of the compounds in the essential oils identified in the samples. Data analyses were performed using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Both methods aim at reducing the multivariate space in which objects (oil samples) are distributed, but are complementary in their ability to present results. [10] PCA provides the data for plots in which both objects (oil samples) and variables (chemical classes or oil components) are represented, while a dendrogram was produced by HCA using a method of hierarchical clustering, based on Euclidean distances between pairs of oil samples. The PCA was carried out using the function PCA of XLSTAT 2014 and the statistical R software (R Foundation, Institute for Statistics and Mathematics, Austria).
Extraction Optimization The experiment was planned using Box-Behnken design (BBD) in Design Expert software v. 8. 0. 6 (Stat-Ease, Minneapolis, MN, USA). The ranges of design parameters (independent variables) were: glycerol concentration (X 1 , 10-90%, w/w), temperature (X 2 , 20-70 • C), and drug weight (X 3 , 0. 6-1g) used for the extraction. TP content, TP/X 3 ratio (TPy), as well as the Gla and Iso content of the extracts were dependent variables. Response-surface methodology was used to find the relationship between dependent and independent variables. Experimental data was analyzed by multiple regression analysis and fitted to the appropriate polynomial models. The validity of the model was confirmed by the analysis of variance (ANOVA). p values < 0. 1 were considered statistically significant.
Acknowledgments: This work was financed in course of the project, " Bioactive plant principles extraction using green solvents -a step towards green cosmeceuticals " ( IP-2018-01-6504 ), supported by  Croatian Science Foundation. The authors are grateful to the company Suban for donation of plant material. Acknowledgments: This work was financed in course of the project, " Bioactive plant principles extraction using green solvents -a step towards green cosmeceuticals " ( IP-2018-01-6504 ), supported by  Croatian Science Foundation. The authors are grateful to the company Suban for donation of plant material.
Data was acquired on a LSR Fortessa (BD Biosciences) and analyzed with FlowJo software version 10 (TreeStar). Microbiota and mucosa-associated commensals preparation and binding studies For total microbiota analysis, fresh content from the whole intestine, except caecum, of SPF or germ-free mice was collected from mice with ad libitum access to food and water. For mucosa-associated commensals isolation, SPF mice or mice treated with antibiotics in utero until the weaning and gavaged with Lactobacillus plantarum (L. plantarum) were starved for 18 hours prior to collecting the mucus by gentle scraping of the epithelium from the small intestine as previously described  (Cohen and Laux, 1995). In both cases, the samples were vortexed, filtered by 70 mm filter, centrifuged at 600 g 1 min to remove large particles, the supernatant was centrifuged at 13000 g for 3 min and the pellet was diluted in to an OD 600nm of 0.
RNA isolation and quantitative PCR Total RNA from the liver parenchymal fraction, small intestine, ileum or PPs was isolated using Trizol (Thermo Fisher Scientific) in combination with RNeasy Mini Kit (QIAGEN) accordingly to manufacturer's instructions. RNA was reverse transcribed to cDNA using random hexamers and High Capacity cDNA Reverse Transcription Kit (Applied Byosystem). Quantitative PCR amplification was performed with the GoTaq qPCR Master Mix (Promega) in a 7900HT Fast Real-Time PCR System (Applied Byosystem). All reactions were done in triplicate, following the manufacturer's instructions. Data were normalized to Gadph or bactin indicated in each case and displayed as relative values. Primer sequences can be found in Table  S1.
All libraries were diluted to the same concentration, using Illumina Resuspension Buffer (RSB) and were combine an equal volume of all of them in the same pool to sequence. The sample pool (10 nM) was denatured with 0. 2 N NaOH, then diluted to 6 pM and combined with 50% (v/v) denatured 6 pM PhiX, prepared following Illumina guidelines. Samples were sequenced on the MiSeq sequencing platform, using a 2 3 300 cycle V3 kit, following standard Illumina sequencing protocols. Processing of 16S sequencing data Samples were analyzed using BaseSpace Application 16S Metagenomics v1. 0 (Illumina) to determine the bacterial taxonomical composition. This program use Illumina-curated version of the GreenGenes taxonomic database and RDP Classifier (Ribosomal Database Project). For some additional data analysis, the ''Quantitative Insights into Microbial Ecology'' software (QIIME version 1.
