dataesr/scientific-paragraphs-categorization huggingface dataset:
physics. science. az-subdomain for the Institute of Physics of ANAS. This subdomain and the subdomains to be generated in the future are coordinated by administration of the Institute of Physics. nuclear. physics. science. az-subdomain for the Laboratory of nuclear studies to be established at the Institute of Physics. uranium. nuclear. physics. science. az-subdomain to be developed to store the data about the uranium. Obviously, if necessary, the administration of this subdomain may be assigned to any applicable department. According to this standard, the number of ASCII characters is 37, and there are uppercase and lowercase Latin letters, figures from 0 to 9 and the symbol "-"  [6]. Nationalization of the domain names is the technical solution converting the domain names written in the national languages into ASCII text format to be registered with the DNS servers.
ICANN created a policy for the use of IDNA in 2003 on the basis of this system and in the same year registered a domain name. jp and some domain names in the subdomains of. infoin March 2004. Developed policy was improved in November 2004 and in October 2009,ICANNadopted an order for the creation of top-level domains in native language scripts. Thus, for the first time, Egypt, Saudi Arabia and Russia registered their country code domains in national scripts in ccTLDDNS servers root zone. Currently,22 out of top-level domain names placed in the top-level domain zone and widely used are written in the alphabet used in different countries (Figure  4 ). Punycode is a way to represent Unicode with the limited character subset of ASCII supported by the DNS  [7]. For example, "München" (German language spelling of Munich) is encoded as "xn -mnchen-3YA" in ASCII characters through Punycode.
The two light bands correspond to d xy bands forming circular Fermi surface sheets (see the Supplementary Material for additional data close to Γ 005 and Γ 115 , as well as photon energy dependence of ARPES data in CaTiO 3 , to confirm their orbital and 2D characters). However, the dispersion of the heavy band is clearly different in SrTiO 3 and CaTiO 3. The rotation of the oxygen octahedra in CaTiO 3 breaks the cubic symmetry of the ideal perovskite lattice (SrTiO 3 ) and thus can result in the hybridization of orbitals of different azimuthal quantum numbers. The hybridization of the d xz and d yz bands in CaTiO 3 is evident from the star-shaped Fermi surface in Fig. 1 (a), which can be understood as resulting from the hybridization of the two elliptic Fermi surface sheets in Fig. 1(e) , and is further supported by the non-parabolic dispersion as well as the light polarization dependence of the heavy band in Figs.
Our reference system is the 2DES in cubic TABLE I. Relationships between crystal structure and electronic properties of the 2DES at the surface of CaTiO3, SrTiO3 and BaTiO3. The Goldschmidt's tolerance factor and the lattice symmetry are given in the first two rows. The effective masses m (in units of the free electron mass me) of the dxy and dxz,dyz bands are given in the next two rows. The subsequent four rows give the bottom energies of the Et 2g bands together with the energy difference between the two dxy subbands, ∆E dxy. The last row gives the electron density n2D of the 2DES. All data correspond to the maximal electron density observed for each of the 2DES.
Simultaneously, positions of all atoms were optimized so as to make all forces less than 0. 01 eV/ Å. With these settings a (6 × 6 × 4) Γcentered Monkhorst Pack sampling of the bulk Brillouin zone assures a convergence of calculated energy differences to 0. 02 eV/CaTiO 3 , and of lattice parameters to less than 0. 01 Å. We have done some calculations using the Wien2k package  [50] , in order to double check our results. In particular, charge density plot showed in Fig. 2(c ) of the main text were done using this code.
We thank  Cédric Baumier  for help during ARPES experiments, and  Marcelo Rozenberg  for discussions. This work was supported by public grants from the  French National Research Agency (ANR) , project  LACUNES  No  ANR-13-BS04-0006-01 , the  "Laboratoire d'Excellence Physique Atomes Lumière Matière" (LabEx PALM projects ELECTROX  and  2DEGS2USE ) overseen by the  ANR  as part of the  "Investissements d'Avenir" program  (reference:  ANR-10-LABX-0039 ), and the CNRS-CONICET 2015-2016 collaborative project  AMODOX  (project number:  254274 ). T. C. R. thanks funding from the  RTRA-Triangle de la Physique  (project  PEGASOS ). R. W. acknowledges support from  CONICET  (grant No. PIP 114-201101-00376 ), and  ANPCyT  (grant No. PICT-2012-0609 ). M. G. and A. F. S. -S. acknowledge the support received from the  Institut Universitaire de France.
