dataesr/scientific-paragraphs-categorization huggingface dataset:
This new JavaScript clanviewer is available as an independent component via open source distribution using the Node Package Manager (https://www. npmjs. com/package/clanviewer). The clanviewer component uses a force directed simulation algorithm included in the D3 library (http://d3js. org/) to efficiently position the Pfam entries belonging to a clan into a readable graph view. As before, each Pfam entry is represented as a node in the graph, but they are now represented as circles, with the diameter of the circle being proportional to the number of sequences in the full alignment. Nodes are still connected based on the HHsearch results between the clan members, but now the width of graph edges is proportional to the statistical significance of the HHsearch similarity. This new style of representing inter-Pfam relationships allows the user to more readily understand the relationship between the Pfam entries within the clan.
com/ProteinsWebTeam/clanviewer). In this particular clan, there are three entries (ThuA (PF06283), GATaseI like (PF07090) and Glyco hydro 42M (PF08532)) that from a disconnected sub-cluster. DUF4159 (PF13709) is also unconnected to any other entry. However, these entries are included as part of this clan based on the structural similarities to other entries in the clan.
Ethics Statement The study was carried out in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and approved by an ethical Committee in Belgium (Commission d'Ethique Animale, Université de Liegè, Belgium, permit number 754), Sweden (Uppsala Local Ethical Committee, Uppsala, Sweden, permit number C 115/8), Finland (Viikki Campus Research Ethics Committee, Helsinki, Finland, no approval number used by committee) and Denmark (Local Ethical Committee, University Hospital for Companion Animals, Copenhagen, Denmark, at the time of the approval, the committee did not operate with approval numbers). In France, data was obtained before the creation of the Local Ethical Committee dedicated to clinical research (ComERC-ENVA).
Genotyping and quality control All individuals have been genotyped using the Illumina 170k CanineHD Bead Chip (Illumina, San Diego, CA, USA) and we imported the raw data into GenABEL-native format  [19]  for further analyses. Before proceeding to association analyses, we performed an iterative genotyping quality control (QC) procedure. The original dataset contained genotyping information for 185,295 SNP markers. We removed 33,004 (17. 8%) of these as non-informative markers with minor allele frequency below 5% and 13,600 (7. 3%) markers due to call rate below 0. 95. Thus, after quality control, 139,092 (75. 1%) markers remained for further genetic analysis. Using 2,000 randomly selected markers, we calculated the average allelic-frequency-weighted identity-by-state (IBS) for all pairs of individuals in the population.
We corrected for multiple testing by applying a conservative Bonferroni correction to a p raw = 0. 05 significance threshold. In addition, we performed a permutation test based on 10,000 randomised datasets to empirically derive the significance of the obtained results based on the particular populations analysed. If there were indications of pvalue inflation, we performed the permutations on the GRAMMAR+-transformed mixed model residuals  [20]  to account for the population structure.
Linkage analyses Subsequently, we evaluated the linkage disequilibrium (LD) structure in regions surrounding the strongest associated significant markers. LD was measured using r 2 and visualised using the LDheatmap function from the package LDheatmap  [21]. Gene annotations in those regions were compiled from the UCSC Genome Browser using the canFam2, canFam3. 1 and hg19 assemblies. We used Ensemble annotations and Broad Improved Canine Annotations  [22]. When required, we used liftOver software  [24]  to map between the assemblies.
The significance was lower in this analysis (p corrected 1. 68 × 10 -6 , Fig 2a) and no longer reached the Bonferroni correctionbased threshold. Both results, however, exceed an empirical, genome-wide significance threshold obtained in a permutation test based on 10,000 randomised datasets (p perm = 0. 024). The standard GWAS results and permutation results are shown in S6 Fig. No genome-wide significant associations to fructosamine concentrations were found in any of the other breeds. Due to their significance for glucose metabolism, genome-wide association analyses for glucose and insulin concentrations were also performed in all breeds and within-breeds, however without finding any robust associations. Linkage disequilibrium in the fructosamine associated region The most associated SNP shows strong LD (r 2 > 0.
Q-PCR analysis Total DNA was purified using the GenElute Bacterial Genomic DNA Kit from Sigma. Samples were analysed using a LightCycler FastStart DNA masterSYBR Green I system from Roche. Reactions were run in triplicate using a LightCycler 480 instrument (Roche). Primer 2690 and 2704, which amplify a specific 150 bp fragment inside rstA gene, were used for phage DNA quantification. Data were normalized with the bacterial chromosome using primers 768 and 769, which amplify a 150 bp fragment within the matP gene. For single strand DNA quantification, total DNA was digested 3 hours with ScaI to remove phage dsDNA. There is a cleavage site for ScaI within the phage fragment used for the analysis. Relative copy number of ssDNA was calculated as follows: 2 x e 1 Cp_digested /e 2 Cp_chromosome , in which e represents the amplification efficiency of the primers pairs used.