9. 0) was used  (Caporaso et al. , 2010). Processed reads were then clustered in Operational taxonomic units (OTUs) using UCLUST with a similarity threshold of 0. 97 and were aligned using PyNast against 16S reference database GreenGenes version 13. 8 using default parameters. Enrichment index was obtained by comparing the relative abundance of each genus in Mincle-hFc-enriched and depleted fractions. Specificity index was calculated by comparing the relative abundance of each genus in Mincle-hFc and Control-hFc enriched fractions.
, 2017). For DAG profiling, the resulting data matrix is composed of all individual species of DAGs detected in the samples sorted by their characteristic retention time and neutral mass, and the abundance of each compound for each sample. DAGs were identified by the exact mass and by the elucidation of MS/MS spectra obtained using a LC-MS/MS analysis in production mode. In contrast, for FFA profiling, the resulting matrix is made of all individual species of FFAs detected in the samples sorted by their characteristic retention time and target ion, and the abundance of each compound for each sample. FFAs were identified by comparing their retention time, retention index and mass fragmentation patterns with those available in an in-house library as described  (Mastrangelo et al. , 2015)  QUANTIFICATION AND STATISTICAL ANALYSIS Statistical analysis was performed using GraphPad Prism 5 (GraphPad Software).
Carlos III (CNIC) facilities for technical support. M. M. -L. received a  Formacion de Personal Universitario (FPU)  fellowship ( AP2010-5935 ) from the  Spanish Ministerio de Educacion. S. I. is funded by grant  SAF2015-74561-JIN  from the  Spanish Ministerio de Ciencia, Innovacion, y Universidades (MCIU)  and  Fondos Europeos de Desarrollo Regional (FEDER). G. D. B and D. M. R. are supported by the  Wellcome Trust  and the  MRC Centre for Medical Mycology at the University of Aberdeen. S. L. L. is supported by the  Swiss National Science Foundation  ( PP00P3_150758 ). Work in the D. S. laboratory is funded by the  CNIC  and grant  SAF2016-79040-R  from  MCIU , the  Agencia Estatal de Investigacion , and  FEDER ;  B2017/BMD-3733  Immunothercan-CM from  Comunidad de Madrid ;  RD16/0015/0018-REEM  from  FIS-Instituto de Salud Carlos III, MCIU , and  FEDER ; the  Acteria Foundation ; the  Constantes y Vitales prize (Atresmedia) ;  La Marato  ´de TV3 Foundation (201723); the  European Commission  ( 635122-PROCROP H2020 ), and the  European Research Council  ( ERC-2016-Consolidator Grant 725091 ).
1) was identified in the transcriptome. The sequence was supported by both RNA-seq and riboseq, but was missing in the genome assembly. Oyster selenoproteins belonged to 22 distinct families. These encompassed all selenoprotein families previously described in invertebrate metazoans  [39] , including three families not found in vertebrates: AHPC, MSRA, and DSBA. In addition, we identified a Sec-containing radical Sadenosyl methionine (RSAM) gene (Radical SAM/ Cys-rich domain; Interpro IPR026351) in M. gigas, including a SECIS downstream of CDS. RSAM selenoproteins had never been observed in metazoa but had been previously reported in bacteria  [62]  and in a single-cell eukaryote, the harmful bloom alga shown. We tested mRNAs with either their native initiation context or an added Kozak consensus, with or without supplemented CT-SECISBP2.
Given the larger numbers of distal UGAs in many cases and the proximity of these UGAs to each other (there are several occurrences of just one codon separating two UGA codons), we consider models involving the SECIS complex tracking with, or acting as a once off ribosome "switch"  [87] , to be more appealing than separate contacts with a SECIS complex at each UGA. While there is good evidence from zebrafish to mammals for the general validity of the Berry model (UGA1 redefinition mainly enabled by SECIS 2, and redefinition of 3 0 UGAs by SECIS 1), we have no experimental data relevant to whether this model is applicable for invertebrates. However, our experiments showed that both SECIS elements of oyster SelenoP supported progressive Sec incorporation in vitro of a zebrafish construct, suggesting that such SECIS specialization is not present in oyster.
