dataesr/scientific-paragraphs-categorization huggingface dataset:
cordingly, in April 2014, the European Union voted to adopt the Clinical Trials Regulation, which requires the 300 Random sample of trials with SAEs posted at ClinicalTrials. gov No corresponding publication n=98 • No publication (n=78) • Time frame or group not matching 202 Trials with results published (n=20) No. of SAEs not fully reported in corresponding publications n=63 • Not mentioned or no SAE (n=30) • Specific SAE only (n=19) 139 Trials reporting the number of SAEs per group in publications • Pooled results (n=8) • Not for each group (n=6) No. of SAEs in publications not matching ClinicalTrials. gov n=44 95 Trials with number of SAEs in publications • More SAEs at ClinicalTrials. gov than in published matching that at ClinicalTrials. gov articles for 31 trials (Difference 30% in 21 trials) Type of SAEs not described in publications n=62 • No description (n=34) • Drug-related SAEs (n=12) • Most common SAEs (n=10) 33 Trials with types of SAEs described in publications • SAEs of interest (n=5) • Withdrawals only (n=1) 33 trials (11%) with SAEs posted at ClinicalTrials.
Results were visualized using Tracer where parameters with an effective sample size (ess) >150, suggesting good mixing and sampling of the trees, were accepted. All consensus trees were generated in TreeAnnotator with posterior support for internal branches estimated. To further investigate the pathway of transmission and to estimate the most recent common ancestor (TMRCA) of each dataset, sequences were analyzed in BEAST1. 6 (http://beast. bio. ed. ac. uk)  [25]. A simple, constant population size coalescent model was used to generate an initial tree followed by a general time reversible (GTR) substitution model (gamma distribution). To estimate the time of divergence of each compartment, we applied an uncorrelated relaxed clock, gamma distribution  [23] , an initial chain of 10 8 and 10% burning.
Results were accepted if effective sample size was >150. We investigated the impact of time on viral migration and shifts in viral populations by analyzing data generated from the two time points sequenced for Case1. All analyses as described above was applied to the complete dataset and illustrated in Phylotype (www. phylotype. org) [26]. Sequences and their annotations (compartment and time point) were analyzed with the following criteria; size (5), persistence (1), size/difference in population size (1) and support (0. 75) with 500 iterations. Highlighter plots were constructed for each dataset against a consensus sequence of the breast milk and another of the plasma sequences for each case (http://www. hiv. lanl. gov/content/ sequence/HIGHLIGHT/highlighter_top. html) in order to identify the founder virus in each compartment  [27].
The TMRCA of HIV-1 DNA and RNA in the mother's BM was also calculated, using the same approach. The mean TMRCA of the DNA and RNA compartments was estimated to fall within the period of gestation in eight cases. This suggests that HIV-1 was introduced in the breast during pregnancy. In two remaining cases (Case 1 and 3), BM TMRCA dated to several years prior to the infant's date of birth suggesting that old archived strains were introduced to the breast. In the remaining case the TMRCA of the BM dataset (RNA and DNA) were dated to within days of the infant's TMRCA (Fig. 2 , Case 4).
Many advocate single genome amplification in order to exclude the possibility of re-sequencing  [10]. However, a comparison of these techniques has found no evidence of re-sequencing, difference in population structure or sequencing bias  [37] , but large dataset (>10 sequences per compartment) were recommended to eliminate the likelihood of re-sequencing. The use of cross-sectional datasets representing a single time point has been criticized as confounding interpretation of viral population diversity  [11]. We nonetheless demonstrated the same pattern of viral migration and heterogeneity as reported in longitudinal datasets  [11] , indicating that careful laboratory technique and advanced computational analysis could accurately estimate complex transmission patterns even in cross-sectional datasets.
This cohort of pregnant women received single dose NVP in order to prevent MCTC, but new guidelines have been implemented since this study was undertaken. Therefore we were unable to test the impact of multi-drug ART regimens on the transmission dynamics and viral kinetics of HIV. A further limitation of the study is that we did not separate immune cells on breast milk collection but stored whole breast milk for later analysis. As such we were unable to investigate the contribution of specific cell types to HIV transmission in MTCT. However, a previous study by Valea et al  [3]  did illustrate that CD4+ T-cells contribute to productive viremia regardless of ART exposure. These limitations notwithstanding, our study does represent one of the largest breast milk derived clonal sequence datasets from one of the largest, most well-documented postnatal MTCT cohorts analyzed to date.
