dataesr/scientific-paragraphs-categorization huggingface dataset:
Field Testing: Sensitivity and Specificity Field-testing of the HHAC was carried out with baboons experimentally infected with L. loa (n = 4) blood and L. loa infected individuals (n = 22) from Cameroon as preliminary 'proof of concept' study; parallel calibrated thick blood smears were used for comparison. As shown in Figure  4 , there was significant concordance (p,0. 0001; r c = 0. 89, CI (0. 78-0. 94)) between the counts obtained by the HHAC and those of the calibrated thickblood smear. As shown in Table  1 , for L. loa microfilaremia detection, the positive predictive values were 95% for mf densities. 5000 mf/ml and 80% for levels. 30,000 mf/ml. It should be noted that while the data obtained from the HHAC occurred within minutes of obtaining the blood, the time to result for the calibrated thick smear ranged from 4 hours to several days.
Toxicological data The substances in the mixture have already been evaluated from the toxicological point of view and they are authorised in the EU legislation for plastic in contact with food. For the purpose of this evaluation, no further toxicological data are necessary. The Panel took note of the opinion of the AFC Panel (EFSA, 2008) setting a TWI of 1 mg/kg bw /week for aluminium. Based on a conservative scenario of consumption of 1 kg of food per day containing aluminium migrated at the level of detection of the method used of 0. 07 mg/kg food, an adult of 60 kg body weight in one week time would be exposed to 0. 008 mg Al/kg bw/ per week, corresponding less than 1 % of the TWI set in 2008. Concerning magnesium, the Panel noted that magnesium occurs naturally in foods and is an authorised food supplement (Annex I of Regulation (EC) No 1925/2006 6  and Annex I of Directive 2002/46/EC 7  ).
Forward primers were labeled with fluorescent dyes (Applied Biosystems). PCR reactions were carried out in 2. 5 ml total volume containing 50-100 ng DNA, 0. 1-1. 0 mM of each primer set, 2x Qiagen Multiplex PCR (3 mM MgCl 2 , 6 U HotStarTaq DNA polymerase) and RNA-free water. DNA amplifications and PCR were performed on a GeneAmp PCR System 9700 (Applied Biosystems). The cycling program began with a polymerase activation step at 95uC for 15 min followed by 37 cycles of 94uC for 30 s, 59uC for 90 s and 72uC for 90 s, with a final extension at 72uC for 10 min. The PCR products were electrophoresed in a 3130x Genetic Analyzer (Applied Biosystems) and alleles scored using a GeneMapper Software v3. 7 (Applied Biosystems).
Statistical analyses At all stages, survival was estimated as a percentage of developing eggs over the total number of eggs. Survival rates were calculated relative to controls after adjustment of the latter to 100%. Survival rates were arcsin square roots transformed for comparison between groups by two-way ANOVA using Statistica (Version 7. 1). Data are presented as means 6 standard deviations (STD). Presence of null alleles in the PCR products was analyzed using Microchecker software version 2. 2. 3  [53].
NS4B AH1 from different hepaciviruses share conserved structural features To investigate the structural conservation of AH1, we compared two distantly related hepacivirus species, HCV and NPHV. As shown in the upper part of Figure  3 , there is only a low degree of aa conservation between the NS4B aa 1-40 segments from the HCV JFH1 strain and NPHV-B10-022  [3]. However, the latter   [66]  are shown (http://euhcvdb. ibcp. fr; [41] ). Genotype, GenBank accession number, and strain are indicated for each sequence. Amino acids are numbered with respect to NS4B (top row). The consensus sequence (top row) was deduced from the ClustalW multiple alignment of the indicated NS4B sequences  [43]. To highlight the aa variability at each position, aa identical to the consensus sequence are indicated by hyphens.
Consensus secondary structure predictions of NS4B from representative HCV strains (Second. struct. cons. ) are indicated as helical (h, blue) or undetermined (coil [c], orange). Predictions were made by using the web-based algorithms SOPM, HNNC, DSC, GOR IV, PHD, Predator and SIMPA96 available at the NPSA website (http://npsa-pbil. ibcp. fr; [42]  and refs. therein). NMR AH2 structure (bottom row) denotes the conformation of residues determined previously by nuclear magnetic resonance (PDB entry 2JXF;  [26] ). Residue conformations are indicated as helical (H) or undetermined (C). doi:10. 1371/journal. ppat. 1004501. g001 was strongly predicted to adopt an a-helical fold by secondary structure analyses performed as described in the Legend to Figure  1. Based on these observations, a-helix projections generated by Heliquest  [22]  were compared, as shown in the lower part of Figure  3.
