dataesr/scientific-paragraphs-categorization huggingface dataset:
ma5> plot PT(mu) [logY] We now turn to the description of all other options coming with the jet clustering module of MadAnalysis 5 and that are related to the identification of the physics objects. After the application of the algorithm, each particle is uniquely identified and cannot contribute to several collections of physics objects. A remark on leptons is however in order here. By default, the jet algorithm distinguishes leptons issued from the hard process from those originating from hadron decays. While the first category of leptons are reconstructed as leptons and included in the relevant collection, leptons of the second category are feeding the jet algorithm and therefore included as parts of the reconstructed jets. This default behavior can be modified by setting the variable main.
clustering. exclusive to false. This forces the jet algorithm to include all visible particles for jet reconstruction, so that a given lepton can consequently be part of the relevant lepton collection and also contribute to a specific jet. MadAnalysis also includes a simple algorithm dedicated to the b-tagging of the reconstructed jets, so that a jet is identified as originating from a b-quark when it can be matched to a b (anti)quark, i. e. , when it lies within a cone of radius R around one of the parton-level b quarks. The size of the cone (R) is specified as the value of the main. clustering. bjet id. matching dr variable, while setting the parameter main. clustering. bjet id. exclusive to true indicates the code that one cannot match several jets to one given b quark. By default, the b-tagging algorithm is perfect, although (mis)identification efficiencies can also be specified.
Summary MadAnalysis 5 is a unique, flexible and user-friendly framework allowing for professional analyses of simulations of collisions to be produced in high-energy physics experiments. It can be used equivalently for the investigation of parton-level, hadron-level or reconstructed events and is therefore able to read many input formats. The program comes with two modes of running, the so-called normal and expert modes. In the first case, the user is invited to define his/her analysis by typing command lines in a Python console. In the second case, he/she can directly implement his/her analysis in C++, using a large class of predefined functions included in the core of the package. In this paper, we have emphasized new functionalities related to an interface with the FastJet program and which include the possibility of using different jet algorithms to cluster hadrons into jets, when analyzing hadron-level events.
Acknowledgments The authors are grateful to the ACAT organizers for the very nice conference. They also thank  A. Alloul  for his numerous checks of the code and for his help during the development and the validation of the later releases. This work has been supported by the  French ANR   12 JS05 002 01 BATS@LHC  and by the  Theory-LHC France initiative  of the  CNRS/IN2P3.
After purification and end repair, these short cDNA were ligated to the sequencing adapters (60 bp on each side) and enriched by polymerase chain reaction (PCR, 12 cycles). A range of final cDNA fragments of 320 ± 20 bp was selected using E-GEL SIZE-SELECT 2% (Invitrogen). The 36 cDNA libraries were normalized and grouped by six in six independent lanes, and finally paired-end sequenced on an Illumina HiSeq™ 2000 at the GeT-PlaGe core facility (Genome and Transcriptome -Plateforme Génomique, Toulouse, http://www. get. genotoul. fr), using TruSeq PE Cluster Kitv3 (2 × 100 bp) and TruSeq SBS Kit v3. All the obtained data were submitted to the Short Read Archive (SRA, http://www. ncbi. nlm. nih. gov/sra/) at the National Center for Biotechnology Information (NCBI), in Bioproject PRJNA229186 under the accession number SRP033217.
Reference gonad transcriptome assembly and annotation The dataset contains a large number of reads made from gonad tissue in different conditions. To limit the assembly problems encountered during the first tests, using all data at once, a two-step strategy was chosen. First, in order to build highly represented transcripts, 10 million random reads were assembled using ABySS with k-mer values of 35, 45, 55, 65 and 75. The resulting contigs were filtered according to their length, keeping those longer than 100 bp. The remainders were meta-assembled with MIRA. In order to keep only highly represented ones, the initial reads were remapped (BWA 0. 6. 1, [46] ) on contigs and only the contigs with ≥ 500 aligned reads were kept. This produced 6,905 highly represented contigs, which were then used to filter corresponding reads in every sample read sets.
