dataesr/scientific-paragraphs-categorization huggingface dataset:
MDS is funded by a research grant ( 13261 ) from the  Villum foundation. Parts of this project were conducted by the  Australian Research Council Centre of Excellence for Allsky Astro-physics (CAASTRO) , through project number  CE110001020. EYH and CA acknowledge the support provided by the  National Science Foundation  under grant No. AST-1613472. ZKR acknowledges support from  European Research Council   Consolidator Grant   647208. This research was supported by the  Australian Research Council   Centre of Excellence for All Sky Astrophysics in 3 Dimensions  ( ASTRO 3D ), through project number  CE170100013. This research has made use of data, software and/or web tools obtained from the  High Energy Astrophysics Science Archive Research Center (HEASARC) , a service of the  Astrophysics Science Division at NASA/GSFC  and of the  Smithsonian Astrophysical Observatory's High Energy Astrophysics Division.
This publication makes use of data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation. We thank the  Swift ToO team  for executing of our observations. Pan-STARRS is supported by the  University of Hawaii  and the  NASA's Planetary Defense Office  under Grant no. NNX14AM74G. GALEX (Galaxy Evolution Explorer) is a NASA Small Explorer, launched in April 2003. We gratefully acknowledge  NASA 's support for construction, operation, and science analysis of the GALEX mission, developed in cooperation with the  Centre National d'Etudes Spatiales of France  and the  Korean Ministry of Science and Technology.
1), the phylogenetically closest bacterial species with standing in nomenclature (Fig. 1 ). Its 16S rRNA sequence was deposited in GenBank under accession number JX424768. As recommended by Stackebrandt and Ebers  [6] , this value was lower than the 98. 7% 16S rRNA gene sequence threshold to delineate a new species without carrying out DNA-DNA hybridization, and a new species was thus identified. The spectrum of strain GD6 was added to our matrix-assisted desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) database. The stool sample was diluted in phosphate-buffered saline (Life Technologies, Carlsbad, CA, USA). Obtained inoculum (100 μL) was incubated for 24 to 48 hours on 5% sheep's blood-enriched Columbia agar (bioMérieux) at 37°C. Growth was tested under aerobic and anaerobic conditions using AnaeroGen Compact (bioMérieux).
Colonies obtained were isolated on 5% sheep's blood-enriched Columbia agar and were identified by MALDI- TOF MS. MALDI-TOF MS identification, measurement and analysis were performed as previously described  [7]  using a Microflex spectrometer (Bruker Daltonics, Bremen, Germany). No significant MALDI-TOF MS score was obtained for strain GD6 against the Bruker database, suggesting that our isolate was not a member of a known species. We added the spectrum from strain GD6 to our database. Genomic DNA of strain GD6 was sequenced using MiSeq Technology (Illumina, San Diego, CA, USA) with the mate-pair strategy. The assembly was performed using the gsAssembler from Roche (Basel, Switzerland) with 90% identity and 40 bp as overlap. It led to 11 scaffolds and 564 large contigs (>1500 bp), generating a genome size of 6.
71 Mb. Genome was annotated by RAST  [8]. The predicted bacterial protein sequences were searched against the GenBank database and the Clusters of Orthologous Groups (COGs) databases using BLASTp (E value 1e-03, coverage 0. 7, identity percentage 30%). The tRNAScanSE tool  [9]  was used to find tRNA genes, whereas ribosomal RNAs were found by RNAmmer  [10]. The resistome was analysed with the ARG-ANNOT database  [11]. The exhaustive bacteriocin database available in our laboratories (Bacteriocins of the Unité des Maladies Infectieuses et Tropicales Emergentes (URMITE); http://drissifatima. wixsite. com/bacteriocins) was performed by collecting all currently available sequences from the databases and from the National Center for Biotechnology Information. Protein sequences from this database allowed putative bacteriocins from human gut microbiota to be identified using BLASTp methodology  [12].
NMNI discriminating genes with large size using a database realized in our laboratory; predicted proteins were compared against nonredundant GenBank database using BLASTp and finally examined using antiSMASH  [15]. PHAST was used to identify phage sequences  [13]. Phylogenetic relationships with closely related species were determined by MEGA6. The evolutionary history was concluded by using the maximum likelihood method based on the JTT matrix-based model. We compared the genome sequence of Paenibacillus ihuae strain GD6 with those of Paenibacillus graminis strain RSA19 T (NZ_ASSG00000000. 1), Paenibacillus polymyxa strain DSM 365 T (NZ_JMIQ00000000. 1), Paenibacillus massiliensis strain DSM 16942 T (NZ_ARIL00000000. 1), Paenibacillus typhae strain CGMCC 1. 11012 T (NZ_FNDX00000000. 1) and Paenibacillus borealis strain DSM 13188 T (NZ_CP009285.
