dataesr/scientific-paragraphs-categorization huggingface dataset:
Proteomic analysis of PIH1D1 identifies the R2TP-prefoldin-like complex The R2TP complex has been previously involved in snoRNP biogenesis  (Zhao et al. , 2005 (Zhao et al. , , 2008;; Boulon et al. , 2008). In humans, it is composed of the conserved core R2TP complex (RuvBL1/ RuvBL2, PIH1D1, and RPAP3), together with a set of prefoldins and few other proteins  (Boulon et al. , 2008 (Boulon et al. , , 2010b;; Cloutier et al. , 2009). Our proteomic analyses readily detected the RuvBL1/RuvBL2 proteins in most of the pre-snoRNP complexes surface of Snu13p that is delineated by the helix 3 and the C-terminal helix 5 (Fig. 7, A  and B ). Comparison of Snu13p complexed to Rsa1p with Snu13p in a free state  (Oruganti et al. , 2005; Protein Data Bank [PDB]  accession no. 1ZWZ) yields a root-mean-square deviation of C positions of 1.
The LTQ-Orbitrap was programmed to perform a Fourier transform (FT) full scan (60,000 resolution) on 400-1,400-Th mass range with the top 20 ions from each scan selected for LTQ-MS/MS. FT spectra were internally calibrated using a single lock mass (445. 1200 Th). Target ion numbers were 500,000 for FT full scan on the Orbitrap and 10,000 MS2 on the LTQ. Quantitation was performed using the program Max-Quant (version 1. 4. 1. 2; Cox et al. , 2009)  and with the Mascot search engine (version 2. 1. 04; Matrix Science). Enzyme specificity was set to that of trypsin, allowing for cleavage N-terminal to proline residues and between aspartic acid and proline residues. Other parameters used were (a) variable modifications: methionine oxidation and protein N-acetylation; (b) fixed modifications: cysteine carbamidomethylation; (c) database: target decoy human MaxQuant; (d) heavy labels: R6K4 and R10K8; (e) MS/MS tolerance: 0.
5 D; (f) maximum peptide length: 6; (g) top MS/MS peaks per 100 D: 6; (h) maximum missed cleavages: 2; (i) maximum of labeled amino acids: 3; and (j) false discovery rate: 5%. In addition to the false discovery rate, proteins were considered to be identified if they had at least one unique peptide, and they were considered quantified if they had at least one quantified SILAC pair, although the number of unique peptides and the number of ratio counts were taken into account during the analysis process to assess the reliability of protein identification and quantification. Proteins labeled as REV (nonreal proteins from the reverse database) were automatically discarded, as well as proteins that did not show any SILAC medium/light, heavy/light, and heavy/medium ratio. SILAC ratios were normalized so that the median enrichment of the specific IP over the control, mostly caused by contaminant proteins, was one.
We thank the  Functional Proteomic Platform of Montpellier (France) , the  Service Commun de Biophysicochimie des Interactions Moléculaires (Université de Lorraine) , and the  FR3209 Ingénierie Moléculaire et Thérapeutique  for the use of their instruments. We are grateful to the  European Synchrotron Radiation Facility  for access to the ID14-4 beamline. This work was supported by the  Centre de la Recherche Scientifique, University of Lorraine , the  Agence Nationale de la Recherche (ANR ; grant no. ANR-11-BSV8-01503 ), grants from  La Ligue contre le Cancer  (équipe labellisée grant # 30025555  Grand-Est),  Association de la Recherche contre le Cancer (ARC) , the  Pôle de Recherche Scientifique et Technologique de la Région Lorraine , the  European Associated Laboratory on pre-mRNA splicing  ( Centre National de la Recherche Scientifique, Lorraine University, Montpellier University , and the  Max Planck Institute) , and the  Fondation pour la Recherche Médicale.
Since this is the first detection of sapoviruses in nonhuman primates living in close contact with humans, we investigated their full genomes. Unbiased deep sequencing employing an Illumina HiSeq platform was conducted as follows: extracted RNA was treated with Turbo DNase (Life Technologies), reverse transcribed (RT) using random hexamers (Life Technologies), and amplified using Phi29 enzyme (4). Contigs were assembled with ABySS software (5) and the CAP3 program  (6)  to construct the viral genomes. The ORFs were identified using the ORF Finder (http://www. ncbi. nlm. nih. gov/projects/gorf/gorf. html). Despite repeated analysis, the 5= untranslated region (UTR) (10 nucleotides [nt]) and 3= UTR [28 nt] were not obtained. The assembled genomes consist of 7,320 nt and contain three presumptive ORFs: nucleotide positions 1 to 6837 (ORF1), 6840 to 7319 (partial ORF2), and 5168 to 5653 (ORF3).