This work was supported by a grant from the  Agence Nationale pour la Recherche  [ ANR-11-BLAN-O2401 ; www. agence-nationale-recherche. fr/] and from the  Fondation Bettencourt Schueller  [2012 Coup d'Elan award; http://www. fondationbs. org/]. The lab is also supported by the  European Research Council  under the European Community's  Seventh Framework Programme  [FP7/2007-2013 Starting Grant Agreement no. 281590 ; http://erc. europa. eu/]. The funders played no role in our study. candidates for the development of safer live attenuated cholera vaccine.
Statistical analysis The data recorded are bearings: swimming directions when following larvae and bearings of positions in the DISC's chamber. Bearings are recorded in magnetic degrees, which varies from degrees true by about 7°in the study area. Detecting cardinal orientation involved two analysis steps, typical in circular statistics  [36]. First, directionality was assessed within each run, using the Rayleigh test (first-order analysis). Directionality represents the ability to swim in a straight line, i. e. to keep a bearing. This test provides a statistic, r, which is close to 0 when bearings are uniformly distributed around a circle or close to 1 when bearings are concentrated around a unique direction, and a p-value which assesses the significance of this concentration. If larvae all swam directionally but towards different directions, it would suggest that they are capable of keeping a bearing but do not of orienting towards a common goal.
Orientation was assessed by a test across runs: the previously significant within-run mean bearings were used as data in a second-order Rayleigh test. Circular mean and standard deviation are reported. The circular standard deviation follows  [37] , converted to degrees, and is analogous to the standard deviation of linear data for unimodal distributions. Comparisons of the strength of directionality (within-run r) between individuals and groups (or between observation methods, locations, etc. ) were done through beta regression, because r is bounded in [0,1]  [38]. Residuals never showed strong heteroscedasticity or non-normality (as per the Shapiro-Wilk test). To test the effect of swimming in groups on orientation, the distributions of within-run mean bearings were compared between groups and individuals.
04, p>0. 1), between DISC and following on the western side (Watson, U 2 = 0. 05, p>0. 1), etc. The mean strength of directionality ( r) is also consistent across locations but often lower in the DISC than when followed by divers (beta regression with method and location as factors; no effect of location, z = -1. 134, p = 0. 257; significant effect of method, z = -4. 341, p<10 -4 ). An in-depth analysis of many more datasets  [29]  shows that, for individuals, (i) orientation is consistent across locations and methods and (ii) directionality should be inspected within methods. This guides similar comparisons for groups. (Watson, U 2 = 0. 0794, p>0. 1), swimming depth (t-test, t = -1. 2, p = 0. 2), or swimming speed (t-test, t = 1. 2, p = 0. 2). So all data are used further.
Here individuals do not significantly orient in a cardinal direction, only groups do; still, the mean direction is to the south. Previous studies found similar patterns  [29]  and, in all those cases where no significant orientation was found, the sample size was <25 and may have been too small for the southerly direction to significantly emerge from the noise. In addition, in most other studies, data were collected over a shorter time span within each day than in this one (commonly 4. 5 h versus 6. 5 h here). If larvae indeed orient using a sun compass, the spread in the sun azimuth was lower in previous studies, which translated into a significant cardinal signal. Here, the spread was larger and the cardinal signal not significant anymore. This is consistent with the result that followed individuals in this study were significantly oriented with respect to the sun's direction (S3 Fig).
To interfere with Golgi vesicle segregation and exocytosis, primary hippocampal neurons were treated with 500 nM brefeldin A (solvent: DMSO) and 500 nM N-ethylmaleimide (solvent: HEPES buffer pH 7. 0) (Sigma) for 1 h at 37 °C. The solvent was used as control. Neurons were then washed twice in PBS and further processed for immunocytochemistry, as described. Image processing and statistical analysis of IF and WB data. For evaluation of relative WB signal intensities, films were scanned with a resolution of 600 dpi and signals subsequently analysed using the ImageJ analysis software (version 1. 38; National Institutes of Health, NIH). Signal intensities of phosphorylated PERM protein levels or protein levels of synaptosomal preparations were normalized, against loading control signals. Furthermore, protein signal intensities after drug treatment or after Rdx depletion (Rdx-KO ( À / À )), respectively, were compared with control treatments or to WT ( þ / þ ) signal intensities that were set to 100%.