The remaining reads were merged and assembled using the previously presented procedure. Finally, a contigs with over ten reads aligned in at least two samples or more than 50 aligned reads in a sample were filtered. To assess the assembly quality, all read sets were realigned on the contigs and had an alignment rate of at least 80%. Assembled contigs were then functionally annotated in two ways. First, sequence similarities were sought by blastn/blastx with (i), a cut-off e-value of 10 -5 against the following databases: Pinctada fucata Proteins (http:// www. marinegenomics. oist. jp/ [47] ), Crassostrea gigas Proteins (http://oysterdb. cn/ [48] ), UniProtKB/Swiss-Prot Release 2012_06 of 13-Jun-2012, RefSeq Protein Index Figure  1  Histological features of the ten categories of P. margaritifera gonad.
Inversion category characterizes gonads in sexual inversion and thus presents both types of germ cells, male and female. And undetermined gonads are those which have no gametes and cannot therefore be associated with a sex. Ov: oocytes; Spc: spermatocytes; Spg: spermatogonia; Spz: Spermatozoid, and T: Connective tissue. Blast of 27-Jun-2012, Pfam Release 26. 0 of Nov-2011, RefSeq RNA Index Blast of 27-Jun-2012; and (ii), a cut off e-value of 10 -2 against the following databases: TIGR Fugu FGI 3. 0, TIGR ZebraFish ZGI 18. 0, UniGene Fugu Build #9, UniGene Human Build #232, UniGene Zebra-Fish Build #125, Ensembl Ciona Transcripts CSAV2. 0 67, Ensembl Fugu Transcripts FUGU4 67, Ensembl Human Transcripts GRCh37 67, Ensembl Tetraodon Transcripts TETRAODON8 67 and Ensembl ZebraFish Transcripts Zv9 67.
Differential expression (DE) analysis Differential level expression of contigs between the ten different gonadic categories was tested using the DESeq package v1. 11. 3  [51]  by use of the negative binomial distribution and a shrinkage estimator for the distribution's variance. The analysis was performed after library normalization (function estimatesizefactor) of the contigs count table (RNAseq quantification, Additional file 2: Table  S2 ), and following the standard procedure. Contigs considered as statistically significant differentially expressed were those showing an absolute value of Log2FoldChange > 2 i. e. , exhibiting a fourfold increased expression under one of the conditions; and a padj < 0. 001, p-value adjusted with a false-discovery rate (FDR) correction for multiple testing by Benjamini-Hochberg method  [52].
Real time PCR In order to validate RNAseq data and expression profiles obtained from the DESeq analysis, real-time PCR was performed on 14 genes on the same samples. Among these genes, 11 were differentially expressed between the ten gonadic categories and distributed in different expression profiles; the remaining three genes did not show statistically differences in expression. Approximately 2. 5 μg of total RNA of each sample obtained as previously described, were reverse-transcribed using M-MLV Reverse Transcriptase and amplified by real time PCR. The amplification reaction contained 5 μL 2X SYBR green qPCR Mix, 1 μL cDNA template, and 2 μL of each primer (1 μM) in a final volume of 10 μL. Each run included a positive cDNA control (reverse-transcribed pool of 0. 5 μg total RNA of each sample) and a blank control (water) for each primer pair.
Functional annotation Blastx/Blastn searches of the 70,147 contigs with the different databases revealed 43,616 (62. 2%) with significant matches to existing protein sequences. Among these, 33,448 (53. 9%) presented best hits with Pinctada fucata proteins, 9,517 (21. 8%) with Crassostrea gigas proteins, and 2,744 with the three generic databases Swiss-Prot (362; 0. 8%), RefSeq Protein (2,205; 5. 1%) and RefSeq RNA (177; 0. 4%) (Table  1 ). Gene ontology (GO) assignment was carried out on contigs in order to categorize the transcripts by putative function. Of 70,147 contigs, 6,394 (9%) were assigned with one or more GO term. Finally, 311,086 GO assignments were obtained, with a total of 10,001 GO terms. The assignments fall into the three major GO functional domains. Thus, among the 9%, 4,751 (74%) are involved in biological process, 5,498 (86%) are cellular components and 5,415 (85%) have molecular functions (Figure  3 ).
