dataesr/scientific-paragraphs-categorization huggingface dataset:
A finer, smaller-scale analysis also including duplications is compatible with our results (Supplementary Figure  S7 ), but does not allow to formulate any specific hypothesis on the relative roles of intra-species transfers and duplications in families with overdispersed abundance profiles. Families with peaked abundance profiles or zero variance are not captured by the model, and hence falsify its null assumptions. Since their abundance varies less than expected across genomes, we speculate that these families might be subject to adaptive abundance constraints, i. e. the domains are needed only in a certain number of 'copies', probably corresponding to properties of the protein machines that they form. Notably, while the majority of the peaked abundance profiles correspond to families where little or no horizontal transfers are reported, there are peaked families with high HGT: we have considered the set of top 15% H f families for both the HGT-DB and DarkHorse data sets, and we found 37 and 35 peaked families, respectively.
These lists are mildly enriched only for the metabolism functional category (21/37, P = 0. 01 for HGT-DB data and 18/35, P = 0. 2 for DarkHorse; the full lists are given as supplementary files), while the other categories (including translation, which is enriched in peaked families) appear sparsely. The domains common to both lists include two domains for the metabolic enzyme cysteine methyltransferase, involved in DNA repair, the ligase domain ADC synthase, and a gated mechanosensitive ion channel domain. Notably, five distinct ATP synthase domains are found in the HGT-DB list of peaked families with high H f ; this gene has a very unusual phylogenetic distribution indicative of HGT  (56). Our observations lead to hypothesize the presence of a (to our knowledge so far unreported) constraint on the abundance, possibly of selective origin.
We use the Zurich ENvironmental Study (ZENS) database to investigate the environmental dependence of the merger fraction Γ and merging galaxy properties in a sample of ∼ 1300 group galaxies with M > 10 9. 2 M and 0. 05 < z < 0. 0585. In all galaxy mass bins investigated in our study, we find that Γ decreases by a factor of ∼ 2 -3 in groups with halo masses M HALO > 10 13. 5 M relative to less massive systems, indicating a suppression of merger activity in large potential wells. In the fiducial case of relaxed groups only, we measure a variation ∆Γ/∆ log(M HALO ) ∼ -0. 07 dex -1 , which is almost independent of galaxy mass and merger stage. At galaxy masses > 10 10. 2 M , most mergers are dry accretions of quenched satellites onto quenched centrals, leading to a strong increase of Γ with decreasing group-centric distance at these mass scales.
g. ,  Angulo et al. 2009). While this indirectly implies a dependence of galaxy mergers and interaction rates on environment, the relative importance of the LSS density, group halo mass, and location within group halos in facilitating galaxy encounters is still unclear. Also debated is whether galaxy mergers can affect both central galaxies and satellite galaxies in the same halos. Semianalytic models of galaxy formation seem to indicate that satellite-satellite mergers are rare and that most mergers involve central galaxies swallowing satellites (e. g. ,  Hatton et al. 2003; Guo et al. 2011) ; currently there is, however, little observational evidence for this. In this work we utilize the database of the Zurich Environmental Study (ZENS)  (Carollo et al. 2013b , hereafter Paper I) to make further progress on understanding the dependence of the merger fraction and the properties merging galaxies on (1) galaxy stellar mass, (2) rank of central or satellite within a group halo potential, and (3) local (mass of host group halo and group-centric distance) versus LSS environment.
In nine of these pairs we found the redshift of both companions in either the Sloan Digital Sky Survey (SDSS)  (York et al. 2000)  or the NED 4  databases (merger flag = 2 in the ZENS database, class 2 in this work); this confirmed a physical association of the two galaxies in a merger process. In the remaining cases for which no spectroscopic information is available (merger flags = 1, 1. 5 in the ZENS database, class 1 in this work), the presence of tidal features or disturbed morphologies often supports the merger scenario, although for galaxies flagged as 1 in the ZENS parent catalog a chance projection may not be excluded. We therefore checked and found that our results do not change if these systems are removed from the analysis. Given the typical separations and the total stellar masses of the 26 pairs we estimate using Equation (10) in  Kitzbichler & White (2008)  that these systems will merge on timescales of a ∼300 Myr (median value).
Comparison with Semianalytical Models In order to make a more quantitative comparison with theoretical expectations, we made use of the publicly available 11 data from the  Guo et al. (2011)  semianalytical model. In brief, the model follows the dynamics of the subhaloes hosting the satellite galaxies until their dark matter content exceeds their baryonic mass, switching a merger clock based on the estimated dynamical friction timescale afterward. By construction, satellite galaxies merge with the central of their halo. Groups fulfilling the ZENS specifics in terms of redshift, masses, number of members and b J magnitude of the galaxies were selected from a snapshot of the simulation box that was projected onto one of its axes in order to define the projected LSS density exactly as in Paper I and to compute projected distances among galaxies.
ACKNOWLEDGMENTS We acknowledge the support from the  Swiss National Science Foundation. This publication makes use of data from  ESO Large Program 177. A-0680. We also use data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. The Millennium Simulation databases used in this paper and the web application providing online access to them were constructed as part of the activities of the  German Astrophysical Virtual Observatory (GAVO).