Extrasynaptic GABA A R-a5 or Rdx levels were defined as noncolocalizing clusters with SV2 (red signals substracted by magenta signals). The percentage of synaptic clusters was calculated as follows: (yellow signals/red signals) Â 100%. Within one experiment, threshold adjustments were unchanged between all conditions. For better visualization, signal intensities were presented in false colour (SV2, blue to green; pEGFP green to grey). Synaptic cluster sizes were assessed by Metamorph-based analysis using the 'Integrated Morphometry Analysis' tool, calculating the area of single objects. Statistical analysis was performed with Microsoft Excel or SPSS 15. 0 (SPSS Inc. Chicago, IL). Statistical significance was assessed with a two way ANOVA (genotype Â receptor localization) or the Student's t-test.
45), a xenon lamp, cooled CCD camera Cascade þ 128 (Roper Scientific, Tucson, Arizona) or ORCA II ER (Hamamatsu photonics, Hamamatsu, Japan). Fluorescent signals were detected using appropriate filter sets for QD (D455/70x and HQ655/20), and mRFP (D535/ 50 and E590lpv2). The movement of GABA A R QDs on the proximal dendrites was recorded with an integration time of 30 ms with 500 consecutive frames (15 s). The recording was done up to 30 min after labelling. Tracking and analysis of QDs is described in detail elsewhere  49. Briefly, QDs were detected by cross-correlating the image with a Gaussian model of the point spread function, and the diffusion parameters were calculated using custom software  9  using Matlab (The Mathworks Inc. , Natick, Massachusetts). Single QDs were identified by intermittent fluorescence (that is, blinking).
Data were low-pass filtered at 2. 9 kHz. Experiments were done at room temperature (21-23 °C). Pipettes were made from borosilicate glass capillaries and had a resistance of 3. 0-4. 0 MO, when filled with intracellular solution. Holding potential was À 70 mV. Transient current events were detected by Mini Analysis (version 6. 0. 1, Synaptosoft, Fort Lee, NJ). For further analysis Igor (Wavemetrics, Portland, OR) and Excel (Microsoft, Redmond, WA) were used. Events with 10-90% rise time larger than 50% decay time were excluded. Tonic currents were defined as bicuculline-sensitive tonic currents. The extracellular solution contained: 143 mM NaCl, 5 mM KCl, 0. 8 mMgCl 2 , 1 mM CaCl 2 , 10 mM HEPES, 5 mM glucose and 0. 5 mM TTX, 10 mM CNQX and 50 mM APV, pH adjusted to 7. 3 with NaOH. The pipette solution contained: 140 mM CsCl, 1 mM CaCl 2 , 1 mM MgCl 2 , 11 mM EGTA, 5 mM HEPES; pH adjusted to 7.
Statistical analysis. The sample size was adjusted according to results of prior pilot data sets or studies that used similar methods or paradigms. Power estimation was performed using G*Power 3 (Version 3. 1. 3, Du ¨sseldorf, Germany). Data were analysed using SPSS (PASW) Statistics 19. 0 (Version 19). Prior to analysis, SPSS was used to explore the data, estimate and compare the variance within each group. Appropriate statistical tests were then chosen. The statistical tests used are outlined in the corresponding figure legends for each experiment. A two-tailed type I error rate of Po0. 05 was adopted as a yardstick for statistical significance. The analyses included the between-subjects factors: genotype (Rdx-KO ( À / À ) versus WT ( þ / þ )), sex (female versus male), treatment (vehicle versus a5IA) and repeated measures (within subjects) factors as appropriated by the design of the experiment.
These included factors such as arms, bins and days. The initial analyses provided no evidence for main effects or interactions involving sex. Hence, the factor of sex was dropped from the analyses to increase statistical power. Supplementary restricted analyses were also performed to assist data interpretation whenever appropriate. Post-hoc comparisons were performed with Bonferroni correction based on the overall error variance associated with the relevant factor. During the experiment, the investigator was blinded to the genotype or the treatment identity of the subjects. All biological experiments were repeated at least three times using independent cell cultures or individual subjects (biological replications).