Sequence analyses and structure predictions Sequence analyses were performed using the web-based tools available at the European HCV Database (http://euhcvdb. ibcp. fr/)  [41]  and the Network Protein Sequence Analysis (NPSA) website of the Institut de Biologie et Chimie des Proteines (IBCP) (http://npsa-pbil. ibcp. fr) [42]. Multiple sequence alignment and analyses of aa conservation were carried out with the ClustalW program using default parameters  [43]. HeliQuest was used for ahelix projections (www. heliquest. ipmc. cnrs. fr) [22].
Results from two independent experiments were pooled (n = 8 for each construct and time point). For statistical analyses, RLU measured at 48 h were compared to the 48-h values obtained with mutant K18A. *, P,0. 001. (B) Structure models of the different AH1 sequences were established by using Swiss-PdbViewer 4. 01 software  [69]  and drawn by using VMD 1. 9 software  [68]. doi:10. 1371/journal. ppat. 1004501. g006.
Images were acquired on a confocal laser scanning microscope with the same settings for each antibody and condition. Analogous results were obtained when polyclonal antibody Y-11 against the HA tag was used instead of monoclonal antibody HA-7. (B) Histogram of the fluorescence intensity ratios between selective and total membrane permeabilization conditions. Fluorescence intensities in 10-60 images for each condition were determined by using ImageJ software  [70]. (C) H7-T7-IZ cells were transfected with a CMV promotor-driven core-E1-E2-p7 expression construct or T7 RNA polymerase-driven N3-5B polyprotein expression constructs harboring the different mutations in HA-tagged NS4B, followed by selective membrane permeabilization and immunofluorescence microscopy using the same antibodies as in panel A.
Peptide synthesis and purification Peptide NS4B  from the HCV strain JFH-1 (accession number AB047639; aa sequence reported in Fig. 2 ) was synthesized on a Milligen 9050 apparatus, employing N-[9-fluorenyl]methoxycarbonyl (Fmoc) chemistry. The peptide was highly purified by reversed-phase high-performance liquid chromatography on a Nucleosil C18 column (120 A ˚, 5 mm, 250 mm) using a water-acetonitrile gradient containing 0. 1% trifluoroacetic acid. The peptide was eluted as a single peak at 65% acetonitrile and identified by mass spectroscopy at its expected molecular mass.
Circular dichroism Far UV CD spectra were recorded on a Chirascan spectrometer (Applied Photophysics) calibrated with 1S-(+)-10-camphorsulfonic acid. Measurements were carried out at 298 K in a 0. 1-cm path length quartz cuvette. Spectra were measured in a 180-260 nm wavelength range with an increment of 0. 2 nm, bandpass of 0. 5 nm and integration time of 1 s. Spectra were processed, baseline corrected, smoothed and converted with the Chirascan software. Spectral units were expressed as the mean molar ellipticity per residue by using the peptide concentration determined knowing the weighted NS4B  peptide used to prepare the NMR sample. Estimation of the secondary structure content was carried out on the DICHROWEB server (http://www. cryst. bbk. ac. uk/cdweb/)  [58].
NMR spectroscopy Purified NS4B  peptide was dissolved in a mixture of 50% TFE-d 2 (. 99%) in H 2 O (v/v), and 2,2-dimethyl-2-silapentane-5sulfonate was added to the NMR samples as an internal 1 H chemical shift reference. Multidimensional experiments were performed at 25uC on a Bruker Avance 500 MHz spectrometer using standard homonuclear pulse sequences, including nuclear Overhauser enhancement spectroscopy (NOESY) (mixing times between 100 and 250 ms) and clean total correlation spectroscopy (TOCSY) (isotropic mixing time of 80 ms), as detailed previously  [59, 60]. Water suppression was achieved by pre-saturation. Bruker Topspin software was used to process all data and Sparky was used for spectral analysis (http://www. cgl. ucsf. edu/home/sparky/). Intra-residue backbone resonances and aliphatic side chains were identified from homonuclear 1 H TOCSY experiments and confirmed with 1 H- 13 C heteronuclear single quantum correlation (HSQC) experiments in 13 C natural abundance.
Additionally, dihedral angle constraints calculated with Talos  [63]  from 1 H and 13 C chemical shifts were introduced. Threedimensional structures were generated from NOE distances with the standard torsion angle molecular dynamics protocol in the XPLOR-NIH 2. 30 program  [64]  using the standard force fields and default parameter sets. A set of 50 structures was initially calculated to widely sample the conformational space, and the structures of low energy with no distance restraint violations (. 0. 5 A ˚) were retained. The selected structures were compared by pairwise root-mean square deviation (RMSD) over the backbone atom coordinates (N, Ca and C9). Local analogies were analyzed by calculating the local RMSD of a tripeptide window sliding along the sequence. Statistical analyses, superimposition of structures and structural analyses were performed with MOLMOL  [65]  and the PDB Protein Structure Validation Suite.
