dataesr/scientific-paragraphs-categorization huggingface dataset:
This is a challenging region as the absolute value of the PDFs is small, and the nCTEQ15 parameterization may not be sufficiently flexible to accommodate the new data. Additionally, the inclusion of certain data sets in the EPPS16 analysis (such as the CHORUS ν-Pb data  [42] ) can have a significant impact. Finally, we also see that the EPPS16 PDFs have consistently larger uncertainty bands (especially at low x). As the nCTEQ15 uncertainty bands in this region are essentially extrapolated from larger x results, the EPPS16 uncertainties are probably a more realistic assessment. The issue of PDF parameterization is a perennial challenge for the nuclear PDFs as there is less data and more degrees of freedom as compared to the proton PDFs. The common solution is to impose assumptions on the nPDF parameters, or to limit the flexibility of the parameterization, and thereby underestimate the uncertainty.
Comparisons with LHC proton data can determine nuclear corrections for large A values; this is a kinematic {x, Q 2 } range very different from nuclear corrections provided by fixed-target measurements. 2. The W ± , Z lead data are sensitive to the heavier quark flavors (especially the strange PDF), so this provides important information on the nuclear flavor decomposition. 3. Improved information on the nuclear corrections from the LHC lead data can also help reduce proton PDF uncertainties as fixed-target nuclear data is essential for distinguishing the individual flavors. Predictions from the recent nCTEQ15 nPDFs are generally compatible with the LHC experimental data; however, this is partially due to the large uncertainties from both the nuclear corrections and the data. We do see suggestive trends (for example W ± production in pPb at large y + ) which may impose influential constraints on the nPDF fits as the experimental uncertainties are reduced.
Acknowledgments The authors would like to thank  C. Bertulani ,  M. Botje ,  C. Keppel ,  S. Kumano ,  J. G. Morfín ,  P. Nadolsky ,  J. F. Owens ,  F. Petriello ,  R. Plačakytė , and  V. Radescu  for valuable discussions. We acknowledge the hospitality of  CERN ,  DESY , and  Fermilab  where a portion of this work was performed. This work was also partially supported by the  U. S. Department of Energy  under Grant No. DE-SC0010129  and by the  National Science Foundation  under Grant No. NSF   PHY11-25915.
Acknowledgments We are grateful to the  European Commission , Contract No. 654198  ( Marie Curie action IIF ) for a post-doctoral Fellowship to SMWR, and  CNRS  ( Centre National de la Recherche Scientifique ) and  IUF (Institut Universitaire de France)  for additional funding. We gratefully acknowledge Dr. Yannick Coppel  for help and advice on the NMR experiments.
Anti-(tetra) acetyl histone H4 antibody has previously been validated for P. falciparum (e. g  (Sumanadasa et al. , 2012; Engel et al. , 2015). ) (PlasmoDB gene ID PF3D7_1105000  (Aurrecoechea et al. , 2009) ) and the P. knowlesi H4 amino acid sequence (PlasmoDB gene ID PKNH_0902600  (Aurrecoechea et al. , 2009) ) is identical. Membranes were imaged using an FLA-5000 imaging system (FUJIFILM, USA). As an appropriate antibody loading control was not available at the time this work was carried out (those normally used in this laboratory for P. falciparum did not cross-react with this species; data not shown), protein loading was assessed by Coomassie blue-staining of samples separated by SDS-PAGE.
Homology modelling and docking As there is no reported crystal structure of HDAC(s) from any Plasmodium species, a homology model of the PkHDAC1 was built using the homology modelling suite in Maestro (Schr€ odinger Release, 2016-1). The BLAST search engine within the suite was used for template searching before model construction. Human HDAC2 (pdb code:3MAX) was chosen as the most suitable template due to its high overall sequence identity (62%) and similarity (81%) with the PkHDAC1 protein, and its high resolution (2. 1 Å) crystal structure in complex with a small molecule inhibitor. The energybased approach was used to build the final model. The homology model created from Maestro was checked for model quality and stereochemistry using SwissModel  (Kiefer et al. , 2009). For ligand docking experiments, 2D-structures of vorinostat, panobinostat and belinostat were drawn in ChemBioDraw 14.