Acknowledgements We thank  M. C. Potier  for her help in how to prepare and use a5IA. This study was supported by the  University Medical Center Hamburg-Eppendorf (UKE) ,  German Research Foundation (DFG)   KN556/4-1 ,  KN556/4-2 ,  KN556/6-1 ,  GRK1459 ,  Chica and Heinz Schaller Foundation and Hamburg Landesforschungsfo ¨rderung (LFF)  program to M. K. The  Swiss Federal Institute of Technology (ETH) Zurich  to J. F. This work was supported by  ERC Plastinhib  and  ANR   Synaptune and the Program 'Investissements d'Avenir' :  ANR-10-LABX-54 MemoLife  to KG and AT. KG was supported by an  EMBO  fellowship. F. F. H. is supported by an  UKE fellowship Forschungsfo ¨rderung Fakulta ¨tsmittel (FFM).
Acknowledgments We are grateful to  D. Baulcombe  for providing the original PVX vector,  S. Santa-Cruz  for the PVX coat protein antisera, and  G. B. Seymour  for critical reading of this manuscript. This work was supported in part by a  Pandeng Programme from Hangzhou Normal University  (201108), an  Innovative Grant for Science Excellence  from the  Hangzhou City Education Bureau China , an  UK-BBSRC core grant  ( BBS/E/H/00YH0271 ) and a  Research Grant  from the  UK Royal Society  ( 2007/R1 ) to Y. H. We also thank the  National Natural Science Foundation of China  ( 31370180 ,  31401926 ,  31200913 ,  31201490 ) and the  Zhejiang Provincial Natural Science Foundation  ( LQ13C020004 ,  LQ13C060003 ,  LQ12C02005 ,  LY14C010005 ) for supporting our work.
Me2 can be located on K211 or K212 of histone H1. H3K9me2 and H3K9me3 were detected by western blot. Novel modifications are underlined. b Novel histone modifications are modeled on the crystal structure of the nucleosome (Protein Data Bank file 3A6N). The histone proteins are shown in ribbon diagram with histone H2A in red, H2B in orange, H3 in blue, and H4 in green. The DNA helix is shown in gray. Modified residues are visible as yellow spheres. The image was generated using the program Pymol  [24]  presence of annotated genes. Around 86 % and 63 % of annotated genes were found to be associated with H3K4me2 and H3K9/14Ac, respectively, while only 605 and 741 TEs, respectively, were marked (Fig. 2b ). In stark contrast with these two marks, H3K9me2 and H3K9me3 were found associated principally with annotated TEs.
MS and data analysis Samples were analyzed by nano-HPLC/MS/MS using an Ultimate3000 system (Dionex S. A. ) coupled to an LTQ-Orbitrap mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). Samples were loaded on a C18 precolumn (300 μm inner diameter × 5 mm; Dionex) at 20 μl/minute in 2 % acetonitrile, 0. 1 % trifluoroacetic acid. After 3 minutes of desalting, the pre-column was switched on line with the analytical C18 column (75 μm inner diameter × 50 cm; C18 PepMap™, Dionex) equilibrated in 100 % solvent A. Bound peptides were eluted using a 0 to 30 % gradient of solvent B (80 % acetonitrile, 0. 085 % formic acid) during 157 minutes, then a 30 to 50 % gradient of solvent B during 20 minutes at a 150 nl/minute flow rate (40 °C). Data-dependent acquisition was performed on the LTQ-Orbitrap mass spectrometer in the positive ion mode.
Survey MS scans were acquired on the Orbitrap in the 400-1200 m/z range with resolution set to a value of 100,000. Each scan was recalibrated in real time by co-injecting an internal standard from ambient air into the C-trap ('lock mass option'). The five most intense ions per survey scan were selected for collision-induced dissociation fragmentation and the resulting fragments were analyzed in the linear trap (LTQ). Target ions already selected for MS/MS were dynamically excluded for 20 s. Data were acquired using the Xcalibur software (version 2. 0. 7) and the resulting spectra were then analyzed via the Mascot™ Software created with Proteome Discoverer (version 1. 4, Thermo Scientific) using an in-house database containing the sequences of histone proteins from P. tricornutum (PtH3_50695, PtH3_21239, PtH4_26896, PtH2A_34798, PtH2A_28445, PtH2B_11823, PtH1_54381) the UniProtKB Phaeodactylum tricornutum database (15,832 proteins) with a Mascot score of 1 % FDR (or <5 %; shown in bold in Additional file 16).
Carbamidomethylation of cysteine, oxidation of methionine, acetylation of lysine and protein N-termini, methylation, dimethylation of lysine, arginine and trimethylation of lysine, methylation of aspartic and glutamic acid, di-glycine of lysine, propionylation of lysine and N-termini of peptides, phosphorylated histidine, serine, threonine and tyrosine were set as variable modifications for Mascot searches. The mass tolerances in MS and MS/MS were set to 5 ppm and 0. 5 Da, respectively. The resulting Mascot files were further processed using myProMS  [57]. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium  [58]  via the PRIDE partner repository  [59]  with the dataset identifier PXD002148.