Methods SelenoP gene finding, phylogeny, and filtering Gene prediction was carried out with the program Selenoprofiles v3. 5c  [88] , which employs a protein alignment profile to scan nucleotide databases and find genes belonging to the same family. Due to its peculiar encoded C-terminal Sec-rich domain, Sele-noP is particularly difficult to predict. This domain contains various stretches of repetitive sequences, resulting in lots of spurious hits in non-homologous repetitive regions of genomes during the very first step of Selenoprofiles (Blast search). We thus forced Selenoprofiles to perform the initial Blast search using only the conserved N-terminal domain. A second challenge derives from the C-terminal domain of SelenoP having no apparent similarity across various lineages, which hinders their discovery by homology.
The 3 0 portion of SelenoP genes can be found by looking downstream of regions with similarity to the N-terminal domain; in genomes, however, this is hampered by the potential presence of intervening introns. To remedy this, we searched abundant transcriptomic data (listed below) and used results to build a profile alignment containing representative SelenoP sequences across diverse lineages. This permitted the finding in genomes of homologous sequences encoding Cterminal domains. The newly created profile is now included in the Selenoprofiles package. In all our searches, we completed gene structures by extending homologous matches upstream (to the farthest AUG before any stop codon) and downstream (allowing UGA codons, but not any other stop). In total, we searched 1159 NCBI genomes, 1375 NCBI Transcriptome Shotgun Assemblies (TSA; all those available from metazoa excluding mammals and holometabolous insects), and 70 transcriptomes  [89]  from diverse lineages of lophotrochozoa.
SECIS prediction and phylogeny We used the program SECISearch3  [93]  to detect potential SECIS elements in SelenoP sequences. We considered regions of 3000 nucleotides downstream of each CDS (or shorter if reaching over the contig end), and we scanned them using all three methods implemented in SECISearch3. During our complex filtering procedure (explained above), some gene predictions were combined to form consensus entries that were longer than their individual components. In those cases, we assigned to each new consensus entry the SECIS set with the highest number of predicted SECIS elements, among the ones in its merged components. To predict the phylogenetic tree of SECIS elements, we produced a structural alignment using the program Cmalign from the Infernal package v1. 1. 1, using the SECIS model at the core of SECISearch3 as template.
Gradients were chased with cesium chloride, and fractions containing the monosomes were identified by OD reading at a wavelength of 256 nm. RPFs were isolated from tightly defined monosome fractions by TRIzol extraction. For RNA sequencing, polyA mRNA were isolated from the same samples using the Poly(A) Purist Mag kit (Ambion) and randomly fragmented using alkaline fragmentation buffer (2 mM EDTA, 10 mM Na 2 CO 3 , 90 mM NaHCO 3 ). Both RPFs and randomly fragmented PolyA were prepared following Ingolia's ribosome profiling library preparation protocol  [97] , omitting the experimental ribosomal RNA depletion step. The prepared cDNA libraries were sequenced by BGI Genomics Co. , Ltd. Shenzhen, China (www. bgi. com). The samples were pooled in one SE50 multiplex lane where 3GB of data were allocated for each ribosome profiling sample and 0.
Bioinformatic analysis of RNA-seq and riboseq Using Ribogalaxy  [98] , raw sequencing reads were pre-processed before alignment to the transcriptome. Oyster ribosomal RNA sequences 5. 8S (ENSRNA022717831), 5S (ENSRNA02271792), 28S (AB102757. 1), and 18S (ENSRNA022718259) were identified in silico using Bowtie version 1. 1. 1 [99]  and removed. The remaining reads were mapped with Bowtie to the oyster transcriptome  [61]  using the same parameters as trips-viz  [100]. Metagene analysis was performed to assess triplet periodicity and P-site offset as a quality control (Supplementary Note 8). For metagene analysis, start and stop codons in each transcript were obtained from the transcriptome assembly ORF annotation  [61]. Previously, we had identified transcripts corresponding to selenoproteins using Selenoprofiles  [88]  and manually selected a single transcript as a representative for each selenoprotein gene (Supplementary Table  1 ).