0 and saved as sdf files. The 3D coordinates of the ligands were prepared in Maestro using the Ligand Preparation suite at physiological pH using the OPLS (2005) force field. GOLD (version 5. 2. 2) was used for ligand docking and Chemscore for scoring the relative affinities of ligand poses. Ligands were docked in the active site defined by a 15 Å radius around the OD2 atom of Asp 262. Each ligand was docked 20 times using scaffold constraint settings to ensure that each hydroxamate mimicked the native binding mode of vorinostat bound in the crystal structure human HDAC2 (pdb code: 4LXZ) during ligand docking simulations.
i. by microscopic examination of Giemsa-stained thin blood smears prepared from tail snip bleeds. Mice were euthanized according to an approved scorecard of criteria and all animal work was conducted using protocols approved by National Health and Medical Research Council (NHMRC) of Australia Animal Code of Practice, as approved by the Griffith University Animal Ethics committee. Data were analysed using two-tailed Student's t-Test using Graph Pad Prism (version 5).
, 2006; Dow et al. , 2008; Trenholme et al. , 2014; Engel et al. , 2015)  against P. falciparum and is considered a marker of HDAC inhibition in the parasite (e. g  (Darkin-Rattray et al. , 1996; Andrews et al. , 2008 Andrews et al. , , 2012a;; Chaal et al. , 2010). ). In P. falciparum, three class I/II HDAC homologues (encoded by Pfhdac1, Pfhdac2 and Pfhdac3 genes) have been annotated in the PlasmoDB  (Aurrecoechea et al. , 2009)  genome database. Two additional class III homologues are also present  (Aurrecoechea et al. , 2009) , but are not essential in asexual intraerythrocytic stage parasites  (Freitas-Junior et al. , 2005; Tonkin et al. , 2009). While recombinant forms of PfHDAC2 and PfHDAC3 are not currently available, the activity of PfHDAC1 can be inhibited by anti-Plasmodial HDAC inhibitors  (Patel et al.
Acknowledgements We acknowledge the  Australian Red Cross Blood Service  for the provision of human blood and sera for culture of Plasmodium parasites and thank  Professor Michael Blackman  and  Dr Robert Moon ,  Medical Research Council UK , for providing P. knowlesi parasites. The authors' work was supported by  Australian National Health and Medical Research Council  ( APP1093378  and  APP1074016  to KTA and DPF), the A- PARADDISE program  funded under the  European Union's Seventh Framework Programme  (grant agreement no. 602080  to KTA, DPF, JL, RJP, EP, GFS and SL) and  Griffith University  (GUIPRS and GUPRS scholarships to MJC). DF acknowledges an  NHMRC Senior Principal Research Fellowship  ( 1027369 ) and  ARC grant  ( CE140100011 ). The work of JL and RP was supported by institutional funds from the  CNRS , the  Institut Pasteur de Lille  and the  Universit e de Lille.
ChIP-seq Adult hippocampi from Del/+, Kansl1 +/-and wt mice were dissected and snap-frozen in liquid nitrogen. Tissue samples were ground in a liquid-nitrogen chilled mortar and the resulting powder was used for ChIP. Chipping for H3K4me3 (Diagenode A5051-001P) was performed as in (www. blueprint-epigenome. eu/UserFiles/file/Protocols/Histone_ChIP_July2014. pdf). Libraries were synthetized with KAPA Hyper prep kit (KK8504) following the manufacturer's instructions. The libraries were pooled (4/lane) and sequenced on the illumina HiSeq. Libraries were mapped with BWA (0. 6. 2). Peaks were called with a custom C++ script and DEseq2 (R+) was used to perform statistical comparisons. Data are deposited in GEO under accession GSE80311. All enrichment analyses are made from standard hypergeometric tests with Benjamini or Bonferroni correction.
Cell-types enrichments ChIP-seq data and ChIP-seq supplementary tables were deposited in GEO and available at the link https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?token=cnavacaedzgztgz&acc= GSE80311. Cell-types enrichments are based on the single-cell RNAseq data from Amit Zeisel et al. , 2015, "cell types in the mouse cortex and hippocampus revealed by single-cell rna-seq"  [26]. In this work, single-cell RNAseq was used to measure trascriptomes of >3000 single cells, allowing to define the markers of 11 different cell types in adult (P21-P30) hippocampus and somatosensory cortex. Given that also our ChIP-seq is done on adult (P30) hippocampus, single-cell RNAseq data from Zeisel et al. becomes a highly valuable resource to gain insight at the cell type level. Here we performed standard hypergeometric tests with bonferroni correction against the cell-type markers derived from data of Zeisel et al.