Data analysis For mapping and analysis we used P. tricornutum genome v. 2. 0 available at the Joint Genome Institute [61]. Reads obtained were quality controlled with a standardized procedure using FASTQC  [62]. Trimmomatic  [63]  was used for quality trimming. GO-based functional analysis on ChIP-marked genes and methylated genes were performed using BLAST2GO  [64]  with a significant FDR cutoff of 0. 05 % probability level. R  [65]  and Biopython  [66]  were extensively used for data analysis. For pattern-based analysis on genes and flanking regions, genes were normalized to equal size, and flanking 2-kb regions were selected as the average intergenic size of ~1500 bp. Data processing, analysis, and plotting were performed using Python, R/Bioconductor and Hyperbrowser  [67]. Results of the analysis have been made available on the Gbrowse-based genome browser at  [68].
Computational analysis of histone modifications in P. tricornutum by ChIP-Seq Single-end sequencing of the five ChIP samples was performed using an Illumina GAIIx with a read length between 36 and 50 bp. This yielded an average of approximately 37 million reads each (Additional file 3). Data for all ChIP samples and input were of good quality with mean quality scores of 30, with 50 % mean GC content. The reads were mapped onto the P. tricornutum genome v. 2. 0 using Bowtie  [69]  with mismatch permission of 2 bp. Unique mapping of reads was adopted. To identify regions that were significantly enriched, we used MACS  [70]  and SICER  [71]  with parameters of W:200 (window length), G:200 (gap size) for H3K4me2 and H3K9_14Ac; W:200, G:600 for H3K27me3, H3K9me2 and H3K9me3; and a FDR <1E-2. Enriched regions were detected against the islands of background control with the same parameter.
MACS and SICER both generated peaks with similar peak ranges and comparable overlapping genomic regions. But for H3K9me2 and H3K27me3, MACS showed much fewer peaks and in these two cases SICER showed significant diffused peaks which overlapped on repeat regions. Visualization and analysis of genome-wide enrichment profiles were done with IGV  [72]. Peak annotations such as proximity to genes and overlap on genomic features such as transposons and genes were performed using Peak Analyzer  [73]. The high-throughput Chip sequencing data have been deposited in NCBI's Gene Expression Omnibus under accession number GSE68513.
Homology estimation analysis We downloaded 57,224,756 protein sequences from 344,297 species from UniProt  [75]  and MMETSP  [76]  databases. The sequences were then categorized into various groups of life, Archaea, Bacteria and as described in the Eukaryote tree of life  [77]. Finally, for the homology estimation analysis, 662,593, 20,176,346, 7,250,814, 1,741,270 and 3,065,341 protein sequences from Archaea (~267 species), Bacteria (~4435 species), Opisthokonta (~853 species), Archaeplastida (~144 species) and diatoms (~70 species) were considered, respectively. The homology was assigned to a pair by BLASTP  [78]  using an expected cutoff value of 1e-5.
Acknowledgments Funding is acknowledged from  CNRS  ( Défi Transition  énergétique: ressources, société, environnement -ENRS) to LT, the  ERC "Diatomite"  and  EU MicroB3  to CB and  Cancéropôle Ile de France  to DL and LT. CB additionally thanks  PSL Research University  ( ANR-11-IDEX-0001-02 ). AR is a PhD student funded by the  MEMO LIFE International PhD program  ( ANR-10-LABX-54 ).
Data analyses All data were analyzed using repeated measures ANOVAs with stimulus intensity and trial type (cued versus non-cued) as withinsubject factors. Specific additional contrasts per experiment: Experiments 1, 2, 4 and 5: genotype as between-subjects factor; Experiment 1: shock intensity as between-subjects factor; Experiment 2: phase as within-subject factor; Experiment 4A and 4: condition (cue-no-shock group and cue-shock group) as betweensubjects factor; Experiment 3 and 4B: drug (three levels) as a between-subjects factor. In Experiment 5, drug (2 levels) was added as a within-subject factor. For analyses of CRF 1 receptor mRNA expression in Experiment 6, expression levels were calculated by correcting the counted amount of dots for the total area. Mean expression levels for the BLA were analyzed using repeated measured ANOVA with hemisphere as within-subject factor and genotype as between-subject factor.