Accession numbers The atomic coordinates for the NMR structure of synthetic peptide NS4B  and the corresponding NMR restraints in 50% TFE are available in the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank under accession number 2LVG (RCSB identification code 102881). The chemical shifts of NS4B  residues have been deposited in the BioMagResBank (BMRB) under the accession number 18568. de Proteines' facility of the UMS 3444 BioSciences Gerland-Lyon Sud. Confocal laser scanning microscopy was performed at the Cellular Imaging Facility of the University of Lausanne.
Amide protons that remained observable in nuclear magnetic resonance (NMR) spectra after three days in 50% D 2 0/50% deuterated TFE (TFE-d 3 ) are indicated by blue squares (slow exchangeable protons). (C) NMR-derived 1 Ha and 13 Ca chemical shift differences were calculated by subtraction of the experimental values from the reported random coil conformation values in TFE [61]. The dashed lines indicate the standard threshold value of DHa (20. 1 ppm) or DCa (0. 7 ppm) for an a-helix. (D) Representative NMR structure of NS4B[1-40] peptide in 50% TFE. The aa sequence of NS4B[1-40] is depicted in (a). The box indicates a-helix residues. Residues are color-coded as described in the Legend to Figure 1. (b-d) representative NMR structure model of NS4B[1-40] (PDB entry 2LVG) showing the amphipathic character of a-helix 4-32.
Images were acquired on a confocal laser scanning microscope with the same settings for each antibody and condition. Analogous results were obtained when polyclonal antibody Y-11 against the HA tag was used instead of monoclonal antibody HA-7. (B) Histogram of the fluorescence intensity ratios between selective and total membrane permeabilization conditions. Fluorescence intensities in 10-60 images for each condition were determined by using ImageJ software [70]. (C) H7-T7-IZ cells were transfected with a CMV promotor-driven core-E1-E2-p7 expression construct or T7 RNA polymerase-driven N3-5B polyprotein expression constructs harboring the different mutations in HA-tagged NS4B, followed by selective membrane permeabilization and immunofluorescence microscopy using the same antibodies as in panel A.
Acknowledgments The authors gratefully acknowledge  Audrey Kennel  for expert technical assistance,  Eric Diesis  for peptide chemical synthesis and purification as well as mass spectrometry analysis, and  Jean Dubuisson  as well as  Charles M. Rice  for reagents. Peptide synthesis and CD analyses were performed at the  'Centre Commun de Microanalyse des Proteines'  and the 'Production et Analyse.
This work was supported by the  Swiss National Science Foundation  ( 3100A0-138484  to DM), the  Novartis Foundation  ( 09C53  to DM), the  French National Agency for Research on AIDS and Viral Hepatitis (ANRS , to FP), and the  Deutsche Forschungsgemeinschaft  ( TRR 83 ,  TP 13  to RB). Financial support from the  TGE RMN THC   FR3050  for conducting NMR studies is gratefully acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
To show the advantages of a cooperative BCI, we evaluate the performance of combining decisions across subjects with data from an event-related potentials (ERP) based experiment where each subject observed the same sequence of visual stimuli. Furthermore, we show that it is possible to achieve a mean AUC superior to 0. 95 with 10 subjects and 3 electrodes on each subject, or with 4 subjects and 6 electrodes on each subject. Several emerging challenges and possible applications are proposed to highlight how cooperative BCIs could be efficiently used with current technologies and leverage BCI applications.
Performance Evaluation To show the relevance and the type of performance that can be expected by combining the decision from several individuals, we consider data obtained from a P300 speller experiment where the goal is to detect ERPs. The performance will be reported with the Area Under the ROC curve (AUC) [? ] and the information transfer rate (ITR) [? ] in bits per minute (bpm) defined by: ITR = 60 T • ϑ (8) where ϑ is the capacity of the channel between the interface and the user's brain: ϑ = log 2 (N out ) + P log 2 (P ) + (1 -P )log 2 ( 1-P Nout-1 ) (9) with P being the probability of a good detection (i. e. , the accuracy), N out being the number of possible different outputs, and T being the time in seconds of recorded EEG signal that is required to make the decision among the N out outputs.