Statistical analyses for behavior and electrophysiology All acquired behavioral data were analyzed using a one-way ANOVA analysis with a post-hoc Tukey's test, when applicable, non-parametric Kruskal-Wallis test or Mann-Whitney U test using the Sigma Plot software (Ritme, France). The Pearson's chi-squared test was used for mutant allele transmission. Data are represented as the mean ± s. e. m. and differences were considered to be significant if P < 0. 05. When comparing freezing levels between wt, Del/+, and Dup/+ animals over different time points during extinction we used the two-Way ANOVA Repeated Measures statistical test followed by Holm-Sidak post-hoc tests to evaluate for interactions between the groups. Otherwise, when comparing wt data with those obtained from respective Del/+ or Dup/+ animals for a single time point, we used Student's t-test.
When data did not follow a normal distribution, we used the Mann-Whitney rank-based statistical test. In electrophysiological experiments input-output relationships were compared initially by a mixed model repeated-measures ANOVA and Bonferroni post hoc test implemented in Prism 5 (GraphPad Software, San Diego, USA) using individual slice data as independent observations. Since several slices were routinely recorded from every mouse, fEPSPmax, PPF and LTP values between wt and mutant mice were compared using two-way nested ANOVA design with genotype (group) and mice (sub-group) as fixed and random factors respectively (STA-TISTICA v. 10, StatSoft, USA). DF error was computed using the Satterthwaite's method and main genotype effect was considered significant if P < 0. 05. Graph plots and normalization were performed using OriginPro 8.
Acknowledgments We are grateful to the animal care-takers for their services at the  PHENOMIN-ICS and Babraham Research Campus. We also wish to thank members of the research groups involved and staff of the  IGBMC laboratory  for their helpful suggestions and discussions. We are thankful to  Erica E. Davis  for critical reading of the manuscript. The Kansl1 knock-out was obtained from the  International Mouse Phenotyping Consortium  (www. mousephenotype. org)at the French PHENOMIN node. Mouse models are available through the Infrafrontier infrastructure.
This work has been supported by the  National Centre for Scientific Research (CNRS) , the  French National Institute of Health and Medical Research (INSERM) , the  University of Strasbourg (UDS) , the  "Centre Europeen de Recherche  en tools valuable for the development of therapeutic approaches. This study also received support from  French state funds through the "Agence Nationale de la Recherche " under the  frame programme Investissements d'Avenir labelled   ANR-10-IDEX-0002-02 ,  ANR-10-LABX-0030-INRT ,  ANR-10-INBS-07 PHENOMIN , from the  EU   FP7 large-scale  integrated project GENCODYS (grant  241995 ) to YHe, MK and  HGS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Vertebrate comparative genomics All 204 vertebrate genomes available on NCBI's FTP site as of November 2016 were downloaded (Supplementary Table  S6 ). The veNOG dataset from the eggNOG orthology database  (46)  was used to find the corresponding genes across all 204 genomes. Although the original veNOG orthology was based on an older set of vertebrate genomes than the set we downloaded, eggNOG-mapper  (47)  was used to map the original veNOG groups onto the new genomes. eggNOG-mapper was run using the 'target orthologs one2one' setting to get as conservative a mapping as possible.
Large introns of CFS genes have resisted size reduction through vertebrate evolution The results obtained here demonstrate that expression of extremely large genes gives rise to chromosomal fragility. To add an evolutionary perspective on gene length, we analyzed its distribution within vertebrates. For this purpose we extracted data on orthologous genes from a total of 203 vertebrates, which we divided into the four classes: Bird (Aves), Fish (Chondrichthyes and Osteichthyes), Reptile plus Amphibian (10 species representing Reptilia and 3 species representing Amphibia) and Mammal (Mammalia). Generally, extremely large genes consist mainly of large introns and code for small proteins, such as the 50 kDa Parkin encoded by PARK2 spanning more that 670 000 base pairs. Despite the proven susceptibility of extremely large genes to large deletion, evolution has not eliminated the introns of extremely long genes.
ACKNOWLEDGEMENTS We thank  Jette Bornholdt Lange  for assistance with the RNA quality check and  Olaf Nielsen  for help with elutriation. Furthermore we are grateful to  Christine Farr  and  Jakob Nilsson  for sharing reagents and to  Sebastian H. N. Munk  and  Vasileios Voutsinos  for helpful discussions. Author contributions: Conceptualization,  V. H. O. Methodology , C. P. , V. H. O. , S. A. S. , N. R. H. , B. L. T. , M. D. , A. S. O. Validation, C. P. , V. H. O. Formal analysis, S. A. S. , N. R. H. , C. P. , V. H. O. Investigation, C. P. , V. H. O. , S. A. S. , N. R. H. , A. S. O. Data curation, N. R. H. , S. A. S. Writing Original Draft, V. H. O. Writing--review and editing, V. H. O. , M. L. , C. P. and N. R. H. Supervision, V. H. O. , M. L. Project administration, V. H. O. , M. L. Funding acquisition, M.