Cells of P. ihuae strain GD6 were resistant to oxacillin and metronidazole but were susceptible to other antibiotics. The analysis of the genome showed the absence of antibiotic resistance genes but the presence of a NRPS. The genome of Paenibacillus ihuae strain GD6 has been submitted to the European Bioinformatics Institute (EBI) database under bioproject ID PRJEB549 with GenBank accession number CTED01000000 and 16S RNA accession number JX424768.
Acknowledgements We thank  L. Hadjadj  for technical assistance,  Aix-Marseille Université IRD, APHM, MEPHI, IHU Méditerranée Infection, Marseille, France. This project was funded by the  French Centre National de la Recherche Scientifique and Infectiopole Sud Foundation. This work was also supported by the  French government  under the 'Investissements d'avenir' (Investments for the Future) programme managed by the  Agence Nationale de la Recherche  (Méditerranée Infection  10-IAHU-03 ).
Behavioral analysis For every participant, behavioral responses (i. e. , whether the target word contained a short or a long vowel) were registered for both Fast and Slow conditions. The perceptual bias was calculated as the difference in the proportion of long vowel (/a:/) responses between the Fast and the Slow conditions. Statistical analysis was performed with MATLAB R2015a. Repeated-measures ANOVA were performed using the proportion of long vowel reports and the perceptual bias as dependent variables and factors of Speech rate (Fast, Slow) and second formant frequency F2 (25%, 50%, 75% long vowel reports F2s).
MEG analysis MEG recordings were collected using a 275-channel axial gradiometer CTF MEG system at a sampling rate of 1. 2 kHz. For source reconstruction analysis, structural magnetic resonance imaging (MRI) scans were obtained from all subjects using either a 1. 5 T Siemens Magnetom Avanto system or a 3 T Siemens Skyra system. MEG data was analyzed using the Fieldtrip software  [60]. MEG recordings were epoched at two distinct windows (Carrier and Target). Epochs for the Carrier window comprised the MEG recordings at the start of the sentence up to the change in speech rate (fixed 3. 55 s duration for the Slow rate condition, 2. 0 s for the Fast rate condition). Epochs in the Target window started after the change in speech rate and comprised the MEG recordings during the presentation of the last three words of the sentence (''Het woordje [target word]'') up to 500 ms before the response screen (the window was of 1.
Hence, the experiment was specifically designed to test the effect of speech rate on sustained entrainment frequency powerand not phase, as average time course or phase-locking across trials/subjects would potentially cancel out the effect due to nonphase-locked nature of the stimuli. To do so, the cross-spectral density data structure was computed using Fast Fourier transform (FFT) with Hanning tapering performed at 3 Hz and at 5. 5 Hz for both Carrier and Target windows. Epochs in the target and carrier windows were analyzed separately to prevent leakage of oscillatory components from the carrier to the target window. For the Carrier window, the first 500 ms of the epochs were removed to exclude the evoked response to the onset of the sentence and ensure the measure of the entrainment regime.
The data was zero-padded up to 4. 0 s for both conditions to match in FFT resolution. During the target window, the data was zero-padded up to 2. 0 s so as to obtain more accurate amplitude estimates of the resolvable 3 Hz and 5. 5 Hz signals components. The co-registration of MEG data with the individual anatomical MRI was performed via the realignment of the fiducial points (nasion, left and right pre-auricular points). Lead fields were constructed using a single shell head model based on the individual anatomical MRI. Each brain volume was divided into grid points of 1 cm voxel resolution, and warped to a template MNI brain. For each grid point the lead field matrix was calculated. Source reconstruction was then performed using a common spatial filter obtained from beaming data from both Slow and Fast speech rate conditions.
The Entrainment Index (EI) was calculated based on the source reconstructed power for each grid point according to the formula: EI = Power Slow 3 Hz ð Þ Power Fast 3 Hz ð Þ $ Power Fast 5:5 Hz ð Þ Power Slow 5:5 Hz ð Þ Sources with significant EI > 1 were estimated using cluster-based permutation statistics across subjects  [65]. First, a ''null hypothesis'' source dataset was generated by setting the EI values to 1. Pairwise t tests were then computed for each grid point between the experimental EI source data to the generated ''null hypothesis'' source dataset. Grid points with a p value associated to the t test of 5% or lower were selected as cluster candidates. The sum of the t-values within a cluster was used as the cluster-level statistic. The.