An examination of the 2,280 amino acids of ORF1 revealed that these genomes contain motifs that are characteristic of caliciviruses: a 2C-like NTPase at residue 480 (GAPGIGKT), VPg at residues 951 (KG-KTK) and 962 (DEYDE), a protease at residue 1167 (GDCG), RNA polymerase at residues 1503 (GLPSG) and 1551 (YGDD), and a VP1 at residue 1740 (PPG). Based on the deduced amino acid sequences of the capsid gene (4,530 to 6,843 nt), Cpz-IJC04 and Cpz-IJC09 are 100% identical, cluster within the human GI clade, and are closest (99. 7% identity) to the GI human enteric calicivirus Plymouth isolate (GenBank accession no. X86559)  (7). The clustering of these two genomes within the human GI clade and the knowledge that these chimpanzees were living in close contact to humans (in the sanctuary) suggest recent crossspecies transmission of these viruses from humans to chimpanzees.
However, further analysis is required to confirm this hypothesis. It is important to investigate the prevalence of these viruses in wild chimpanzee populations in order to obtain a better understanding of their evolution and adaptation in these animals. The acquisition of these new SaV genomes will facilitate the design of new specific primer panels for reverse transcription-PCR (RT-PCR) assays, allowing a better understanding of the epidemiology and potential pathogenicity of these SaVs. Nucleotide sequence accession numbers. The sequenced genomes of Cpz-IJC04 and Cpz-IJC09 have been submitted to Gen-Bank under accession no. KJ858686 and KJ858687, respectively.
ACKNOWLEDGMENTS We thank the  Gabonese Government and Total Gabon  for their financial support. This work benefitted from financial contributions from the  PRE-DICT  project of the  United States Agency for International Development (USAID)   Emerging Pandemic Threats Program. This work was supported by the  fellowship BSTD of IRD France. We thank the personnel of the  Tchimpounga Sanctuary in the Republic of Congo  who collected the fecal samples. We also thank all people of the research unit  MIVEGEC (IRD, France)  and of the  Institut Pasteur (France)  for discussions.
Quality trimming and de novo assembly of the reads were performed using the CLC Genomics Workbench version 6. 0. 1 (CLC bio, Aarhus, Denmark). The resulting contigs were reordered in Mauve version 2. 3. 1 (8) using Rickettsia montanensis strain OSU 85-930 as a reference genome (accession no. CP003340. 1). Potential coding sequences (CDSs) were predicted using AMIGene  (9) , and the assignment of protein functions was performed by searching against the RickBase (10), GenBank, and Pfam (11) databases using BLASTp  (12) , while rRNAs, tRNAs, and other RNAs were identified using BLASTn, tRNAscan-SE version 1. 21  (13) , and RNAmmer 1. 2  (14) , respectively. Orthologous genes between R. aeschlimannii and R. montanensis were identified using Or-thoMCL  (15) , with a BLASTp E value cutoff of 1 ϫ 10 Ϫ5 and the default Markov cluster algorithm (MCL) inflation parameter of 1.
The R. aeschlimannii chromosome exhibits a high level of synteny to R. montanensis but differs from it by three inversions of 66,949 bp, 3,404 bp, and 53,495 bp and the absence of the genes encoding the integration host factor beta-subunit (himD), large extracellular ␣-helical protein, 3-hydroxyacyl-coenzyme A (CoA) dehydrogenase (fadB), and competence protein F (comF1 and comF2). Nucleotide sequence accession numbers. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under accession no. CCEA01000001 to CCEA01000014 (BioProject PRJEB6087), and the two plasmid sequences have been deposited under accession no. CCEB01000001 and CCEB01000002.
Study Design (Based on CoPanFlu Djibouti Program) The protocol and samples used in this study were derived from the Djibouti Cohort of Pandemic Influenza (CoPanFlu) program  [18, 19] , as mentioned above. After receiving authorization from relevant government departments, 1,045 individuals from 324 households were enrolled randomly, between 11 th November 2010 and 15 th February 2011, from a pool of 1,835 households, which were derived from two sources: 1,335 households were from the 2009 Hajj Pilgrim database and 500 households were from the community of health workers (CHW) cognisance list of vulnerable households. The initial project was designed as a seroprevalence study and therefore no specific clinical information was used for recruitment. Complete households were included, whatever the medical history of each family member.