XU = (D 1 A 1 + D 2 A 2 + H)U (11) where D 1 and D 2 are two real Toeplitz matrices of size N t ×N 1 and N t ×N 2 , respectively. D 1 has its first column elements set to zero except for those that correspond to a target onset, which are represented with a value equal to one. For D 2 , its first column elements are set to zero except for those that correspond to stimuli onset. N 1 and N 2 are the number of sampling points representing the target and superimposed evoked potentials, respectively. A 1 ∈ R N 1 ×Ns and A 2 ∈ R N 2 ×Ns represent the ERP response on a target class and the ERP response on every stimulus, respectively. The spatial filters U maximize the signal to signal-plus-noise ratio (SSNR): SSNR(U ) = argmax U T r(U T ÂT 1 D T 1 D 1 Â1 U ) T r(U T X T XU ) (12) where Â1 corresponds to the least mean square estimation of A 1 : Â = Â1 Â2 (13) Â = ([D 1 ; D 2 ] T [D 1 ; D 2 ]) -1 [D 1 ; D 2 ] T X (14) where The training and test database contained 4800 trials for each subject (800 for target, 4000 for non-target).
In the second approach (SS common ), electrode selection optimizes the mean SSNR across subjects and not the SSNR of each subject, because EEG systems with a low number of electrodes are typically fixed, and it is not possible to adjust the location of the electrodes. With SS common , a single electrode subset is created. In both approaches, the electrode selection procedure is based on a recursive backward elimination. Starting with all electrodes, it consists in testing each electrode for its significance across subjects and in removing the least relevant one at each iteration step. An irrelevant electrode is an electrode whose removal barely impairs the performance or selection criterion. Elimination goes on until every electrode has been eliminated. The score corresponding to the subset selection for SS common is given for each electrode between brackets in  The electrode subsets are estimated with data from the first session, which contain 400 patterns corresponding to the presentation of a target and 2000 patterns for the presentation of a non-target.
In addition to hardware issues for setting a cooperative BCI, BCI frameworks should embed features for transferring signals and decisions online. These features are already available in current BCI frameworks, e. g. , BCI2000, OpenViBE, MATLAB+Simulink [? ? ], facilitating the development of cooperative BCIs. With cooperative BCIs for healthy people, it would be more efficient to have several users with relatively cheap systems with few electrodes than to have a single user with a very expensive system, e. g. , with 32 channels. By considering the EEG signals from different users, a remaining issue is the synchronization of the different EEG signals to the different visual stimuli to avoid jitter effects.
The methylation level per cytosine serves as an estimate of the percentage of cells bearing a methylated cytosine at a specific locus. For subsequent analysis we only considered cytosines that are covered by at least four reads (except for the ECM methylome, which has a considerably lower overall coverage). Table  2  shows that approximately 90% of the cytosines in FB and FLM were retained for analysis and that this percentage drops to approximately 75% for 5-aza treated and untreated. These values suggest our methylation profiles provide a good representation of the truffle methylomes (see Table  3  for the DNA methylation levels of genome, genes and transposable elements in individual BS-seq libraries). BS-seq data coverage (i. e. , read depth) across the genome revealed CNVs between FLM and FB.
Specifically, we identified 107 genomic regions with significant CNV (defined as a 100 kb window with a |Log coverage ratio (FLM/FB)| ≥0. 3), corresponding to 7. 3% of the genome. One hundred and two (95%) of these regions were independently confirmed by standard Illumina sequencing performed on non-bisulfitetreated FLM genomic DNA. This high coincidence rate supports the notion that BS-seq data are unbiased for CNV calling.
RNA-seq library preparation and data analysis Total RNA was extracted as described above, dissolved in H 2 O after LiCl precipitation and purified with the RNeasy Plant Mini kit (QIAGEN) followed by on-column DNase I digestion as per manufacturer's instructions. RNA integrity was verified with a Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), which yielded RNA integrity number (RIN) scores of 7. 0 and 6. 5 for FB and FLM, respectively. RNA was quantified with the Qubit RNA BR Assay kit (Life technologies, Carlsbad, CA, USA) and 1 μg of purified RNA was utilized as starting material for library construction, which was carried out with the Illumina TruSeq RNA  Sample Preparation kit as per manufacturer's instructions. Libraries were sequenced with an Illumina HiSeq 2000 system using 50-bp single-end reads.