e. 1. 6 mg. L -1 ). The volume of acetonitrile injected (<1% of the total volume) was low enough to have no impact on PHE sorption, as previously studied  (Groboillot et al. , 2011). To evaluate the remobilization capacity of rhamnolipids, the biosurfactant (AGAE Technologies (USA), 90% purity) was added as a powder in the reactors 5 h after the introduction of PHE, to a final concentration of 0. 55 g. L -1 (concentration ten times higher than its CMC of 0. 05 g. L -1 ) to ensure the formation of micelles in the aqueous phase. Five hours was chosen as the time to add the biosurfactant after PHE spiking because it was observed that a "quasi" plateau was reached for PHE sorption after this time (data not shown). Mixtures treated or not with rhamnolipids were stirred during 24 h at 25°C to be sure to reach thermodynamic equilibrium (time evaluated during preliminary tests)  (Barnier et al.
Rhamnolipids were added to distilled water to a final concentration of 300 mg. kg -1 dw soil into half of the microcosms. Rhamnolipids correspond to a mixture of mono and di-rhamnolipids (R90 product). So the concentration of the added biosurfactant was around 0. 61-0. 69 g. L -1 in the water fraction, which was near the concentration tested in sorption studies. The flasks were opened once a week in sterile conditions for 15 min to renew the atmosphere, and were incubated at 20°C in static mode in a dark room. Analyses of microcosms were carried out after 0, 8, 16 and 30 days of incubation for Pv soil and after 0, 20, 42 and 50 days for PPY soil, based on the different PHE degradation rates evaluated through a preliminary experiment (data not shown). These different sampling times between the two soils were chosen in order to be able to compare the labeled DNA between the two soils (in terms of percentage of dissipated PHE), to potentially have the same quantity of 13 C possibly incorporated into the degrading bacteria DNA.
After quality checking on agarose gel electrophoresis and quantification using NanoDrop, three independent PCR products per sample were pooled together to minimize PCR bias on sequencing, and an equimolar mixture was then prepared. Pyrosequencing was carried out using a GS FLX 454 (Beckman Coulter Genomics, Danvers, MA, USA) and produced 316. 5 megabases corresponding to a total of 716,250 partial 16S rRNA gene sequences having a median size of 490 bp. Sequence analyses, i. e. reducing sequencing error, removing chimeras, grouping into OTUs, subsampling to 1000 sequences, SILVA database alignment, and classifying using the RDP reference files were performed using MOTHUR (version 1. 36), as recommended by its author  (Schloss et al. , 2011). Following the trimming, we obtained 250 bp sequences from the forward primer (corresponding to the V1 and V2 16S rDNA regions).
, 2014). This observation was of concern because these solvents are frequently used together in industry. Indeed, according to the COLCHIC database  (Mater et al. , 2016) , 9473 individual measurements of TOL were performed in 1209 companies in France between 2005 and 2014. Of these measurements, 64% revealed a multiple exposure context, and the TOL/MEK mixture was present in almost 44% of companies. Statistical analysis of long-term measurements in manufacturing industry (which covers the vast majority of measurements) revealed that the 8h-OEL for TOL and MEK were exceeded in 3. 3% and 0. 6% of cases, respectively, with peaks exceeding 1400 ppm in both cases. Even though the extent to which the 8h-OEL were exceeded remains low, it must not be forgotten that most OEL are established from results of studies involving exposure to single pure substances, and these values might be inappropriate when subjects are exposed to several chemicals simultaneously  (Viau, 2002).
Consequently, several authors, and the ACGIH, proposed an additivity formula to assess the risks encountered by workers faced with combined exposure. Thus, when two or more hazardous substances which have similar effects on the same organs are present in workplaces, their effects should be considered to be additive, unless established otherwise. Some tools, like the MIXIE database (http://www. inrsmixie. fr) (Vyskocil et al. , 2007) , apply this approach to help occupational physicians to predict the risks faced by mixture-exposed workers. For a biometrological assessment,  Ogata et al. (1993)  proposed a means to use a single biomarker to monitor exposure to a mixture when it can be assumed that its components act additively and that there are no toxicokinetic interactions between the elements composing the mixture.