Statistical Analysis Data entry and management was performed in the FileMaker Pro Advanced 11 (FileMaker) environment. From the 19 household ownership properties, the principle component analysis was used to create three socioeconomic status (SES), the Upper SES, the Middle SES and the Lower SES  [24]. The criterion used to differentiate the three SES levels was based on rank score of household property ownership, and as described in details elsewhere by Vyas et al. [24]  and Nauta  [25]. The 19 household properties used for SES determination, included the ownership of Vehicle, Music System, Washing Machine, Sealing Air Fan, Bicycle, Toilet, Telephone, Television, Separate Seating/Lounge Room, Radio, Motor Cycle, House Owner, Fridge, Electricity, DVD Video Player, Gas Cooker, Electric Cooker, Air Conditioner System, and running tap water.
Acknowledgments The Authors are grateful to the  CoPanFlu Djibouti Program Partners  and personnel for their various contributions. From the  EHESP Rennes , they acknowledge the administrative support of  Regine Ducos ,  Sarah Kitar  and  Imane El Hamdi. From the  Ministry of Health Djibouti  and the  National Institute of Public Health Djibouti , they acknowledge the participation of  Moulid Barkhat ,  Basra Ibrahim  and the entire field operation team. From the  Marseille EPV-IRD-UMR 190 Research Laboratory , they acknowledge the field operation support of  Nathalie Leblanc  and  Marie-The  ´rese Aristodemo.
Funding and support was obtained from: (a). WHO-EHESP Copanflu International Consortium of EHESP French School of Public Health ,  CS 74312 -35043  Rennes, France; (b). ',  13005 , Marseille, France; (c). French Institute of Microbiology and Infectious diseases (IMMI)  and (d). Ministere de la Sante,  Institut National de SantePublique ,  BP 1157 ,  Djibouti, Republic of Djibouti. (e). The project  PREDEMICS FP7 /2007-2013 -nu278433 -(http://predemics. biomedtrain. eu/cms/). (f). The EDENext FP7-nu261504  EU  project and this paper is catalogued by the EDENext Steering Committee as EDENext# 278  (http://www. edenext. eu). FA was financed by a doctoral fellowship awarded by  EHESP  and  Aix-Marseille Universite. The work of RNC was done under the frame of EurNegVec  COST Action   TD1303. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The Antarctic Ice Sheet is the largest potential source of future sea-level rise. Mass loss has been increasing over the last 2 decades for the West Antarctic Ice Sheet (WAIS) but with significant discrepancies between estimates, especially for the Antarctic Peninsula. Most of these estimates utilise geophysical models to explicitly correct the observations for (unobserved) processes. Systematic errors in these models introduce biases in the results which are difficult to quantify. In this study, we provide a statistically rigorous error-bounded trend estimate of ice mass loss over the WAIS from 2003 to 2009 which is almost entirely data driven. Using altimetry, gravimetry, and GPS data in a hierarchical Bayesian framework, we derive spatial fields for ice mass change, surface mass balance, and glacial isostatic adjustment (GIA) without relying explicitly on forward models.
, 2014). The study reported here was performed as a proof of concept for a timeevolving version of the framework for the whole Antarctic Ice Sheet, which is currently under development. The timeevolving solution will use updated data sets and, as explained above, will also solve for the elastic signal in the GPS data. In addition, it will provide improved separation of the pro-cesses because of the additional information related to temporal smoothness that can be incorporated into the framework (discussed further in Sect. 5).
Additional data sets RACMO. Elements of the Regional Atmospheric Climate Model version 2. 1 (RACMO,  Lenaerts et al. , 2012)  were used to constrain SMB properties. Spatially varying length scales describing the spatial smoothness of precipitation patterns were obtained from the 2003-2009 SMB anomalies (with respect to the 1979-2002 mean). These ranged from 80 km in the Antarctic Peninsula to 200 km east of Pine Island Glacier. The amplitude of the anomalies, which peaked at 50 mm water equivalent in the Antarctic Peninsula, was used to provide an order of magnitude annual amplitude for expected regional SMB variability  (Zammit-Mangion et al. , 2014). RACMO2. 1 also provides a surface density map: the mean annual density of the surface layer. This was used to translate height changes corresponding to the SMB field to mass changes.