Metabolic pathways prediction To identify conserved or non-conserved metabolic pathways, we used three different datasets, consisting of (1) the closely related L. lactis NCDO 2118, L. lactis KF147 and L. lactis IL1403, (2) all strains from L. lactis subsp. lactis and L. lactis subsp. cremoris (non-pathogenic dataset), and (3) all strains from this study (including L. garviae). The number of metabolic pathways harbored by each genome varies from 148 to 206, with a general mean of ~183 pathways. Both L. garvieae strains contained 148 metabolic pathways, L. lactis subsp. lactis showed an average of ~192 metabolic pathways, and L. lactis subsp. cremoris showed ~186 pathways. The main differences were that the strain L. lactis NCDO 2118 contains more peptidoglycan biosynthesis pathways than L.
Phage 5, predicted as incomplete in the reference genome, was absent in L. lactis IO-1, L. cremoris KW2, L. cremoris UC5099 and partially present in both L. garvieae strains. ) was added to root the tree. The species in comparison are distributed from 1 to 17 in the same order, both vertically and horizontally. The numbers in the heatmap show the percentage of similarity between the species, varying from yellow (low similarity) to green (high similarity), or from 40% to 100%, respectively. The heatmap and the phylogenetic tree were created with the software Gegenees and Mega (Neighbor-Joining method with 1000 bootstraps replicates), respectively. https://doi. org/10. 1371/journal. pone. 0175116. g001 In the GIPSy predictions, we identified 9 Genomic Islands (GEIs), 5 Metabolic Islands (MIs), 4 Symbiotic Islands (SIs) and 3 Miscellaneous Islands (MSIs), which were predicted as harboring both metabolic and symbiotic related factors.
However, the amino acid sequence predicted from this region only presents similarity to a hypothetical protein. Lactococcin A is a class IId, non-pediocin-like, single-peptide bacteriocin normally produced by strains of L. lactis. Four genes are responsible for the biosynthesis of lactococcin: the lactococcin-A coding gene, one immunity gene and the dedicated ABC transporter system along with its accessory protein. L. lactis NCDO 2118 harbors an immunity protein (NCDO2118_1767) and lactococcin-A (NCDO2118_1768). As for the class III prediction, the predicted putative bacteriocin is located upstream of two hypothetical proteins (NCDO2118_2258 and NCDO2118_2259); however, little is known about the organization of the gene cluster of class III bacteriocins  [45] , and the putative bacteriocin predicted by BAGEL only presents similarity to hypothetical proteins in GENBANK.
Moreover, an additional bacteriocin-coding gene was harbored by GEI 9 (S2 Table ), which was not predicted by BAGEL. Through blast analyses, we found a significant amino acid similarity, with identities varying from 76% to 98%, between this gene and a bacteriocin-coding gene from other L. lactis in the UNIPROT and NCBI BLAST databases. However, many of the genes were also described as hypothetical proteins. In addition, we also searched for other genes that could possibly play a role in the competitive exclusion of other bacteria. A Lyzozyme M1 and a Macrolide biosynthetic protein encoding genes were also included in S3 Table after manual curation in the L. lactis NCDO 2118 genome. In the present study, a deferred agar spot assay was used for the initial determination of antagonistic activity via diffusible compound(s) produced by L.
In silico identification of putatively secreted proteins Here, we strove to predict genes encoding secreted proteins from L. lactis NCDO 2118 that are absent from the genomes of the strains L. lactis IL1403 and L. cremoris MG1363, as the secreted proteins of L. lactis NCDO 2118 are possibly responsible for the immunomodulatory effects of this transient bacterium inside the host  [18]. To predict the secreted proteins, we used the software SurfG+, which classifies the proteins using an identification approach based on the presence/absence of signal peptides, signal retention and transmembrane helix  [46] , which are correlated with the cell wall thickness of the bacteria. To determine the cell wall thickness, we made photomicrographs of L. lactis NCDO 2118 (Fig 4 ); the cell wall was measured more than 270 times, showing an average size of ~20 nm, and this value was used to determine the motifs.