The reads were mapped against transcripts of genes and TEs using Tophat  [16]  allowing up to two mismatches, and only unique alignments were kept. The quality of alignments was checked using FastQC  [17]. The resulting alignment files were processed with the HTSeq program (version 0. 5. 4p3, [18] ) to create a gene matrix for downstream analysis. Only reads mapped entirely to a single gene or TE were kept for further analysis. In our FB and FLM datasets, approximately 75% of genes are covered with a depth of at least 10 reads (see Figure  1A ). In another set of data comparing 5-aza treated versus untreated FLM samples, about 90% of genes are covered by 10 or more reads (see Figure  1B ), suggesting a satisfactory completeness of our data coverage. Reads corresponding to similar, but not identical repeats were also mapped, albeit with a lower ability to be mapped compared to a regular (i.
Prediction of novel genes We used the RNA-seq datasets listed in Table  1  (FB, FLM, FLM treated/untreated with 5-aza) to predict novel genes not included in the original annotation  [20]. The prediction pipeline was based on a previously published approach  [21]. First, reads were aligned to the reference genome (truffle v1. 0 assembly) using TopHat  [16] , which resulted in the mapping of non-annotated regions. A guided transcript assembly using Cufflinks  [22]  with the truffle v1. 0 assembly was then carried out for each sample. The resulting individual transcript assemblies were compared with the reference assembly using Cuffcompare  [22]  to identify novel transcripts. A transcript is deemed novel if it does not overlap any known transcript in truffle v1. 0 and its FPKM (Fragments Per Kilobase of exon per Million fragments mapped) value is at least four in the 95% FPKM confidence interval with a class code "u" in the output of Cuffcompare (i.
e. , unknown, intergenic transcript). Finally, to claim these as novel genes, the overlapping novel transcripts are clustered into a single interval, i. e. , the longest transcript, that spans across all the overlapping transcripts. The clusters of overlapping novel transcripts are considered novel genes if their sequences are homologous to any known protein (BlastX analysis via Blast2GO)  [23]. After removal of 11 extremely long genes (≥36,573 bp, the longest known gene in truffles, see Additional file 1: Figures  S1  and S2  for the length distributions), we identified a total of 614 novel genes, 200 to 500 of which present in each sample (Table  4 ). A complete list of novel genes along with expression levels and predicted protein functions are available in Additional file 2: Table  S1.
Availability of supporting data For all sequencing data, in addition to those already described in  [11]  (GSE49700) we provide several formats to facilitate downstream data analysis: BS-seq data is presented in several formatsa BAM file of alignments, ATCGmap for SNP calling and coverage analysis, CGmap for methylation analysis and a wiggle file for data visualization on genome browsers, such as IGV  [31]  (see Additional file 1: Table  S1 ). For WGseq data, BAM and ATCGmap are provided. For RNA-seq data, we provide BAM files and novel genes detected from the merged transcriptome assembly (see Additional file 2: Table  S1 ). All the datasets presented here are publicly available in the GigaScience repository, GigaDB  [19].
Here, we briefly describe the genome sequencing of R. hoogstraalii strain Croatica T (ϭ DSM 22243 T ϭ UTMB 00003 T ). The genome was sequenced using MiSeq technology (Illumina, San Diego, CA, USA) with a mate-pair strategy. SPAdes-3. 1. 0 (10)  was used to perform a de novo assembly of the reads, and the best assembly with a k-mer value of 127 was chosen for annotation. Potential coding sequences (CDSs) were predicted using AMI-Gene  (11) , and the assignment of protein functions was performed by searching against the RickBase (12), GenBank, and Pfam (13) databases using BLASTp  (14) , while ribosomal RNAs, tRNAs, and other RNAs were identified using BLASTn, tRNAs-canSE version 1. 21  (15) , and RNAmmer 1. 2  (16). Orthologous genes between the chromosomes of R. hoogstraalii and R. felis strain URRWXCal2 (GenBank accession no.
hoogstraalii, including genes encoding a putative esterase and a putative hydrolase/acyltransferase of the ␣/␤ hydrolase superfamily, a toxin of an toxin-antitoxin system that contains a PIN domain for nucleic acid binding (vapC2), a site-specific DNA methylase, a superfamily I DNA and RNA helicase, a sugar kinase from the ribokinase family, three guanosine polyphosphate pyrophosphohydrolases/synthetases (spoT5, spoT8, and spoT10), the cell surface antigen Sca11, a major facilitator superfamily (MFS)type permease (proP), an alkylated DNA repair protein, the DNAdamage-inducible protein J (relB2), the MnhF subunit of a multisubunit Naϩ/Hϩ antiporter, a glutamine amidotransferase-like protein, and a penicillin acylase. Nucleotide sequence accession numbers. The genome and plasmid sequences have been deposited in DDBJ/EMBL/GenBank under accession numbers CCXM01000001 and CCXM01000002, respectively.