The three helices (H1, H2 and H3; blue boxes), the two wings (W1, W2) and the β-strands (orange boxes) structuring the Forkhead domain are indicated. (H) The phylogenetic tree generated using the Forkhead domain of the FOX proteins. Alignments were generated using Clustal W2. Identical amino acids and amino acids with conserved similarities are indicated by asterisks and by dots/colons, respectively. The numbers of amino acid residues at the beginning and at the end of the different domains are indicated. The phylogenetic trees were generated using MEGA v6 via the neighbor-joining method. Numbers in the branches represent the bootstrap values (as a percentage) from 100 replicates. GenBank accession numbers of the reference sequences are listed in Additional file 1: Table  S1. K-means clustering was performed on differentially expressed contigs and ten distinct clusters with similar expression patterns were produced (Figure  6 ).
Reference transcriptome of the gonad of Pinctada margaritifera A reference gonad transcriptome was de novo assembled based on the sequencing of 36 cDNA libraries, each corresponding to gonad samples of different reproductive stages and sexes. Finally, 70,147 contigs with an average length of 1,294 bp were obtained. Approximately twothirds of the contigs had significant matches against sequences of existing proteins and 9% were annotated using Gene Ontology terms. This result is higher than recently reported in transcriptomic analysis of other bivalves  [66, 67] , supposedly because the complete C. gigas and P. fucata protein databases have become available since their genomes were sequenced  [47, 48]. Moreover, the higher number of contigs (10,294 more) with significant matches with P. fucata proteins than with C.
gigas proteins would likely be due to the closer relationship between the two Pinctada species. From our sequencing effort, we identified a total of 196 in silico SSRs motifs and 3,667,510 putative in silico SNPs available for future genetics studies. This database and the available expressed sequences  [6]  will accelerate the development of both genomics and genetics in this commercially-important species and may also benefit the recently published draft genome of Pinctada fucata. For our main interest, this work successfully allowed the relation of numerous mRNA orthologs to sex determination/differentiation and gonad development genes.
Acknowledgments This study was conducted as part of the  ANR  " PolyPerl" project  ( ANR-11-AGRO-006 ) and the  Contract Project "BiodiPerl ". The authors are indebted to  J. Y. Daniel ,  V. Quillien ,  V. Vonau ,  J. Fievet ,  C. Cabrau ,  L. Quintric ,  F. Marquer  and all the staff of the  GeT-PlaGe core facility  (http://www. get. genotoul. fr/) for their helpful assistance. We thank  H. McCombie  for her help with editing the English. V. Teaniniuraitemoana is financially supported by  Ifremer  and holds a grant from la  Délégation à la Recherche de Polynésie française.
1% (51%) -RefSeq RNA 177 (5265) 0. 4% (12. 1%) -Otherdatabases 7833 (19131) 18% (43. 9%) 988 bp. The deduced amino acid sequences are 280aa and 329aa long, respectively, and contain the DM domain consensus sequence (from aa 12 to 66, Contig_44478; from aa 23 to 77, Contig_639) with conserved cysteines and histidines characteristic of the DMRT protein family (Figure.
Current experiments are providing measurements of the flux power spectrum from the Lyman-α forests observed in quasar spectra with unprecedented accuracy. Their interpretation in terms of cosmological constraints requires specific simulations of at least equivalent precision. In this paper, we present a suite of cosmological N-body simulations with cold dark matter and baryons, specifically aiming at modeling the low-density regions of the inter-galactic medium as probed by the Lyman-α forests at high redshift. The simulations were run using the GADGET-3 code and were designed to match the requirements imposed by the quality of the current SDSS-III/BOSS or forthcoming SDSS-IV/eBOSS data. They are made using either 2 × 768 3 1 billion or 2 × 192 3 14 million particles, spanning volumes ranging from (25 Mpc.
2LPT All our simulations are tuned to obtained a given σ 8 at z = 0. This is done with the spnorm Python script that rescales the total matter power spectrum P CAMB S issued from CAMB before generating the initial conditions such that P S = P CAMB S ×        σ 8 (z i ) σ CAMB 8 (z i )        2 , (3. 1) where z i is the redshift at which the initial conditions are run, σ CAMB 8 is the value of σ 8 obtained with CAMB for a chosen cosmological model, and σ 8 (z i ) = σ 8 (z = 0) σ CAMB 8 (z = 0) × σ CAMB 8 (z i ). (3. 2) Thus, in the 2LPTIC code, the power spectra are taken from CAMB at z = 0 and scaled back to the initial redshift z i = 30 by explicitly forcing the simulation to achieve the desired value of σ 8 at z = 0. While CAMB includes radiation, this is not the case for GADGET.