Genome sequences The genome sequences of L. lactis NCDO 2118  [24]  and 15 other strains of Lactococcus were retrieved from the GENBANK dataset of NCBI (Table  1 ). Briefly, the dataset is composed of 8 strains of Lactococcus lactis subsp. lactis, 2 of which were isolated from legumes (L. lactis NCDO 2118 and Lactococcus lactis subsp. lactis KF147), 6 Lactococcus lactis subsp. cremoris isolated from dairy or other fermented foods, and 2 Lactococcus garvieae isolated from diseased fish. L. garvieae was added to the analyses because it is a closely related pathogenic species. S. thermophilus LMD-9 was used as an outgroup to root the phylogenetic tree. Only complete genomes were used to avoid bias.
Genome synteny. The genome synteny analyses were performed using Mauve, with the "progressiveMauve" option and all genome sequences in the. fna format. Mauve predicts gene synteny by merging locally collinear blocks of conserved genome orthologous regions and ordering them according to a reference genome  [89]. Genome plasticity. The genome plasticity analyses were performed by searching for horizontally acquired regions such as genomic islands and phage sequences. The genomic islands were searched using the software GIPSy: Genomic Island Prediction Software  [90] , which updates the methodology of the software PIPS: Pathogenicity Island Prediction Software. Briefly, GIPSy performs the prediction of four different classes of genomic islands: Pathogenicity Islands, Resistance Islands, Metabolic Islands and Symbiotic Islands.
In this work, we searched for metabolic and symbiotic islands in the genome of L. lactis NCDO 2118 using Lactococcus lactis subsp. cremoris MG1363 and Lactococcus garviae Lg2 genomes as subjects. After, we consolidated and manually curated the results. The choice of metabolic and symbiotic islands was made based on the lifestyle of L. lactis NCDO 2118, a strain isolated from vegetables, and its metabolic importance. All the analyses were performed using GENBANK files and default parameters. The results were exported in tabulated format and used in BRIG (Blast Ring Image Generator) to generate circular genome comparative views  [91]. Finally, the prophage prediction was performed using the GENBANK file and the software Phast  [92] , and the results were exported in table format and used as input in BRIG.
Bacteriocin prediction. The bacteriocin prediction was performed in BAGEL software using the. fna file from L. lactis NCDO 2118. Briefly, the software works with a curated dataset of bacteriocins, in which the input data are evaluated based on a Hidden Markov Model. The genetic information is analyzed based on combinations of PFAM domains  [42]. For the putative bacteriocin predicted on L. lactis NCDO 2118 (NCDO2118_1768), we used the Transporter Classification Database (TCDB)  [93]  with an e-value of e-07. Circular comparison map of genomic sequences. To create circular genome comparisons, we used the software BRIG and all genome sequences in the. fna format; we created the figure with L. lactis NCDO 2118 as reference strain. Additionally, we added the coordinates of the genomic islands and phage regions to the figure to visualize genome plasticity events.
Finally, all genomes underwent BLAST analyses against the reference strain to create the circular comparison map. Metabolic pathway prediction. A genome sequence in. fasta and a genome annotation in the. gbk format were used for reconstructing the Lactococcus species metabolic pathways. Posteriorly, the Pathway/Genome Databases (PGDB) for each of the 16 strains were computationally predicted using Pathway Tools software version 16. 5  [94] , developed by SRI International. The MetaCyc, a highly curated and non-redundant reference database of small-molecule metabolism, was used as a reference database for the PathoLogic component of the Pathway Tools software  [95]. The metabolic pathways of L. lactis NCDO 2118 were used as a reference for the comparative analysis using the following comparisons: i) L.
lactis NCDO 2118, L. lactis KF147 and Lactococcus lactis subsp. lactis IL1403, ii) non-pathogenic strains of L. lactis (L. lactis subsp. lactis and cremoris), and iii) all strains in this study. Identification of the secretome. The prediction of the putative subcellular localizations of L. lactis NCDO 2118 proteins was performed in silico using SurfG+. This software contains such tools as SignalP, LipoP and TMHMM for the identification of motifs  [46]. Interestingly, SurfG+ uses the size of the membrane wall to better differentiate the membrane (MEM) and potentially surface exposed (PSE) proteins. Here, the measurements of the membrane wall were performed with electron microscopy with EM10A equipment (Zeiss), as previously described  [96]. L. lactis NCDO 2118 was grown at 30˚C for 18 h in M17 medium (Difco) containing 0.
lactis NCDO 2118 wall was determined from the image analysis micrograph in ImageJ software (available at imagej. nih. gov/ij/). To measure the wall, we used at least five micrographs of L. lactis NCDO 2118 with magnifications of 50,000 and 100,000 times. We calculated the mean size of the cell walls, and the average number of amino acids for the obtained wall thickness was ~55 amino acids. This value was added to the SurfG+ software together with the. fasta sequence of amino acids (. faa) exported from the strain of interest. After this process, we used OrthoMCL tool to predict the orthologous and paralogous genes between L. lactis NCDO 2118 and L. lactis IL1403.