Statistical analysis All statistical analyses were performed using Statgraphics Centurion XVI Software (Version 16. 2. 04) (StatPoint Technologies, Inc. , Warrenton, VA). To analyze results, various statistical procedures were implemented. These procedures were based on analysis of variance (ANOVA) to test one or more possible effects (the day effect, the atmospheric concentration effect, the co-solvent effect or the group effect). When necessary, a general linear model including the different fixed effects and a random effect (subject) was used to take the non-independent nature of data into account. Post hoc analysis was performed by the Bonferroni method. The 95% confidence interval was used as an indicator of statistical significance. In addition, a least squares regression procedure based on a multiplicative (loglog linear) model was used to analyze the blood/atmosphere, brain/blood or urinary metabolites/blood relationships.
Blood and brain concentrations For each of the eleven 1-month inhalation exposure conditions (single-compound exposure and binary exposure), blood was collected at four time points. Statistical analyses revealed no day effect on the TOL and/or MEK blood concentrations measured. Table  2  summarizes the blood concentrations measured on day 1 and the mean concentrations measured over the four collecting times. Because the results obtained on day 1 and days 5, 10 and 20 were very similar, the effect of mixtures was measured using data from samples collected on day 1. [Table  2  near here] The relationship between the blood TOL (TOL-Blood) and atmospheric TOL (TOL-A) concentrations is plotted in Figure  1. Data from single-compound exposures (white diamonds) could be fitted with a multiplicative model (log-log): ln TOL-Blood (µg/g) = -6.
In addition, the toxic effects of TOL are mainly linked to the molecule itself rather than its metabolites  (ATSDR, 2015). Blood samples were collected on days 1, 5, 10 and 20 and the TOL and MEK blood burdens were measured after single or binary exposure. These concentrations remained relatively constant throughout the study. Blood concentrations were therefore extensively analyzed based on samples from day 1. The relationship between TOL-Blood and TOL-A when animals were exposed to TOL alone, based on data collected at the end of the first day of exposure, could be fitted by the following equation: ln TOL-Blood (µg/g) = -6. 217 + 1. 378 × ln TOL-A (ppm). This equation was quite similar to that reported by  Benignus et al. (1984)  [ln TOL-Blood = -6. 53 + 1. 356 × ln TOL-A] when studying inhalation exposure in Long-Evans rats exposed for 3 h to 50, 100, 500 and 1000 ppm TOL, and to the one we reported with Sprague-Dawley (SD) rats following 6-h inhalation of 2 to 500 ppm TOL [ln TOL-Blood = -5.
However, the activities of CYP1A2 and CYP2E1, the isoforms specifically involved in TOL (and MEK) metabolization  (ATSDR, 2015 , Cosnier et al. , 2017) , were systematically higher in exposed animals than in controls. No additive or synergistic induction was detected (even after 1-month exposure) in the presence of MEK. These data support a hypothesis of inhibition, presumably competitive but the determination of the exact nature of the inhibition (competitive, uncompetitive or non-competitive) would require additional experimentations. Consequently, clearance of the solvents takes longer in cases of co-exposure, and the relative amount of both solvents determines their half-life in the blood  (Vodickova et al. , 1995). Based on the 8h-OEL for TOL before 2012 (50 ppm), we examined the maximum increase in TOL-Blood between TOL50 and TOL50/MEK1400.
We therefore recommend that those responsible for prevention consider measuring suitable urinary metabolites of all the main chemical products used in workplaces to get a true idea of the hazards present in the work environment. However, it is important to keep in mind that extrapolation from these measurements to determine the exposure concentrations must be done carefully, taking the impact of mixtures on the kinetics of metabolism into consideration. PBPK models for inhaled TOL and MEK are available for rats and humans. For MEK, they are based on a limited number of datasets. Future work should test the predictive capabilities of these models to describe the presented data (blood, brain and urinary) from single exposures (TOL or MEK) and the toxicokinetic interactions between both chemicals.
MOAB0103 Safety, efficacy and dose-response of GSK3532795/BMS-955176 plus tenofovir/emtricitabine (TDF/FTC) in treatment-naive (TN) HIV-1-infected adults: week 24 primary analysis Oral Abstracts acquired HIV (APH), this analysis aimed to describe APH outcomes in SSA by country income group (CIG). Methods: Through the CIPHER cohort collaboration, individual retrospective data from 12 cohort networks across 5 continents were pooled; 7 networks representing SSA were included here. APH included were HIV-infected children with entry into care at age <10 years (proxy for perinatally acquired HIV) and follow-up at age >10 years. CIG was classified according to World Bank Classification at median year of first visit by country. Cumulative incidence functions were calculated by competing risk analysis for mortality, transfer-out and loss-to-follow-up.