NMR data analysis Peak picking and intensity measurements were performed using Sparky  (22). Secondary chemical shifts correspond to the deviation of the experimental difference 13 C␣- 13 C␤ chemical shift values for each residue in the protein and the reference value for the corresponding amino acid residue found in databases of chemical shifts compiled from strictly disordered proteins. Secondary chemical shifts 13 C␣- 13 C␤ were here calculated as indicators for residual local secondary structure based on the random coil values database (ncIDP) from Tamiola et al. (23)  that are neighbourcorrected. The secondary structure propensity (SSP) analysis compares the C␣ and C␤ values not only to the random coil values expected in a strictly disordered protein but also to the C␣ and C␤ values expected in a fully formed ␣-helix or ␤-strand, taking additionally into account the propensity of a given amino acid to adopt such a secondary structure.
The results correspond for each amino acid residue to the fraction of the conformer distribution to adopt a secondary structure. SSP scores  (24)  were calculated using the refDB random coil database  (25). Consecutive positive values of both secondary 13 C␣- 13 C␤ chemical shifts and structure propensity are related to a local helical tendency while negative values indicate an extended conformation. Calculations of the co-linear chemical shift deviations (CCSD) are based on the detailed protocol in  (26). A CCSD line was first defined between the resonance in free MED25 and in the ERM 38-68 -bound MED25 as chemical shift deviations (CSD) in ppm = ((Hshift) 2 + (0. 2*Nshift) 2 ) 1/2. A scalar projection on this line of the CSD for resonances corresponding to the set of complexes was next calculated.
These results pinpoint a critical role for F47 and W57 and suggest that aromatic/hydrophobic character is an important criterion for effective binding of ERM to MED25. Finally, we assessed the effect of these mutations on the ability of ERM to activate transcription. For this assay (Figure  3D ), ERM 38-68 and the three mutants (F47L, W57L and F47L/W57L) were fused to the DNA-binding domain of Gal4 and their activity for a luciferase reporter gene was measured relative to Gal4 alone. As shown in Figure  3D , the F47L or W57L mutations drastically reduced the ability of ERM to activate transcription (3. 9 and 0. 3% residual transactivation, respectively). The double mutant F47L/W57L was even more severely affected, with a residual transcriptional activity less than 0. 1%. Collectively, all these data indicate that the affinity of ERM TAD-MED25 ACID/PTOV interaction strongly correlates with ERM transcriptional activity and demonstrate a crucial role of the hydrophobic residues F47 and W57.
ACKNOWLEDGEMENTS US  Open Science Grid (OSG)  are acknowledged for the use of web portals, computing and storage facilities. We thank  Prof. A. Bonvin  ( Utrecht University, Netherlands ) for his support in using the  Haddock Web interface ,  Dr F. Bontems  ( CNRS, France ) for preliminary NMR measurements of ERM and  Dr G. Lippens  ( CNRS, France ) for helpful discussions. We thank the anonymous reviewers for their challenging and lively comments on the initial manuscript.
The main objective of this study is to apply a glacier mass balance and redistribution model to the Dudh Koshi River basin, Nepal. To accomplish this, we (1) develop downscaling routines for temperature and precipitation; (2) calibrate and test the model with available field and remotely sensed observations; and (3) explore the modelled sensitivity of glaciers in the Everest region to future climate change with a suite of temperature and precipitation changes from the CMIP5 ensemble. 2 Data and methods.
Model validation Temperature and precipitation fields developed for this study were tested independently using point observations of mean daily temperature and total daily precipitation at the four EVK2NCR sites. We calculate mean bias error (MBE) and root mean square error (RMSE) to evaluate the skill of the elevation-based downscaling. To validate the calibrated glacier mass balance and redistribution model, model outputs are compared against the following independent data sets: -ice thickness profiles derived from ground-penetrating radar (GPR) at Mera Glacier  (Wagnon et al. , 2013)  and Changri Nup Glacier (Vincent, unpublished data); -annual mass balance and glacier mass balance gradients calculated from surface observations at Mera Glacier  (Wagnon et al. , 2013) ; -decadal glacier extents  (1990, 2000, 2010)  extracted from Landsat imagery  (Bajracharya et al.