Bacterial growth was evaluated by inoculating MRS broth with 2% v/v of control cells in saline and artificial gastric juice-treated cells onto microplates in triplicate, before incubating them in a Microplate Spectrophotometer System SpectraMax 340 (Molecular Devices Inc. , Sunnyvale, CA, USA) at 37˚C for 18 h. The OD 620nm (optic density) was recorded at 30 min intervals. The percentage of growth inhibition was calculated as (1 -areaAGJ/areaCT) x 100, where area-AGJ and areaCT are the areas under the growth curve for the simulated gastric juice and control, respectively. The total area under the curve was calculated by definite integration using the OriginPro 8. 5 program (OriginLab Corporation, Northampton, MA, USA). The results were based on the average of three independent assays. Susceptibility to bile salts.
μL -  ) delivered through the NanoLockSpray source reference sprayer. The multiplexed data-independent (DIA) scanning with additional specificity and selectivity for non-linear 'T-wave' ion mobility (HDMS E ) experiments were performed using a Synapt G2-S HDMS mass spectrometer (Waters, Manchester, UK). Following the identification of proteins, the quantitative data were packaged using dedicated algorithms  [104; 105]  and searching against a database with default parameters to account for ions  [106]. The databases used were reversed "on-the fly" during the database queries and appended to the original database to assess the false positive rate during identification. For proper spectra processing and database searching conditions, the ProteinLynxGlobalServer v. 2. 5. 2 (PLGS) with Identity E and Expression E informatics v.
2. 5. 2 (Waters) was used. Uni-ProtKB with manually reviewed annotations was used, and the search conditions were based on taxonomy (L. lactis). The maximum allowed missed cleavages by trypsin were up to one, variable modifications by carbamidomethyl (C), acetyl N-terminal, phosphoryl (STY) and oxidation (M) were allowed, and a peptide mass tolerance value of 10 ppm was used  [107]. The collected proteins were organized by the PLGS Expression E tool algorithm into a statistically significant list that corresponded to higher or lower regulation ratios among the different groups. For protein quantification, the PLGS v2. 5. 2 software was used with the Identity E algorithm using the Hi3 methodology. The search threshold to accept each spectrum was the default value in the program with a false positive value of 4%.
Statistical analysis The experimental data are expressed as the mean ± standard deviation (SD). Data transformation was applied if necessary for the statistical analysis. The results of the tannin effects were analyzed by one-way analysis of variance (ANOVA) followed by the Newman-Keuls post-hoc multiple-range test, whenever there were statistically significant differences between the means (at P < 0. 05, or P < 0. 10). Simple linear regression was applied to determine the relationship between the fish parameters (dependent variable) and included dietary TA levels (independent variable). Statistical analysis was performed using Statistica, version 6 software (StatSoft France, Maisons-Alfort, France).
Acknowledgements This work was conducted within the  Aquaculture research programs 'Quality of process and products'  ( Ifremer Plouzané, France ) and ' Functional food and health of fish' (Inra UR NuMeA Saint-Pée-sur-Nivelle, France ). The authors thank  F. J. Gatesoupe  ( Ifremer, Plouzané; Inra UR NuMeA ), the two anonymous reviewers and  W. Hempel , for critical reading and comments, and amendments to the article that have been appreciable, the  Bibliothèque La Pérouse  and  S. Gros  ( Ifremer, Plouzané ), for resource collection and previous assistance on a contribution to a conference. This research was not supported by a specific grant.
EXPERIMENTAL MODEL AND SUBJECT DETAILS Tumor and whole blood samples were obtained from patients at contributing centers, with informed consent from their local Institutional Review Boards (IRBs, see below). Biospecimens were processed centrally, and DNA and RNA were distributed to TCGA analysis centers. In total, 80 evaluable primary tumors with associated clinicopathologic data were assayed on at least one molecular-profiling platform. TCGA Project Management has collected necessary human subjects documentation to ensure the project complies with 45-CFR-46 (the "Common Rule"). The program has obtained documentation from every contributing clinical site to verify that IRB approval has been obtained to participate in TCGA. Such documented approval may include one or more of the following:.