, 2014). Spatially varying triangulations (meshes) are used for the different processes reflecting the assumption that, for example, ice loss is more likely to occur at smaller scales near the margins of the Ice Sheet where fast, narrow ice streams are more prevalent than in the interior. We thus use a fine mesh at the margins (25 km) and a coarse mesh in the interior for this field. GIA, however, is assumed to be smooth. This allows us to use a relatively coarse mesh for this process (∼ 100 km). We note that our methodology differs from others in that it is not an unweighted average of estimates with markedly different errors  (Shepherd et al. , 2012)  or a sum of corrected data sources  (Riva et al. , 2009 ) but a process-based estimate. For each of the four fields (noting that elastic rebound and firn compaction covary in this implementation), we infer a probability distribution and standard deviation for every point in space.
Acknowledgements. The authors would like to thank the following colleagues for helpful discussions:  Volker Klemann ,  Ingo Sasgen ,  Matt King ,  Liz Petrie ,  Pete Clarke ,  Martin Horwath ,  Finn Lindgren , and  Valentina Barletta. This work was funded by  UK   NERC  grant  NE/I027401/1. Also, the following colleagues provided additional data without which the project would not have been possible:  J. M. Lenaerts ,  S. Ligtenberg ,  Erik Ivins ,  Ricardo Riva ,  Brian Gunter ,  Pippa Whitehouse ,  Ingo Sasgen ,  Rory Bingham ,  Grace Nield ,  Liz Thomas. Edited by:  E. Larour.
Mice recovered during 1 week with daily handling. One hour before intraperitoneal cocaine injections (15 mg kg -1 ; Sigma Aldrich), 5 pmol of cellpenetrating peptide were bilaterally infused (0. 5 μl; 0. 2 μl min -1 ) into the NAc. Locomotor activity (1/4 turns) was measured automatically as the number of crossings between quarters of a circular corridor using ANY-Maze software (ANY-Maze, Stoeling, Wood Dale, IL, USA) and locomotor sensitization was performed as previously described. 13 Spontaneous locomotor activity was first measured (day 1), over 15 min and 1 h after a bilateral infusion of TAT-GluN1C1 or TAT-GluN1C1Δ. Primary striatal cell cultures, immunoblots, immunocytochemistry, cyclic AMP (cAMP) production assay and live calcium imaging were conducted as previously described. 14.
ACKNOWLEDGMENTS This work has been supported by the  'Centre National de la Recherche Scientifique' (CNRS) , the  'Institut National de la Santé et de la Recherche Médicale' (INSERM) , the  Université Pierre et Marie Curie-Paris VI , 'l' Agence Nationale pour la Recherche' (ANR ,  ANR-08-BLAN-0287 ),  ANR-08-BLANC-028 , the  Bio-Psy Labex cluster of excellence, and the 'Fondation Jérôme Lejeune '. EC has been supported by  Ecole des Neurosciences de Paris and Fondation pour la Recherche Médicale (FRM). CL is supported by the  Swiss National Science foundation. Authors are grateful to  Dr Nicolas Gervasi  for his help during some primary culture experiments and to  Dr Catherine Delagrange  for helpful advice. We also thank  Christophe Piesse  and  Richard Schwartzmann  from the  Institut de Biologie Paris_seine (IBPS)  for their assistance in peptide synthesis and cellular imaging, respectively.
T. Yamamoto 91a,aa , T. Yapici 86a , P. Younk 84a , G. Yuan 85a , A. Yushkov 42a , B. Zamorano 75a , E. Zas 76a , D. Zavrtanik 71a,70a , M. Zavrtanik 70a,71a , I. Zaw 87,ca , A. Zepeda 56a,ba , J. Zhou 91a , Y. Zhu 37a , M. Zimbres Silva 18a , M. Ziolkowski 42a The Pierre Auger Collaboration Spherical harmonic moments are well-suited for capturing anisotropy at any scale in the flux of cosmic rays. An unambiguous measurement of the full set of spherical harmonic coefficients requires full-sky coverage. This can be achieved by combining data from observatories located in both the northern and southern hemispheres. To this end, a joint analysis using data recorded at the Telescope Array and the Pierre Auger Observatory above 10 19 eV is presented in this work. The resulting multipolar expansion of the flux of cosmic rays allows us to perform a series of anisotropy searches, and in particular to report on the angular power spectrum of cosmic rays above 10 19 eV.
Evading such hypotheses and thus measuring the multipoles to any order in an unambiguous way requires full-sky coverage. At present, full-sky coverage can only be achieved through the meta-analysis of data recorded at observatories located in both hemispheres. The Telescope Array and the Pierre Auger Observatory are the two largest experiments ever built to study ultra-high energy cosmic rays in each hemisphere. The aim of the joint analysis reported in this article is to search for anisotropy with full-sky coverage by combining the data of the two experiments. The data sets used for this search are described in section 2 together with some properties and performances of the experiments relevant for this study. Special emphasis is given to the control of the event counting rate with time and local angles as well as to the respective exposures to each direction of the sky.
