dataesr/scientific-paragraphs-categorization huggingface dataset:
Running 16 selection processes, i. e. one process per real and virtual hardware thread, results in an almost 20% increase in event throughput when HT is switched on. In the early days of ATLAS TDAQ software development the Online, L2 and EF groups worked quite independently of each other and their software had not yet been merged into one overall release. Separate C++ threading packages were developed and used, all based on the POSIX pthreads library (a C language interface, see ref. [218] ), at least on Linux platforms. A few attempts at standardizing on one of the packages did not succeed because they were already widely used in their respective domains and the effort of changing was considered to have lower priority than the task of finalizing the TDAQ software. In fact, in the recent past one additional threading package, boost::thread  [153] , started to be used in some parts of the software.
14. All the tools used for system administration (either third-party like Nagios and in-house like ConfDB, described in 2. 14) use MySQL  [43]  as the supporting relational database. MySQL has -124 -been preferred above other database servers not just because no other one was available in the ATCN at the time, but also because a large variety of third-party tools, internally used for system administration, offer default support for it. Nowadays Oracle is also available inside the ATCN, but a migration is considered not to bring significant advantages.
As described in section 2. 3. 4 these energy sums are extracted on the basis of special requests by the ROS and forwarded to the L2 trigger. The upgrade of the ROS PCs has made it possible to deploy this trigger at a rate of about 10 kHz. The implementation of the DAQ/HLT system has benefitted from various developments in software technology, in particular: the development and deployment in and outside high-energy physics of the Linux operating system, the transition to object-oriented programming and multithreaded programming, and the development of web technology as well as database technology. Although running multi-threaded algorithms in the L2 trigger and the Event Filter in principle allow better utilization of the available resources (in particular of the available memory) it has been found that currently the best approach is to embed sequential algorithms as used in offline processing into a multi-threaded environment, which makes it possible to benefit from the effort spent on development, testing and maintenance of the offline software.
However, in the future, with an increasing number of cores per CPU, another approach has to be found, for HLT as well as for offline processing, to make full utilization of the available processing resources possible. For all other aspects of the DAQ/HLT system parallelism is exploited to a high degree. Examples are the tree of run controllers, database access via database proxies, distributed summing of histograms, the multi-threaded ReadOutApplication and SFI application. A multi-threaded version of the SFO application is currently under development. The DAQ/HLT system is in principle a large distributed computer system. Configuration management, control, error detection and handling, monitoring and system administration, including access management are all essential. Good solutions have been found and implemented and continue to evolve on the basis of the experience gained from operating the system.
Figure26. Schematic view of an Athena service that reads online specific detector conditions from the online Information Service (IS) and presents them to the event selection algorithms in the same format as the corresponding service does in the offline domain. There the data are read directly from the offline conditions database. In this way algorithms have complete transparent access to their conditions data. The arrow between IS and the offline conditions database indicates that selected data delivered by IS are also archived asynchronously in the conditions database.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. -127 -We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation, Denmark ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF, I-CORE  and  Benoziyo Center , Israel; INFN, Italy;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  RCN, Norway ;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
In addition, individual groups and members have received support from  BCKDF , the  Canada Council, CANARIE ,  CRC ,  Compute Canada, FQRNT , and the  Ontario Innovation Trust ,  Canada ;  EPLANET ,  ERC ,  FP7 ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex and Idex ,  ANR ,  Region Auvergne  and  Fondation Partager le Savoir, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF; BSF, GIF  and Minerva, Israel; BRF, Norway; the  Royal Society and Leverhulme Trust ,  United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from  CERN  and the ATLAS Tier-1 facilities at  TRIUMF (Canada) ,  NDGF (Denmark, Norway, Sweden) ,  CC-IN2P3  (France),  KIT/GridKA (Germany) ,  INFN-CNAF (Italy) ,  NL-T1  (Netherlands),  PIC (Spain) ,  ASGC (Taiwan) ,  RAL (U.