Biospecimen and Clinical Data Processing Patient and Sample Characteristics: Eight academic medical centers provided primary UM tumor samples, matched blood for germline DNA, and clinicopathologic data from 121 enucleated UM patients under IRB-approved protocols. 80 primary UM from six centers passed all quality-control measures and had data from all molecular analytic platforms except reverse phase protein array (RPPA), for which data were derived from 12 primary UM. Eleven of these 12 cases had BAP1, SF3B1 or EIF1AX mutations; V4-A9EH did not, and was removed from further analysis (Table  S1 ). Patients who had been treated prior to tissue procurement with systemic chemotherapy or focal radiotherapy were excluded. Enrollment criteria required tumors to consist of at least 200 mg of fresh frozen tissue, and DNA-matched normal (blood) controls to be available Clinical Outcome Analysis: All clinical outcome events were calculated using the time interval in days from the date of the pathologic diagnosis of primary tumor to either the date of documented metastatic disease (UM metastasis) or death (UM survival) or last follow-up, censored to 5 years.
At the BC Cancer Agency's Genome Sciences Centre (BCGSC), Strelka (v1. 0. 6)  (Saunders et al. , 2012)  was used to identify sSNVs and sINDELs (up to ~22 bp long) from the exome sequencing data for tumors and blood normals. All parameters were set to defaults, with the exception of "isSkipDepthFilters", which was set to 1 in order to skip depth filtration, given the higher coverage in exome datasets. The variants were subsequently annotated using SnpEff  (Cingolani et al. , 2012) , and the COSMIC (v61)  (Forbes et al. , 2010)  and dbSNP (v137)  (Smigielski et al. , 2000)  databases. Calls generated at the Broad Institute were merged with the calls from BCM, UCSC, and BCGSC. Calls were included in a consensus set if they were called by either the Broad or by two or more of the four participating centers.
Each of these tests returns a p-value for every gene, which are Fishercombined and false discovery rate (FDR)-corrected via Benjamini-Hochberg. Genes were considered "significant" if their FDR value was below 0. 1. Validation Analysis: Calls in significantly mutated genes were subject to Fluidigm validation. Samples were initially aligned with BWA-backtrack, but an inability to properly align reads spanning long deletions led to realigning all samples with NovoAlign (www. novocraft. com), which properly gapped these reads. Mutations were validated by comparing allelic fractions in the whole exome alignments with allelic fractions in the validation alignments; mutations that fell outside of the expected beta-binomial distribution of deviation were rejected. In addition, recurrent sites found in the deep coverage validation data but not present in the lower coverage exomes were added to the final set of calls.
nci. nih. gov/). Quantification of genes, transcripts, exons and junctions can also be found at the GDC Data Portal. Unsupervised Clustering: For clustering, a set of 1,981 genes that were both highly expressed and had highly variable expression values were identified. The 0. 75 quantile of mean(RSEM) values was used as a threshold for highly expressed genes, while the 0. 9 quantile of variance(RSEM) values was used as a threshold to identify genes with highly variable expression values. After median centering the log10(RSEM+1) values by gene, consensus clustering was applied using the ConsensusClusterPlus R package  (Wilkerson and Hayes, 2010)  with partitioning around medoids (PAM), a Spearman correlation-based distance, and 10,000 subsamples with a 0. 85 random gene fraction. Output from ConsensusClusterPlus along with gene expression heatmaps, principal component analysis, and silhouette plots suggested four expression subtypes: Cluster 1 (n = 22), Cluster 2 (n = 21), Cluster 3 (n = 15), and Cluster 4 (n = 22) (Figure  3A ).
We used two algorithms to identify gene fusions: MapSplice  (Wang et al. , 2010)  and PRADA  (Torres-Garcia et al. , 2014). PRADA uses BWA  (Li and Durbin, 2010)  to extract all best alignments per read from a dual (genome and transcriptome) reference file. After this initial mapping, the alignment coordinates of reads that mapped to the transcriptome are transformed into coordinates on the genome. Mapped reads whose best alignments have multiple genomic coordinates are removed. Quality scores are recalibrated using the Genome Analysis Toolkit (Van der  Auwera et al. , 2013). Index files are generated using Samtools  (Li et al. , 2009)  and duplicate reads are flagged using Picard. The PRADA fusion module detects fusion transcripts by identifying discordant read pairs and junction-spanning reads.