Gadget-3 GADGET-3 (GAlaxies with Dark matter and Gas intEracT) is a massively parallel tree-SPH code for cosmological simulations, originally developed by Volker Springel and collaborators  [44, 72]. It is written in ANSI C, and uses the standardized message passing interface (MPI) along with several open-source libraries (GSL 5  , FFTW 6  ). Gravitational interactions are computed via a hierarchical multipole expansion using the standard N-body method, and gas-dynamics are followed with smoothed particle hydrodynamics (SPH); collisionless dark matter and gas are both represented by particles. Since its original version (GADGET-1), the code underwent a series of improvements and optimizations over several years (GADGET-2 and 3), to maximize the work-load balance and the efficiency in memory consumption and communication bandwidth.
In what follows, we briefly describe the key features of the code. GADGET-3 follows a collisionless fluid with the standard N-body method, and an ideal gas with smoothed particle hydrodynamics (SPH). The code solves simultaneously for the dynamics of the collisionless component and of the ideal gas, both subject to and coupled by gravity in an expanding background space. The N-body implementation only differs from other cosmological codes by the accuracy of the gravitational field computation. A number of further physical processes have also been implemented in GADGET-3, from radiative cooling/heating physics to non-standard dark matter dynamics, star formation and feedback. In figure  3 , we present the evolution of a filament with redshift and in figure  7  we show the image of a snapshot made with splotch 7.
Such realizations can be used for visual confirmation before quantitative analysis as well as for public outreach and education. Several optimization strategies have been added in GADGET-3. These include a Peano-Hilbert space decomposition, a massively parallel version of the Fast Fourier Transform library, the possibility of splitting the simulation data across several files (to facilitate and speed-up the input/output process), and the fact that the code can be run on an arbitrary number of processors. In its current version, GADGET-3 is highly efficient in memory consumption (it allocates up to 80 bytes per particle) and communication bandwidth, is versatile and flexible, accurate and fast. Another important aspect is the scalability of the code, i. e. its performance when the number of processors is increased, which has currently been tested up to 16,000 cores.
Although the correction factors show discontinuities at the boundary where the simulation chosen as reference changes, the spliced power spectrum is continuous by construction. In the large-mode splicing regime, at k > k Nyq,100 /4, it is unclear whether a constant box-size correction or even any correction at all is indeed the optimal combination, since both the correction factor and the residuals are at the same level of about 0. 95. This regime, however, is only probed by the medium resolution SDSS-III/BOSS data in the highest redshift bins where measurement uncertainties significantly exceed the splicing errors. Its optimization is thus beyond the scope of this paper. or 256 3 particles in a volume of (25 Mpc/h) 3 or (100 Mpc/h) 3. Dashed vertical lines illustrate the regime boundaries.
[29]. The parameter f Si III is adjusted and v is fixed at 2271 km/s. We model the imperfection of the resolution of BOSS spectra though a multiplicative term. Finally, we allow for imperfection in the noise estimate of the BOSS spectra with eight additive terms (one for each redshift bin). Figure  8  illustrates the good agreement between the data and the simulations. Without any adjustment of the cosmological and astrophysical parameters, the χ 2 per number of degrees of freedom is already better than 1. 2. The good agreement between data and simulation covers the whole redshift range, z = [2. 1 -4. 5], in contrast with the cosmological analysis described in Palanque-Delabrouille et al. [1]  which was performed over the reduced redshift range z = [2. 1 -3. 7]. This simple comparison demonstrates the improvement obtained with these simulations over the previous generation of simulations  [24].
We have performed several check runs to ensure the quality and validity of our simulation grid, using either different seeds, or off-the-grid values of the cosmological and astrophysical parameters. These checks were all consistent with the power spectrum predicted using our second-order Taylor expansion, thus validating it. We compared our central simulation to published data from BOSS and showed that they were already in good agreement without any adjustment of any of the simulation parameters. In forthcoming work, we will use this Taylor expansion for a quantitative comparison to data in order to extract best-fit cosmological parameters. These simulations are accompanied by a set of simulations where massive neutrinos are included. These required additional developments for an efficient treatment and a proper account of the additional particles (at all levels of the pipeline: in CAMB, in the setup of the initial conditions for thermal velocities, in Gadget-3, etc.