We present data relating to the interactome of MCM9 from the nuclei of human cells. MCM9 belongs to the AAA þ superfamily, and contains an MCM domain and motifs that may confer DNA helicase activity. MCM9 has been shown to bind MCM8, and has been implicated in DNA replication and homologous recombination. However, the mechanistic basis of MCM9's role in DNA repair is poorly understood, and proteins with which it interacts were hitherto unknown. We performed tandem affinity purification of MCM9 and its interacting proteins from nuclear extracts of human cells, followed by proteomic analysis, thereby generating a set of mass spectrometry data corresponding to the MCM9 interactome  [1]. The proteomic data set comprises 29 mass spectrometry RAW files, deposited to the ProteomeXchange Consortium, and freely available from the PRIDE partner repository with the data set identifier PXD000212.
Expression of tagged proteins in human cultured cells, preparation of nuclear extracts, tandem protein purification Experimental features Human MCM9 constructs, tagged at the N-or C-termini with FLAG-HA, were expressed in HeLa S3 cells, and tandem affinity purified. Complexes of MCM9 and associated proteins were analyzed by SDS-PAGE and silver staining, followed by liquid chromatography-mass spectrometry  [1]. Peptides and proteins were identified by database searching.
Data accessibility The data are provided in the public PRIDE repository with the dataset identifier PXD000212. The direct URL to access the data is http://www. ebi. ac. uk/pride/ archive/projects/PXD000212 Value of the data The data revealed for the first time the nuclear interactome of human MCM9. The data allow the research community to investigate the MCM9 interactome. The data act as a benchmark reference of MCM9 interactors for future studies. Mining these data could produce additional discoveries about MCM9 interactors. Analyzing the data could lead to further insights into MCM9 and its processes. 1. Data.
Mass spectrometry RAW data files We present 29 mass spectrometry RAW data files that correspond to our proteomic analysis of the human nuclear MCM9 interactome. These data files were deposited to the ProteomeXchange Consortium via the PRIDE partner repository  [2]  with the data set identifier PXD000212 (direct access: http://www. ebi. ac. uk/pride/archive/projects/PXD000212). Data files with names 24770. RAW to 24784. RAW correspond to N-terminally tagged MCM9; 24786. RAW to 24801. RAW correspond to Cterminally tagged MCM9.
Protein interaction network We present a protein interaction network centered on MCM9, generated using the Cytoscape program (http://www. cytoscape. org; version 3. 2. 1) [3]  (Fig. 1 ), based on our proteomic data. The diagram comprises MCM9 plus 22 interacting proteins (combined from the N-terminal and Cterminal FLAG-HA tagged purifications). Contaminant or background proteins have been removed.
Following Nuclear interactome network for human MCM9. The network diagram, generated with Cytoscape software, shows nuclear proteins (nodes), identified by tandem affinity purification and mass spectrometry analysis, that form a complex with human MCM9. Proteins are represented by their official gene symbols, and attributed a color code according to their associated biological processes or functions, as indicated. Note that connecting lines (edges) between proteins do not imply direct physical association, but merely that proteins are members of a shared interactome centered on MCM9. A "live" interactive version of this Cytoscape figure is available on the Data in Brief web page for this article. amplification, cell clones were selected and cultured, and the expression level of double-tagged MCM9 in each clone tested by immunoblotting.
Peptide and protein identification Identification of peptides and proteins was performed by the data analysis pipeline of the Taplin Facility. MS RAW data files were matched to entries in the human International Protein Index (IPI) database  [8]  using the Sequest algorithm  [9]. All accepted peptides have a cross-correlation (Xcorr) score of at least 0. 5. Identified peptides, Xcorr scores and corresponding protein identifiers (IDs) for each sample were presented as HTML tables accessible via a web browser.
Data post-processing and data-mining Peptide data were copied from HTML tables and pasted into Microsoft Excel spreadsheets for further processing and analysis. Identified proteins were mapped from their IPI to equivalent Swiss-Prot  [10]  identifiers using the ID mapping functionality of the UniProt website (http://www. uniprot. org/uploadlists/). Percentage sequence coverage, and numbers of non-distinct and distinct peptides were calculated using the Protein Coverage Summarizer program from the Pan-Omics Research division of the Pacific Northwest National Laboratory (http://omics. pnl. gov/software/protein-cover age-summarizer). UniProt IDs were used to generate a hyperlinked data table, as described  [11]. A subset of identified proteins was classified as being "background or contaminant" (at the level of gene symbols) based on two reference sources: the set of "contaminant" proteins published by the MitoCheck consortium  [12] , and the CRAPome database (http://www.