In summary, the theoretical problem is two-fold: On the one hand E, and on the other hand F, are to be computed with 3PN precision or better. To obtain E we must control the 3PN equations of motion of the binary in the case of general, not necessarily circular, orbits; as for F it necessitates the application of a 3PN wave generation formalism. It is remarkable that such high PN approximation is needed in preparation for the LIGO and VIRGO data analyses. As we shall see, the signal from compact binaries contains the signature of several non-linear effects which are specific to general relativity. We thus have the possibility of probing, experimentally, some aspects of the non-linear structure of Einstein's theory  [84, 85, 15, 14].
Acknowledgments The author's works reviewed in this article have been done in a series of collaborations with K. G. Arun ,  Alejandro Bohé ,  Alessandra Buonanno ,  Thibault Damour ,  Steven Detweiler ,  Gilles Esposito-Farèse ,  Guillaume Faye ,  Bala R. Iyer ,  Alexandre Le Tiec ,  Sylvain Marsat ,  Samaya Nissanke ,  Olivier Poujade ,  B. S. Sathyaprakash ,  Gerhard Schäfer ,  Bernard F. Whiting ,  Clifford M. Will , and  Alan G. Wiseman.
For this reason we will distinguish in this paper only geomagnetic (current induced) and charge-excess (charge induced) radiation. The contribution of the geomagnetic emission mechanism, described as a time-changing transverse current by Kahn and Lerche  [12] , has been observed and described in several papers; see e. g. Refs. [3, [17] [18] [19] [20]. Studies on possible contributions of other emission mechanisms from air showers have also been reported  [21] [22] [23] [24] [25]. An observation of the charge-excess effect in air showers has been reported by the CODALEMA collaboration  [26]. We present the analysis of two data sets obtained with two different setups consisting of radio-detection stations (RDSs) deployed at the Pierre Auger Observatory. The first data set was obtained with a prototype setup  [27]  for AERA; the other one with AERA itself  [5] [6] [7]  during its commissioning phase while it consisted of only 24 stations.
B. Data pre-processing and event selection for the SD The incoming directions and core positions of air showers were determined from the recorded SD data. A detailed description of the trigger conditions for the SD array with its grid spacing of 1500 m is presented in Ref. [41]. However, as mentioned before, additional SD stations were installed in the neighborhood of the RDSs as infills of the standard SD cell. Because of these additional surface detectors, we used slightly different constraints as compared to the cuts used for the analysis of events registered by the regular SD array only. FIG. 3. Direction of the incoming shower, denoted as v, with respect to the position of RDS which is symbolically indicated by an antenna. The direction of the magnetic field vector is denoted by B and the direction ξ is defined by the projection of the vector v × B onto the ground plane.
C. Event selection and data analysis for the RDSs The data from the RDSs were used to determine the polarization of the electric field induced by air showers. Here we take advantage of the fact that the LPDAs were designed as dual-polarized antennas. The data for each of these two polarization directions were stored as time traces with 2000 samples (thus with a length of 10 µs). We use the Hilbert transformation, which is a standard technique for bandpass-limited signals  [42] , to calculate the envelope of the time trace. An example of such a trace is shown in the upper panel of Fig. 4 , which was recorded for an air shower with parameters: θ a = (30. 0 ± 0. 5) • , φ a = (219 ± 2) • and E = (0. 19 ± 0. 02) EeV near the AERA24 site. We took several measures to ensure good data quality for the received signals in the RDSs.
D. Coincidences between the SD and the RDSs The data streams for the SD and RDSs were checked for coincidences in time and in location. For the SD events we used the reconstructed time at which the shower hits the ground at the core position. For the timing of an event registered by one or more of the RDSs we used the earliest time stamp obtained from the triggered stations. We required that the relative difference in the timing between the SD events and the events registered by the radio detector is smaller than 10 µs. The distribution of the relative time difference of the coincident events registered with AREA is shown in Fig. 5. The shift of about 8 µs between the SD and RDS timing is caused by the different trigger definitions used for each of the two different detection systems: SD and RDS.
This figure clearly displays the prompt coincidence peak and some random events. The events selected for further analysis are within the indicated window in this figure. Our analysis is based on 37 air-shower events, 17 registered with AERA24, the other 20 registered with the prototype. We note here, that each event can produce several data points in our analysis. The distance d between the shower axis and the SD station closest to the center of the radio setup, the angle α between the magnetic field vector and the shower direction, and the shower energy E are relevant parameters for the RDS triggers discussed in Section II B. The upper (lower) panel of Fig. 6  displays a scatter plot of these coincident events in the (E, α) and (E, d) parameter space.