At all four EVK2CNR stations, daily temperatures estimated from APHRODITE vertical gradients are greater than observed, with mean daily differences ranging from -1 to +8 • C (Fig. 4 ). Micro-meteorological conditions may contribute to the larger biases observed at Pyramid (winter) and Pheriche (summer). During the summer monsoon pewww. the-cryosphere. net/9/1105/2015/ The Cryosphere, 9, 1105-1128, 2015 riod (mid-June to mid-September), the mean difference for all stations is approximately 5 • C. We develop a bias correction for the day of year (DOY) based on the mean temperature bias from the four stations, which ranges from 3. 22 to 6. 00 • C. The largest bias coincides with the approximate onset of the summer monsoon (DOY 150, or 31 May). A possible mechanism for this is the pre-monsoon increase in humidity at lower elevations, which would be well-represented in the gridded APHRODITE data but not at the higher elevation EVK2CNR stations.
Discussion Through a multi-parameter calibration and validation with independent data sets, we model the mass balance and mass redistribution of glaciers in the Dudh Koshi basin over the period 1961-2007. Temperature and precipitation changes specified from end-members of the CMIP5 ensemble are applied to historical climate fields to examine the sensitivity of glaciers in the region to future climate change. Expected increases in temperature will result in sustained mass losses that are only partially offset by increases in precipitation. We can identify three main sources of uncertainty in our approach: parametric, structural, and climate inputs. These are discussed below. Although considerable progress is made in this study by the systematic integration of field-based observations into our modelling approach, there are still a number of key challenges to be addressed in the future.
Input climate data uncertainty The lack of high-elevation temperature and precipitation data to force the mass balance model is one of the key challenges that nearly all Himalayan modelling studies face. In this study, we derive temperature gradients and precipitationelevation functions from the 0. 25 • gridded APHRODITE data, which in turn is based primarily on low-elevation stations. The downscaling approach is then tested with semiindependent station data from the EVK2CNR network of stations in the Dudh Koshi basin. While temperatures can be skillfully modelled after applying a bias correction based on the day of year, our ability to predict precipitation ranges from very good (at Pyramid) to very poor (at Pheriche). Difficulty in quantifying precipitation and precipitation gradients in high-mountain areas is likely one of the largest sources of uncertainty in mountain hydrology  (Immerzeel et al.
Acknowledgements. We gratefully acknowledge the  Royal Norwegian Embassy at Kathmandu  for funding the Cryosphere Monitoring Project at the  ICIMOD  and the contributions of  Finu Shrestha  and  Sudan Maharajan  to the glacier inventory work. This study was partially funded by the  Netherlands Organization for Scientific Research  through their  VENI program  and by the research for development ( R4D) program  of  DFID , as well as core funds of ICIMOD contributed by the governments of  Afghanistan, Australia, Austria, Bangladesh, Bhutan, China, India, Myanmar, Nepal, Norway, Pakistan, Switzerland , and the  United Kingdom. Mera and  Changri Nup  glacier research was supported by the  French Service d'Observation GLACIOCLIM  and the  French National Research Agency  through  ANR-09-CEP-005-01/PAPRIKA.
Acknowledgements We thank  Dr. Long Chen ,  Dr. Ruomeng Duan  and  Christoph Sonn  for their contribution in the early synthetic attempts and  Dr. Fabiola Liscio  for enlightening discussions on the XRD part. This work was financially supported by the  EC  through the  European Re-search Council  project  SUPRAFUNCTION  ( GA-257305 ), the  Marie-Curie ITN  projects  GENIUS  ( PITN-GA-2010-264694 ) and iSwitch (GA no. 642196 ) the  Agence Nationale de la Recherche  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ), and the  International Center for Frontier Research in Chemistry (icFRC).
a Gel filtration of mESC nuclear extracts. Every second fraction eluted from a Superose 6 column was analysed for the presence of Tip60, together with Tip49 and Baf53α by Western Blot. Native molecular weight markers eluting in the corresponding fractions are indicated on the top of the panel. b ChIP-qPCR validation of the ChIP-seq data in mESCs using purified anti-Tip60 antibodies  [20]  and a negative control IgG antibody. Primers were designed at randomly selected MACS14 peaks with different tag densities (t), as indicated. An intergenic region (IR) without Tip60 binding was selected as an additional negative control. The graph represents the results obtained in two biological replicates (with three technical qPCR replicates each). Standard deviations are indicated. c-e Tip60 binding profiles (GSE69671) together with DNAse I hypersensitive sites (DHS) (GSM1014154), H3K4me3 (GSM307618) and Pol II (GSM307623) binding are shown at three selected genes (Rps9, Nodal and Cdkn1a) as demonstrated by the UCSC genome browser.