Acknowledgments We thank the  Taplin Biological Mass Spectrometry Facility  for information and advice. The research leading to the generation of these data received funding from the  European Research Council  (FP7/2007-2013 Grant agreement no. 233339 ). This work was also supported by the  Fondation ARC pour la Recherche sur le Cancer , and the  Fondation pour la Recherche Médicale en France (FRM). P. C. and J. R. A. H. were supported by post-doctoral fellowships from the  FRM. P. C. was also supported by a postdoctoral fellowship from the  ARC. D. L. was supported by a studentship from the  FRM  and the  Agence Nationale de Recherches sur le Sida et les Hépatites Virales (ANRS).
Second, the calculation of the cross-sections for a single collision energy, even the lowest one, converged as a function of the total angular momentum, takes several hundred of hours of CPU time and reaches quickly (around a collision energy of 1 cm À 1 ) a thousand of hours. Therefore, we developed an MPI version of our Newmat code  35  using asynchronous task parallelization. The elementary task is the propagation of the wavefunction at one particular collision energy. The MPI code distributes N tasks over M processors. Because the tasks are independent, this parallelization scheme requires no overhead and feeds efficiently all the processors. We included 25 rotational levels in each of the 19 vibrational levels used to perform the calculations. The propagation step size was taken to be 0.
Acknowledgements M. A. G. and M. H. acknowledge  Marie Curie International Research Staff Exchange Scheme Fellowship  within the 7th European  Community Framework Program  under Grant No. PIRSES-GA-2012-31754  and  COST ACTION   CM1405  MOLIM. Computer time for this study was provided by the  Mesocentre de Calcul Intensif Aquitain computing facilities of Universitede Bordeaux and Universitede Pau et des Pays de l'Adour. E. R. H. and S. K. acknowledge support from the  ARO, ARO-MURI  (grant Nos. W911NF-15-1-0121 ,  W911NF-14-1-0378 ) and  NSF  (grant Nos. PHY-1255526 ,  PHY-1308573 ).
7A, B ). To select the proteins that highly contributed to the two first dimensions of the PCA, we applied the 'dimdesc' function of the FactoMineR R package at the threshold of 0. 05. A total of 39 and 19 proteins, respectively, were detected as high contributors to Dim 1 (Cu1/Cu6 vs. Cu4; Fig. 7A ) and Dim 2 (Cu6-specific) in the apical part of the stem, whereas 22 and eight proteins, respectively, significantly contributed to these two principal components in the most lignified, basal region (in that case Dim 1 represents Cu1 vs. Cu4, and Dim 2 represents the Cu6-specific protein profile, Fig. 7B ). Similarly, we performed a third PCA on the 91 proteins which were initially selected according to the 'treatment' effect of the global ANOVAs (P-value < 0. 05) (Fig. 8 ). Again, the first dimension of the PCA (31.
05, global data set) and that highly contribute to differentiate Cu1 from Cu4 after applying the 'dimdesc' function of the R package FactoMineR at the threshold of 0. 05. Normalized abundances are represented by a color gradient from blue (negative values) to red (positive values). Additional data are available in Supplementary Table  S3. shown to have a distinct structure and metabolism  (Printz et al. 2015). A panel of 14 genes involved in cell wall synthesis (specifically cellulose and lignin) were selected and their expression level was followed by RT-qPCR. In accordance with  Printz et al. (2015) , multivariate analysis indicated that the genes involved in cellulose synthesis tend to be more expressed in the apical extremities of the stem. However, at the two lowest concentrations the apical fringes shared higher similarities in the gene expression profile with the basal mature segment than with the actively growing apical stem regions (Fig.