Both distributions are shown in figure  1  to be indeed uniform above 10 19 eV within statistical uncertainties. Due to the steepness of the energy spectrum, the event counting rate can also be largely distorted by systematic changes of the energy estimate with time and/or local angles. The two main effects acting in this way, namely the atmospheric and geomagnetic effects, are by default accounted for in large-scale anisotropy searches reported by the Auger collaboration  (The Pierre Auger Collaboration 2008 , 2011b). This is necessary given the available statistics around 10 18 eV. However, above 10 19 eV, the impact of these effects is marginal given the reduced statistics. The fact that the same treatment is not implemented in the data set provided by the Telescope Array has no impact on the accuracy of the anisotropy measurements presented in this report, as is shown below.
With at least three years of data taking indeed, the event rate variation at the solar time scale decouples from the one at the sidereal time scale, so that any significant modulation of the event rate of experimental origin, primarily visible at the solar time scale, would not impact significantly the event rate at the sidereal time scale  (Billoir & Letessier-Selvon 2008). Although there is no shower size correction for weather effects in the data set of the Telescope Array, it is worth noting, however, that the natural time scale at which the modulation of the event rate operates is the solar one. This means that over whole years of data taking as in the present analysis, such a modulation is expected to be partially compensated at the sidereal time scale, as just emphasized. The size of the residual effect, together with the level of the sideband effect induced by a seasonal modulation of the daily counting rate, can be checked empirically by evaluating the amplitude of the first harmonics for fictitious right ascension angles calculated by dilating the time of the events in a way that a solar day lasts about four minutes longer  (Farley & Storey 1954).
However, the impact of these corrections is shown in section 6 to be marginal given the limited statistics above 10 19 eV. That the same kind of corrections are not carried out on the data set provided by the Telescope Array is thus unimportant for the present study. This is further reinforced by the fact that geomagnetic effects are expected to be more important for a water-Cherenkov detector array which is more sensitive to muonic signal than for a scintillator one as measurements are made up to larger zenith angles.
Joint Data Analysis All analyses reported in this section are based on a joint data set consisting of events with energies in excess of roughly 10 19 eV in terms of the energy scale used at the Telescope Array by evaluating in the Auger data set the energy threshold which guarantees equal fluxes for both experiments. We are thus left here with 2130 events (795 in the common band) above 10 19 eV from the Telescope Array and 11 087 (3435 in the common band) above 8. 5×10 18 eV from the Pierre Auger Observatory. The arrival directions are shown in figure  7  in equatorial coordinates using the Mollweide projection. Auger data can be seen as the red points in the Southern hemisphere, while Telescope Array ones are shown as the black crosses in the Northern hemisphere. The methodology presented in the previous section allows us to estimate the multipole coefficients and to perform a rich series of anisotropy searches by taking profit of the great advantage offered by the full-sky coverage.
After iterations, the coefficient b is b = 1. 011. Choosing to use nominal energies to build the joint data set would lead to a different value for b (0. 755) due to the different statistics in the Auger data set (8259 events in total instead of 11 087), but it will be shown in next section that this choice impacts the physics results to only a small extent. The normalization convention of the multipole moments used hereafter is chosen so that the a ℓm coefficients measure the relative deviation with respect to the whole contribution of the monopole (i. e. the a ℓm coefficients are redefined such that a ℓm → √ 4πa ℓm /a 00 ). Equatorial Coordinates -15° smoothing Fig. 9. -Left: Flux sky map in km -2 yr -1 sr -1 units, using a multipolar expansion up to ℓ = 4. Right: Significance sky map smoothed out at a 15 • angular scale.
3 are not applied. Given the current statistics above 10 19 eV, the distortions are however expected to be marginal for the specific analysis reported here. This is evidenced by the power spectrum shown as the open squares in figure  12  obtained without applying the corrections to the (cross-calibrated) Auger data set. All points are indeed observed to be within the statistical uncertainties of the estimate shown in figure  11. Given the smaller statistics available in the data set of the Telescope Array, and given that the size of this geomagnetic effect is expected to be smaller due to the lower weight of the muonic component to the signal size with scintillators compared to water-Cherenkov detectors, this provides support that the absence of energy corrections in the data set of the Telescope Array does not impact on the results presented in this report.