ACKNOWLEDGMENTS We would like to thank  Annelies van Wijngaarden  for the recordings of her voice and  Anne van Hoek  for help with pretesting. This research was supported by the  Netherlands Organisation for Scientific Research (NWO) Gravitation  Grant  24. 001. 006  to the  Language in Interaction Consortium , the  NWO Spinoza Prize , the  Academy Professorship Award  of the  Royal Netherlands Academy of Arts and Sciences  to P. H. , the  James S. McDonnell Foundation  grant  220020448 , the  Welcome Trust  grant  207550 , and the  Royal Society Wolfson Research Merit Award  to O. J.
Deposited Data Stimulus generation code This paper; Donders Repository http://hdl. handle. net/11633/di. dccn. DSC_3011050. 03_094 Raw and analyzed data This paper; Donders Repository http://hdl. handle. net/11633/di. dccn. DSC_3011050. 03_094 Speech materials This paper; Donders Repository http://hdl. handle. net/11633/di. dccn. DSC_3011050. 03_094.
Genomic reads from two libraries were filtered and assembled with AllPathsLG  (Gnerre et al. , 2011). RNA-seq data were de novo assembled into consensus sequences using Rnnotator  (Martin et al. , 2010). Fungal genome was annotated using the JGI Annotation pipeline and made available via JGI fungal genome portal MycoCosm (http://genome. jgi. doe. gov/Sodal1; Grigoriev et al. , 2014) , and DDBJ/ENA/GenBank database. For extended methods, see Supporting Information Appendix S1.
| Proteinases Putative proteinases were found by two successive searches against merops databases  (Rawlings, Waller, Barrett, & Bateman, 2014). At first, predicted proteomes of 32 fungi were used as queries for blastp against full merops database (version 12) to remove false positives  (Rawlings & Morton, 2008). Then, the output was used as queries for blastp against a smaller merops Scan database of type domains. In both searches, e-value was set to 0. 0001. Secretory peptidases were predicted with TargetP 1. 1b under default settings  (Emanuelsson, Brunak, von Heijne, & Nielsen, 2007).
The technical triplicates were averaged and used for calculating C t difference relative to the lowest pH point. The ΔΔC t method was employed to calculate relative gene expression. qRT-PCR results were analysed by the bio-r ad cf x manager version 2. 0 software. Baseline threshold line was set arbitrarily at exponential phase of PCR (500 relative fluorescence units), and results were normalized to the act1 gene signal.
| Horizontal gene transfer detection The method described in Marcet-Houben and Gabaldón (  2010 ) was used for the detection of putative horizontal gene transfer (HGT) cases. Shortly, if protein sequences are present in a few fungi as well as in a high number of bacterial genomes, this gene is assumed to have undergone HGT. To identify these cases, we blasted (blastp, e-values 10 -5 , 10 -10 , 10 -15 , 10 -20 , 10 -30 , 10 -50 ) the predicted proteome of S. alkalinus against proteomes (248 fungal and 6,321 bacterial proteomes) obtained from the nonredundant database in NCBI as of April 2017. We tagged a given protein to be horizontally transferred if it fell in the quadrant limited by first 35-quantile for fungal occurrences and 100*SE from loess fit in bacterial distribution. Substantial intersection of the proteins that we obtained after analyses ran at various e-values strengthens our results.
DATA ACCE SS I B I LIT Y DNA sequences: Mycocosm: http://genome. jgi. doe. gov/Sodal1; Genbank: This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession NITX00000000. The version described in this paper is version NITX01000000. Phylogenetic analysis: TreeBASE #22172.
3. 3 | Carbohydrate-active enzymes in silico and in vitroFungi decompose complex organic carbon compounds externally and use it as an energy source. The majority are soil saprobes that degrade dead organic matter using a versatile arsenal of extracellular enzymes including glycoside hydrolases, carbohydrate esterases, polysaccharide lyases and several auxiliary enzymes, which are classified in the CAZy database(Lombard et al. , 2014)(http://www. cazy. org). Given the unique habitat of S. alkalinus, we compared its major enzyme sets with those of other fungal species. Figure2depicts the phylogenomic placement of S. alkalinus based on 1,347 single-copy orthologous genes, which confirms the affiliation of the fungus to the Plectosphaerellaceae. substantially fewer, about fifty per cent, CAZymes than its closest relatives.