12. 7)  [53]. The analysis was conducted with unique reads. Read density (wig) files were created out of bed files by extending reads to 200 bp length and creating 25 bp bins. To detect ChIP-seq peaks the MACS14 peak-calling algorithm was applied using default parameters  [21]. The Tip60 ChIP-seq data were deposited in the Gene Expression Omnibus (GEO) databank under the accession number GSE69671. For further analyses the following ChIP-seq files were included from Gene Expression Omnibus(http://www. ncbi. nlm. nih. gov/geo/): Input (GSM798320)  [54] , RNA polymerase II (GSM307623), H3K4me3 (GSM307618), c-Myc (GSM288356), Oct4 (GSM288347), Sox2 (GSM288346) and Nanog (GSM288345)  [31] , H3K4me1 (GSM594577), H3K27ac (GSM594578)  [37] , H3K9ac (GSM775313)  [54] , H4K16ac (GSM1056596)  [55] , p300 (GSM723018)  [56] , H3K27me3 (GSM307619)  [57] , Nsl1 (GSM1300940) and Msl1 (GSM1300939)  [18].
Tip60 data are deposited in GEO under the following accession number: GSE69671. Fastq files were generated from SRA lite format and aligned to the NCBI37/mm9 assembly using Bowtie (0. 12. 7)  [53]. DNAse I hypersensitive sites were taken from Encode/UW (GSM1014154). Detailed information summarizing all the used ChIP-seq files is presented in Additional file 3: Table  S1.
Bioinformatics analyses of Tip60 ChIP-seq in mESCs Density profile calculation around TSSs as well as K-means linear clustering was conducted with seqMINER  [22]. K-means clustering was performed with normalized read densities, while resulting heatmaps show total number of reads. Obtained MACS14 peaks were annotated using the software HOMER  [59]  based on the ENSEMBL 67 database (mm9). To determine the Tip60 or Pol II enrichment at genes, the peak tag density of the nearest peak to the TSS (in a region of +2 kb) was taken and correlated with gene expression levels. We considered a total of 26,460 ENSEMBL TSSs based on the ENSEMBL 67 database (mm9). For this, raw RNA-seq data of mESCs from Gene Expression Omnibus (GSE34473) were processed using the software tools TopHat  [60]  and HTSeq with default parameters.
See figure on next page. ) Tip60 binding defines active gene sets and enhancer regions. a Heatmap showing k-means clustering of Tip60 (GSE69671), Pol II (GSM307623), H3K27ac (GSM594578), H3K4me3 (GSM307618), H3K4me1 (GSM594577) and H3K27me3 (GSM307619) using 7693 high-confidence Tip60 binding sites as reference coordinates. Densities are represented in region of ∓5 kb around Tip60 binding sites. Four clusters are defined as indicted by the dotted lines. The two "enhancer" peaks are divided into H3K4me1 positive and H3K4me1 negative peaks. The colour scale bars under each data set in A reflect the read densities between 1 and 20 of the given dataset. The number of reads of each data set is indicted in the colour scale bars in millions (m) of reads. b GO term analyses of Pol II positive or bivalent peaks after gene annotation using Manteia [62].
Acknowledgements We are very grateful to  B. Amati  for providing the Tip60 antibodies. We would like to thank the IGBMC sequencing and bioinformatics platform for data generation and support,  M. Hestin  and  G. Rossi  from the IGBMC ES cell culture facility and  F. Klein  for the gelfiltration experiments. We thank  J. Pellegrino ,  M. Gerard  and  D. Devys  for critical reading the manuscript. SR was supported by a fellowship from  Association pour la Recherche sur le Cancer (ARC). This work was supported by funds from  CNRS, INSERM, Strasbourg University , the  European Commission Marie Curie-ITN  ( NR-NET ) and the  Agence Nationale de Recherche  ( ANR-11-BSV5-010-02   Chromact ;  ANR-13-BSV6-0001-02   COREAC ;  ANR-13-BSV8-0021-03  DiscoverIID). LT is recipient of a  European Research Council (ERC)   Advanced Grant  ( ERC-2013-340551 , Birtoaction).