1 Cycle Sequencing and the BigDye XTerminator Purification kits, according to the manufacturer's instructions. The results were analyzed using the qBase PLUS version 2. 5 software (Biogazelle;  Hellemans et al. 2007 ) and normalized using translation initiation factor IIA (TFIIA) and elongation initiation factor 4A (eif4A)  (Guerriero et al. 2014). The primers used to amplify the reference genes, the CesA genes, PAL, CAD and SuSy, have already been described  (Guerriero et al. 2014). Two class III Pxd orthologs shown to be involved in lignification in A. thaliana (i. e. Pxd42 and Pxd53; E-values 0 and 6e-153, identity 84% and 59%, respectively)  (Nielsen et al. 2001 , Yokoyama and Nishitani 2006 , Marjamaa et al. 2009 , Saathoff et al. 2013)  were targeted. The primers to amplify the Pxds were the following: Pxd42Fwd T GCTGTTCAGAGTTGTGATGC, Pxd42Rev CCAAAGCTTCTGTCGTGTTC (designed on the M.
truncatula Refseq accession No. XM_003610383. 1) and Pxd53Fwd TGGTCCTGATTGGAAAGTGC, Pxd53Rev TTGAAAGCGGGACTA GGAAG (designed on the M. sativa GenBank accession No. AAB41811. 1). The expression levels are indicated as 'Normalized relative expression' (Supplementary Table  S2A , B) and have been analyzed by PCA. To validate our proteomics data, seven genes whose encoded proteins changed significantly in abundance with the treatment, as well as the Cu transporter HMA5 were selected and their expression followed by RT-qPCR. These genes were the following: Medtr7g013660. 1, copper chaperone ATX-1; Medtr7g069980. 1, ferritin-1; Medtr5g083170. 1, ferritin-2; Medtr4g014540. 1, ferritin-3; Medtr5g010420. 1, heavy metal P-type ATPase; Medtr2g046710. 1, S-adenosylmethionine synthase-like protein; Medtr6g027920.
Proteomics Proteomics extraction, labeling, migration and spot detection were performed as described previously  (Printz et al. 2013b). Spot selection for multivariate analysis. Following spot detection using the DeCyder software (v7. 0, GE-Healthcare), normalized spot volumes were extracted and standardized according to the spot volumes measured on the internal standard. All volumes were log(2) transformed. Only spots for which a value was obtained on at least three out of every four gels were conserved for statistical analysis. In cases where one missing value was reported, the fourth value was mathematically imputed by implementing the mean log(2)-transformed volume of the 10 spots having the nearest profile of expression on all other gels. Data were finally median normalized (Supplementary Table  S3A ).
Further processing was performed using R (v3. 1. 2). Homoscedasticity was tested and, depending on this test, an ANOVA or a test of equal means in a oneway layout in the case of unequal variances were applied. This procedure was performed on (i) the global data set and separately on (ii) the apical samples and (iii) the basal samples (Supplementary Table  S3B ). In cases where a treatment effect was reported for a spot (P < 0. 05) in at least one of the three statistical processings (treatment effect on the global data set, treatment effect on the apical samples, treatment effect on the basal samples), the spot was checked using the Decyder (v7. 0, GE-Healthcare) spot view and selected for picking. Spot picking, digestion and MS/MS analysis. Selected spots were picked with an Ettan Spotted Picker (GE Healthcare).
1% (v/v) TFA was added. A MALDI peptide mass spectrum was acquired using the AB Sciex 5800 TOF/TOF (AB Sciex), and the 10 most abundant peaks, excluding known contaminants, were automatically selected and fragmented. MS analyses were carried out as described previously  (Printz et al. 2013b). MS and MS/MS spectra were submitted for NCBInr database-dependent identification using the taxonomy Viridiplantae (2,471,722 sequences) (http://www. ncbi. nlm. nih. gov) downloaded on October 30, 2014 and containing 51,656,746 sequences on an in-house MASCOT server (Matrix Science, www. matrixscience. com). A second search was carried out against an EST (expressed sequence tag) Fabaceae database downloaded on December 17, 2013 and containing 19,932,450 sequences. The parameters used for these searches were mass tolerance MS 100 p.