■ MATERIALS AND METHODS ZnO Particles. Three types of zinc oxide (ZnO) particles were used: in-house synthesized ZnO nanoparticles and commercially acquired ZnO nanopowder (Sigma-Aldrich, <100 nm particle size, ∼80% Zn basis) and ZnO powder (Sigma-Aldrich, ACS reagent, ≥99. 0% (KT)). The in-house ZnO nanoparticles synthesis was carried out according to a modified method described by Sun et al. 27  as detailed in the Supporting Information section SI1. Particle Characterization. Particles morphology was analyzed by TEM (see section SI2 for details). The sizing of the in-house synthesized ZnO nanoparticles and commercially acquired ZnO nanopowder and ZnO powder was performed using ImageJ software (see section SI3 for details). The structure of ZnO particles was analyzed by XRD (see section SI4 for details) and their chemical composition by EDX (see section SI5).
Ownership Links We start our data collection by retrieving shareholder information for all (currently and formerly) listed companies around the world from Bureau van Dijk's Orbis database. We find 360,000 ownership links between a public company and a public (downstream) company. 4 Still, most of these ownership links are participation stakes held by financial institutions including insurance companies (45% of all of the above links) or mutual funds (25%) and these equity stakes are small with an average (median) of 4. 52% (0. 56%) of the equity. Some of the equity stakes in Orbis are not given in a numerical format, which is why we decode them following  Kalemli-Ozcan et al. (2015) : we replace a percentage with a leading "<", or ">" by the percentage after the symbol plus or minus 0. 1%; we eliminate possible signs that preceded percentages: "_", "?", or "Â"; the "WO" codes (wholly owned) are replaced by 98.
We correct the data for potential mistakes; e. g. we deleted the link-year observations prior to the IPO year, and after the delisting and full takeover year. IPO and delisting dates are collected from BvD Amadeus and Datastream. Takeovers dates are collected from BvD Zephyr as it has common identifiers with the Orbis database. We also retrieved all historical ownership links available in the Orbis Historical files related to companies involved in the sample of cross sectional links.
burgdorferi. Based upon patterns of lysine acetylation in other bacteria, we hypothesized that acetylation is used by B. burgdorferi to modulate the enzymatic activity of proteins required for central metabolism, transcription and translation. This strategy would allow these spirochetes to control energy flux via reversible PTM of proteins in an energy efficient manner. To test this concept, we determined a growthphase related acetylome in B. burgdorferi by using a high sensitivity mass spectrometry-based proteomic approach. Our data suggested that central metabolism and genetic information processing were the two main cellular processes affected by acetylation. We also demonstrated that acetylation directly modulated the activity of glycolytic enzymes using specific enzymatic assays. Additionally, using specific mutant strains deficient in acetate anabolism [B31-A3 ackA (Ac-P minus, acetyl-CoA minus) and B31-A3 pta (Ac-P plus, acetyl-CoA minus)], we were able to demonstrate that, unlike lysine acetylation in other bacteria, Ac-P accounts for 99% of the acetylation observed in B.
, 2003) , and UniProt (The UniProt Consortium, 2017) databases (Table  1 ). The acetylated proteins were involved in genetic information (18 proteins at ML vs. 25 at S), metabolism and transport (16 proteins at ML vs. 21 at S), protein folding and degradation (eight proteins regardless the growth phase), detoxification (ClpP2, TrxA, and SodA), outer surface protein (OspA, OspB, and OspC), motility and chemotaxis (FliN, CheD). The overall trend in Gene Ontology (GO) distribution of the acetylated proteins was similar to what has been observed in other bacteria. To understand the connection between all acetylated proteins, we constructed a protein interaction network map by using the STRING database  (Szklarczyk et al. , 2015)  (  Figures 4C, D ) with a confident score of 0. 7. This interaction map showed that 47 of the 52 acetylated proteins found at ML phase were network nodes and connected by 381 direct physical interactions (Figure  4C ) while 57 of the 64 acetylated proteins found at S phase were network nodes and connected by 502 direct physical interactions (Figure  4D ).
Database Search Raw data files were processed using Proteome Discoverer 1. 4 software (Thermo Scientific). Peak lists were searched using the MASCOT search software (Matrix Science) against the database B. burgdorferi. Database searches were performed with the following parameters: two missed trypsin cleavage sites allowed; variable modifications: carbamidomethylation on cysteine, oxidation on methionine, and lysine acetylation. The parent-ion and daughter-ion tolerances were 5 ppm and 0. 35 Da, respectively. False discovery rate (FDR) threshold for identifications was specified at 1% (for proteins and peptides). For each identification, we looked carefully at the ion score (above 19), the peptide rank, the daughter ions (b-and y-fragment ion series), and the expectation value (E-value, ≤0.