Assembly results relative to 21 strains Twenty-one strains of Propionibacterium freudenreichii were chosen according to MLST results  [12]  and in order to represent the diversity of the species (Table  1 ). They covered 17 different MLST sequence types out of the 46 revealed by a study of 113 strains  [12]. They were sequenced using paired-end Illumina technology and the genomes were assembled de novo. Scaffolds were ordered by comparison with the complete reference genome CIRM-BIA1, obtained previously using traditional Sanger sequencing. All related assembly metrics can be found in Table  2. Annotated genomes were deposited in the EMBL database and are available on the website: http://www. ebi. ac. uk/ena/data/view/ Taxon:Propionibacterium%20freudenreichii.
Results regarding the core and accessory genomes The core genome is defined as the pool of genes corresponding to the orthologous protein coding genes present in all strains. Following application of the orthologous protein clustering protocol, the maximum number of core protein clusters was obtained with thresholds All sequences and annotations are now publicly available. They were deposited in the EMBL database and can now be retrieved using the Bioproject accession. All the strains were able to ferment the following eight common carbon sources: glucose, glycerol, mannose, galactose, inositol, erythritol, adonitol and esculine. The numbers of sugars used by each strain ranged from 10 (CIRM BIA527) to 15 (CIRM BIA 513), and each strain displayed an unique fermentation profile (Table  3 ).
The red pipe indicates the 3' part of the island. The 5' part of the island is not the same for all lactose positive strains. Other genomic islands cited in Table  4  are available on the genome browser of EBI website. [15]  and gluconate  [8]  pathways excepted. Reference pathways of other species are cited in the Results section. In the absence of a gene name, the locus-tag (PFREUD) of the reference strain CIRM BIA 1 was provided. A list of orthologous genes in the 21 strains is available in Additional file 1: Table  S1. The annotations of all the strains studied are available in the EMBL database. using polyP  [16]  encoded by pfp and pfkA, respectively. Fructose 1,6-biphosphate is converted into dihydroxyacetone phosphate and glyceraldehyde 3-phosphate which then pursues glycolysis.
Sequencing The strains were sequenced using paired-end Illumina technology, except for CIRM-BIA9 for which single reads data were produced. Libraries were generated using the NEBNext Sample Reagent Set (New England Biolabs). DNA shearing and library preparation were performed according to the manufacturer's instructions. Sequencing was carried out on GAIIX apparatus and produced 2 * 36 nucleotides length paired-end reads for all strains, CIRM BIA 1025 excepted (2* 81 nucleotides).
Assembly For each strain, assemblies were made of the complete genomes using Velvet 1. 1. 03 [34]. k-mer sizes ranging for 25 to 35 were systematically tested, and a scaffold assembly was generated. The optimum assembly for each strain was then chosen using the N50 criterion. A micro assembly covering less than 10 kb of a targeted locus was performed with Mapsembler  [35]. Mapsembler processes large datasets of reads on commodity hardware. It can check for the presence of given regions of interest that can be constructed from reads, and then builds a short assembly around it, either as a plain sequence or as a graph, to reveal the contextual structure. Mapsembler was used to either check for the absence of genes in a specific genome or to bridge scaffolds of the Velvet assembly.
Annotation All strains were automatically annotated using Agmial  [36]  and the CIRM-BIA1 public genome  [4]  [Genbank:FN806773] as a reference. Functional annotations were transferred by bidirectional best hits. Newly discovered genes were annotated manually. Transporters were annotated using the TCDB database  [37]  and ABCdb repertory  [38]  by homology with transporters from other species. Metabolic pathways were reconstructed according to the KEGG Atlas (http://www. genome. jp/kegg/atlas. html), Metacyc (http://metacyc. org/ META/class-tree?object = Pathways) or specific pathways only described in the literature and cited in the text. When all studied strains, or only one strain, displayed an ability to degrade a specific carbon source, the loci responsible for this degradation were determined by homology with genes previously described in other bacterial species (Figure  5 ).
Acknowledgements This work was supported by a grant:  AIP  Bioressouces 2009 (Propiodive) funded by  INRA  for the sequencing of the 21 strains. SA is the recipient of a capes-cofecub grant (project  720/11 ). Sequencing was performed in collaboration with the  CEA-Genoscope (Evry, France)  for all strains, CIRM BIA 1025 excepted. This latter was sequenced in collaboration with Imagif platform ( Gifsur-Yvette , France). We are very grateful to the  CIRM-BIA (Rennes, France)  for supplying the Propionibacterium freudenreichii strains that were sequenced, to Genouest for providing access to the Galaxy platform, and to MIG for access to the Migale platform.