Read counts per selenoprotein gene were normalized by transcript length (kbp) per million mapped reads (RPKMs and RPFKMs, for RNA-seq and riboseq respectively) using the programs cuffquant and cuffnorm from the package cufflinks version 2. 2. 1  [101]. Genes with RPKM or RPFKM below 1. 0 in either selenium supplementation group (DIO. 2, SELENOH. 1, PSTK. 2, SBP2. 117) were discarded to avoid over interpretation in the differential expression analysis due to low coverage. Translation efficiency was computed as the ratio of RPFKM/RPKM for each gene. To calculate Sec URE  [67, 69] , we defined the 5 0 and 3 0 regions for each selenoprotein CDS based on the position of the Sec-UGA codon (UGA1 in case of SelenoP). URE was expressed as the ratio of RPF density in the region 3 0 of UGA to the region 5 0 of UGA (3 0 RPFKM/5 0 RPFKM).
Acknowledgments We thank  Sumangala Shetty  and  Mark Pinkerton  for advice;  Iarfhlaith Connellan  from  Cartron Point Shellfish Ltd. , Co. ; Clare  for providing the diploid oysters and microalgae;  Lizzie Nelson  of  Pacific Sea Foods, Quilcene, Washington , for a gift of oyster larvae;  Matt Ashton  of  Department of Natural Resources, Maryland , for a gift Elliptio complanata fresh water mussels;  Declan O'Sullivan of Collorus, Co. Kerry, Ireland , for provision of marine farmed Mytilus edulis mussels; and  Ximing Guo , for oyster larvae from the  Rutgers Haskin Shellfish Laboratory. M. M. was supported by  EMBO  short-term fellowship  ASTF 289-2014. The work was supported by the following grants:  NIH DK117149 ,  GM065204 , and  AG021518  (V. N. G. );  BIO2014-57291-R  from the  Spanish Ministry of Economy and Competitiveness  (R.
G. Riviere, C. Klopp, N. Ibouniyamine, A. Huvet, P. Boudry, P. Favrel, GigaTON: an extensive publicly searchable database providing a new reference transcriptome in the pacific oyster Crassostrea gigas, BMC Bioinformatics. 16 (2015) 401, https://doi. org/10. 1186/s12859-015-0833-4.
This is estimated from the amount of nitrogen adsorbed in relation to its pressure at the boiling point of liquid nitrogen and at normal atmospheric pressure. The SA 3100 BET (Beckman Coulter, Brea, CA, USA) measures the specific surface of granulated samples via gas adsorption using the Brunauer-Emmett-Teller (BET) method. In this so-called discrete method, the data points obtained and the gas pressures are balanced before the reading is recorded. The volume of adsorbed gas retained by the sample is calculated from the pressures recorded at each measuring point.
'Caméor'). (A) Developmental stages at which stresses were imposed. Control, plants were well watered under non-limiting S conditions; S-, plants were deprived of S from the mid-vegetative stage until harvest; WS, plants were subjected to water stress from flowering of the 2nd or 3rd reproductive node for 9 d, then re-watered for recovery; S-WS, plants were subjected to a combination of the two stresses. Developing seeds were collected at the end of the water-stress period for transcriptome analyses, and other plants were harvested at maturity for phenotyping and analyses of seed composition. (B) Leaf water potential measured at the end of the water-stress period. Data are means (±SE), n=4 plants. Different letters indicate significant differences as determined by ANOVA followed by a SNK test (P<0.
8%), 1% SDS, 0. 05% ammonium persulfate, 0. 05% TEMED; Electrophoresis buffer: 50 mM Trizma base, 380 mM glycine, 0. 1% (g/v) SDS]. After staining with Coomassie Blue R250 (Bio-Rad), gels were scanned using an Odyssey Infrared Imaging System (LI-COR) with an intensity of 7. 5 and a resolution of 84 μm. Protein band detection and quantification were performed using Phoretix 1D (v11. 2, TotalLab Limited). In each well, the quantitative data were normalized by dividing the volume of each protein band by the total band volume, and the molecular weight (kDa) of each band was calculated using a low-range protein ladder (Bio-Rad).