Bioinformatics Annotation of Lysine-Acetylated Peptides All identified acetylated peptides were compared to the UniProt database (The UniProt Consortium, 2017) using B. burgdorferi (strain ATCC 35210/B31/CIP 102532/DSM 4680), taxon identifier 224326 as reference. Proteins were classified by GO based on biological process, subcellular localization and molecular function using the UniProt database (The UniProt Consortium, 2017) and ERGO  (Overbeek et al. , 2003). The Kyoto Encyclopedia of Genes and Genomes (KEGG)  (Kanehisa and Goto, 2000; Tanabe and Kanehisa, 2012)  was used to annotate the metabolic pathways. A protein interaction network was generated by using the STRING database  (Szklarczyk et al. , 2015). All networks were made with a confidence score in the interactions of 0. 7 (high confidence).
Enzyme Modeling Domains (i. e. , active site) on proteins were defined by using the Prosite database  (Sigrist et al. , 2002 (Sigrist et al. , , 2005 (Sigrist et al. , , 2013;; Hulo et al. , 2008). The structures of LDH and GAPDH were predicted using I-TASSER  (Yang et al. , 2015). The correctness of the modeled structure was evaluated by a confidence score (Cscore)  (Zhang, 2008). C-scores are normally in the range of  [-5, 2]. A higher C-score indicates more confidence in the model, with a cutoff C-score > -1. 5 indicating models with a very high likelihood of correct topology  (Zhang, 2008). I-TASSER combines available state-of-the art threading programs and ranks the templates by sequence and structure based scores. Top templates from each threading program are then selected for further refinement.
| Field data acquisition Data on fecal microbiota and PHA response were collected during 2014 (late April -late June) from barn swallow nestlings (n = 58) distributed in 32 clutches (Czech Republic, 49° 4′ 7. 762″ N, 14° 42′ 36. 521″ E, Supporting Information Table  S1 ). Tissue thickness of the left wing web (patagium) of 12-day-old barn swallow nestlings was measured using a standard thickness gauge (Mitutoyo, Japan). Subsequently, the PHA solution (0. 10 mg of PHA-P dissolved in 20 μl of DPBS) was injected and the magnitude of the swelling reaction was measured after 24 hr. Both pre-and posttreatment tissue thickness measurements were performed three times by the same person (A. P. , accuracy ~0. 01 mm). Repeatability of these measurements was high (intraclass correlation coefficient = 0.
| Microbiome profiling and bioinformatic processing of 16S rRNA data Metagenomic DNA was isolated from fecal samples using PowerSoil Mo Bio kits (Qiagen). The V3-V4 region of 16S rRNA was amplified using S-D-Bact-0341-b-S-17 (CCTACGGGNGGCWGCAG) and S-D-Bact-0785-a-A-21 (GACTACHVGGGTATCTAATCC) primers  (Klindworth et al. , 2013) , tagged with 10 bp oligonucleotide indices for demultiplexing. Technical PCR duplicates were prepared for all samples in order to check for microbial profile consistency. Sequencing libraries were prepared using TruSeq Nano Kits and sequenced on Illumina Miseq using v3 chemistry. The resulting 300 bp long paired-end reads were merged using Pear  (Zhang, Kobert, Flouri, & Stamatakis, 2014)  and demultiplexed using Mothur  (Schloss et al. , 2009). Lotus pipeline  (Hildebrand, Tadeo, Voigt, Bork, & Raes, 2014)  was used for quality filtering (elimination of sequences, if average Q < 30 and if average Q within 50 bp sliding dropped below 25) and elimination of chimeric sequences.
Subsequently, UPARSE algorithm  (Edgar, 2013)  implemented in Lotus was used for clustering of resulting high-quality reads at 97% similarity threshold to operational taxonomic units (OTUs). Taxonomic assignment of representative sequences for each OTU was performed using RDP classifier and Green Genes database  (v. 13_5, DeSantis et al. , 2006)  as a reference. Representative sequences were aligned using PyNAST  (Caporaso et al. , 2010)  and a phylogenetic tree constructed using FastTree  (Price, Dehal, & Arkin, 2010). The OTU table, sample metadata, taxonomic annotations, and phylogenetic tree were stored as a phyloseq object  (McMurdie & Holmes, 2013)  for further analysis. OTUs not assigned to phylum level, or those classified as chloroplasts (1% and 8. 2% reads, respectively), were considered as sequencing artefacts and diet contaminants, respectively, and eliminated from all downstream analyses.