Acknowledgments We acknowledge  PRACE (Partnership for Advanced Computing in Europe)  for awarding us access to resource curie-thin nodes based in France at TGCC, under allocation number  2012071264. This work was also granted access to the resources of CCRT under the allocation 2013-t2013047004 made by  GENCI  ( Grand Equipement National de Calcul Intensif). A. B. , N. P. -D. , G. R. and Ch. Y. acknowledge support from grant  ANR-11-JS04-011-01  of  Agence Nationale de la Recherche. M. V. is supported by  ERC-StG  "CosmoIGM". JSB acknowledges the support of a  Royal Society University Research Fellowship. We thank  Volker Springel  for making GADGET-3 available to our team.
Moreover, two additional peaks were detected at higher q values (q 2 ffi 0:25 nm À1 and q 3 ffi 0:38 nm À1 ), which can also be related to the hexagonal short-range order of the rods. Before discussing the field dependence of dAE=d, we provide an analysis of the SANS data in the saturated state. For fully saturated particles, like the Co nanorod array under study at a magnetic field of 0 H ¼ 2 T, equations (1) and (  2 ) reduce to equations (  3 ) and (4). We now assume that both Fourier coefficients j Ñ Nj 2 and j M M z j 2 are independent of the orientation of q [as supported by the two-dimensional data shown in Figs. 5(a b À2 H j Ñ NðqÞj 2 þ j M M z ðqÞj 2. By assuming that j M M z j 2 at saturation is independent of the orientation of the externally applied magnetic field, one can combine these two equations and separate the nuclear from the longitudinal magnetic SANS: 8 3 V j Ñ NðqÞj 2 ¼ 2 dAE ?;sat d À dAE jj;sat d ; ð5Þ 8 3 V b 2 H j M M z ðqÞj 2 ¼ dAE jj;sat d À 8 3 V j Ñ Nj 2 : ð6Þ The so-determined experimental nuclear j Ñ NðqÞj 2 and longitudinal magnetic j M M z ðqÞj 2 SANS cross sections are shown in Fig.
Summary and conclusion We have reported the results of magnetic field dependent unpolarized SANS experiments on a Co nanorod array. Measurement of the SANS cross section dAE=d in a saturating applied field of 2 T for two different scattering geometries (H?k i and Hjjk i ) allows us to separate nuclear from magnetic SANS without employing the usual sector averaging in unpolarized SANS. The ratio of the experimentally determined nuclear-to-magnetic scattering is in good agreement with the theoretically expected value. The total SANS data in the saturated state (as well as the corresponding nuclear and magnetic contributions) could be well described by a model that combines a structure factor with the form factor of a cylinder. The obtained structural parameters (cylinder radius and centre-to-centre distance) of the Co nanorod array are consistent with the results from electron microscopy.
Proteins were separated on SDS-PAGE and phosphoproteins were revealed by autoradiograph. Microscope image acquisition and data analysis. Fluorescence images were acquired using the AIM 4. 2 software (Zeiss, Jena, Germany) and the confocal LSM510 Meta system (Zeiss) with a × 63 objective and a sequential acquisition setting at 1024 × 1024 pixel resolution; for each image, 0. 5 μm sections were acquired to span the entire cell and z-projection was obtained using the AIM 4. 2 software. Images were processed using ImageJ (National Institute of Health, Bethesda, MD, USA) and Adobe Photoshop software (Adobe Systems Incorporated, San Jose, CA, USA). Quantification analyses were performed blind after randomization of the images. Quantification of WB analysis was performed by means of computer-assisted imaging (ImageJ) after normalization on tubulin or GST levels.
Values are expressed as mean ± S. E. M. of at least three independent experiments. For surface/total ratio assays and colocalization analysis, cells were chosen randomly for quantification from four different coverslips (two or three independent experiments), images were acquired using the same settings/exposure times and at least eight cells for each condition were analyzed. Colocalization analysis was performed using AIM 4. 2 software (Zeiss) on a pixel basis. We draw an ROI of the area of interest and we evaluated the colocalization coefficients and expressed it as the percentage of colocalization. For quantification of surface/total ratios, all images were analyzed using Image J. The average intensity of surface fluorescence staining was determined after cell tracing, and normalized to the total intensity to correct for differences in expression.