Acknowledgements We are grateful to  Dr. Lucien Hoffmann  for the careful reading of the article. We acknowledge in particular  Celine Leclercq, Sebastien Planchon  and  Laurent Solinhac  for their day-to-day technical support,  Simone Zorzan  for valuable discussions concerning statistics,  Johanna Ziebel  for performing all ICP-MS analyses, and  Patrick Grysan ,  Esther Lentzen  and  Brahime El Adib  for their teachings on SIMS analysis.
6 -1. 0 2130 8 357468557 Ferritin-3 1. 9 1. 0 0. 9 0. 3 0. 7 0. 0 2248 8 47027073 2-cys peroxiredoxin-like protein 0. 9 0. 8 0. 3 -0. 2 0. 5 0. 6 g Represented are the proteins whose abundance varied significantly with the treatment (P-value ANOVA < 0. 05, global data set) and that highly contribute to differentiate Cu1 from Cu4 after applying the 'dimdesc' function of the R package FactoMineR at the threshold of 0. 05. Normalized abundances are represented by a color gradient from blue (negative values) to red (positive values). Additional data are available in Supplementary Table.
Genome Sequencing Genome sequencing data for each isolate were generated with both the Pacific Biosystems (PacBio; Menlo Park, CA, USA) RSII instrument and the Illumina HiSeq 2500 (San Diego, CA, USA). DNA was extracted from plated isolates by using ArchivePure DNA purification kit (5prime, Gaithersburg, MD, USA). PacBio sequences were generated by using P4-C2 sequencing chemistry and assembled using PacBio's Hierarchical Genome Assembly Process version 3 (HGAP)  (21). HGAP produces linear DNA sequences, so we identified circular, complete chromosome sequences on the basis of the existence of reads that bridged the 2 ends of the chromosome after 1 copy of the terminal repeat produced by the assembler was removed. These assemblies were corrected with 250-bp, paired-end Illumina read data generated with TruSeq Rapid SBS chemistry (Illumina) from 600-bp libraries prepared with NEBNext Ultra DNA library preparation kits (New England BioLabs, Ipswich, MA, USA).
The Illumina reads were trimmed with Trim Galore version 0. 3. 7 (Babraham Bioinformatics, Cambridge, UK) to remove reads below Q28, 100 bp, and an error rate of 0. 03, then mapped with bowtie version 2. 1. 0 (22)  and used to identify base-calling errors and indels by using freebayes version 0. 9. 16 (https://github. com/ekg/ freebayes) with base quality >20, alternate count >20, and coverage >100. The PubMLST (http://pubmlst. org/neisseria/) identifier for the sequences are in online Technical Appendix Table  1  and genome coverage information and statistics on each genome in online Technical Appendix Table  2.
outbreaks. Examination of the PubMLST database revealed only 2 observations of meningococcus with similar profiles: the same ST was observed in serogroup C strains from Nigeria during 2013-2014, and a similar ST (ST-9367, matching at 6 of 7 MLST loci) was represented by a nongroupable isolate from a carriage study in Burkina Faso during 2011. Although comparison between the ST-10127 NmC isolates from Niger 2015 and Nigeria 2013-2014 would reveal recent evolution of ST-10127 lineage, close comparison between ST-10127 and ST-9367 may illuminate recent evolution of the capsule locus. The origin of ST-10127 is unclear due to the limited number of genetically closely related strains. Additional invasive and carriage meningococcal strains that were collected from Africa and other countries in the past few decades should be examined at the genomic level to identify closely related strains and assess the genetic variations that have led to the emerging ST-10127 NmC.
Acknowledgments We thank the  CDC Core Facility , team members from CERMES for their laboratory support, the  PubMLST  staff for data management assistance, and the PIBnet team and sequencing facilities at the  Institut Pasteur. We also thank  Kristian Alfsnes ,  Nadia Debech , and  Inger Marie Saga  at the  Norwegian Institute of Public Health  for their technical assistance and  Médecins Sans Frontières  for sending the isolates to Norway. This work was funded by  CDC. Dr. Kretz  is a molecular microbiologist and bioinformatician in the  Bacterial Meningitidis Laboratory in the Meningitis and Vaccine Preventable Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta ,  Georgia. Her  research focuses on the molecular characterization and comparative genomics, using whole genome sequencing, of meningitis causing pathogens and more specifically N.