It was checked that the contribution from a cross correlation between the E j,x and E k,y samples can be neglected. From Q and U the polarization angle for each recorded shower and at each RDS was obtained using: φ p = 1 2 tan -1 U Q , ( 16 ) in which the relative sign of Q and U should be taken into account. The uncertainty on φ p is given by σ φp = σ 2 Q U 2 + σ 2 U Q 2 4(U 2 + Q 2 ) 2. ( 17 ) To assure good data quality for the registered time traces, only signals were considered that pass the following signal to noise cut S N = Q 2 + U 2 σ 2 Q + σ 2 U > 2 (18) For the data obtained with AERA24, we compare in Fig. 8  the measured polarization angle φ p (me. ) to the predicted polarization angle φ p (pr. ) as expected from a pure geomagnetic emission mechanism (a = 0). The error bar on the measured value φ p (me.
V. CONCLUSION We have studied with two different radio-detection setups deployed at the Pierre Auger Observatory the emission around 50 MHz of radio waves from air showers. For a sample of 37 air showers the electric field strength has been analyzed as a tool to disentangle the emission mechanism caused by the geomagnetic and the charge-excess processes. For the present data sets, the emission is dominated by the geomagnetic emission process while, in addition, a significant fraction of on average (14 ± 2)% is attributed to a radial component which is consistent with the charge-excess emission mechanism. Detailed simulations have been performed where both emission processes were included. The comparison of these simulations with the data underlines the importance of including the charge-excess mechanism in the description of the measured data.
Then, the aggregate of these requests (∑μ2) is sent by the hop-1 MPNCs to the ref-MPNC (Figure  17 ). Additionally, the hop-1 MDEVs send individual requests to the ref-MPNC, which follow an exponential distribution with mean equals to (μ1) μs. One request per each device is sent during a CAP. Several MATLAB simulations were run by using the parameters' values that are summarized in Table  2 , while taking different values of the means (μ1) and (μ2) as specified in the sequel. The satisfaction factor, the average requests' rejection rate, the average superframe utilization and the fairness index variations are studied. Figure  18  shows a comparison of the superframe utilization for different loads (μ1) when using the dynamic superframe size proposal or fixing the superframe size to its maximum value.
For this analysis the HLT selection for the J/ψ requires two muons, respectively µ 1 and µ 2 , originating at a common vertex, with the invariant mass of the muon pair lying between 2. 5 GeV and 4. 3 GeV. The individual muon p T thresholds are p T (µ 1 ) > 6 GeV and p T (µ 2 ) > 4 GeV. This study uses pp collision data collected in the years 2011 ( √ s = 7 TeV) and 2012 ( √ s = 8 TeV) by the AT-LAS experiment. The datasets used correspond to an integrated luminosity of 4. 9 fb -1 and 19. 2 fb -1 , respectively. The luminosity estimate has an uncertainty of 1. 8% in 2011  [11]  and 2. 8% in 2012. To improve the resolution, peaks are sought in the distribution of the variable Q = m(B ± c ππ) -m(B ± c ) -2m(π ± ), where m(B ± c ) is the offline reconstructed invariant mass of the B ± c candidate, m(B ± c ππ) is the invariant mass of the B ± c candidate combined with two charged pion candidates, and m(π ± ) is the charged pion mass  [10].
Here S is the number of signal events and B refers to the number of background MC events scaled according to the production cross section. The production cross sections are taken from the MC generators' predictions. The optimization has been carried out separately for the two different datasets. The pion candidate from the B ± c is required to have p T > 4 GeV. Hit requirements in the pixel detector and SCT are imposed on all tracks to ensure a reliable impact parameter reconstruction. B ± c candidates are reconstructed by fitting two muon tracks from the J/ψ candidate together with a pion candidate track to a common vertex. The invariant mass of the two muons is constrained to the world average of m(J/ψ). The yields per unit of integrated luminosity, and the central values of the dimuon invariant mass distributions are verified to be consistent within each dataset.
The signal yield per fb -1 of the B ± c is lower in 8 TeV data due to the harder p T requirements. The stability of the B ± c yield was checked through its normalization to B ± → J/ψK ± decays that were reconstructed with similar requirements. [MeV]  The reconstruction of the excited state candidates uses the B ± c ground state candidates within ±3σ of the fitted mass value of the corresponding dataset. These candidates are combined as described below with two pion candidate tracks associated with the corresponding primary vertex. The p T threshold of the pion candidates is 400 MeV. No additional selection requirement is applied to the B ± c (2S) pion candidates. The three tracks from the secondary vertex and the two tracks from the primary vertex are refitted simultaneously with the following constraints given by the decay topology: the refitted triplet of the B ± c tracks and the pair of PV pion tracks must intersect in two separate B ± c and B ± c (2S) vertices.
In terms of standard deviations the significance of the observation is 3. 7σ in the 7 TeV data and 4. 5σ in the 8 TeV data. For the combined 7 TeV and 8 TeV dataset the total significance of the observation is found to be 5. 2σ. The local significance of the observation, obtained by fixing the mean value of the signal component, is 5. 4σ. In conclusion, the distribution of the mass difference Q = m(B ± c π + π -) -m(B ± c ) -2m(π ± ) for events with the B ± c meson reconstructed in its decay to J/ψπ ± has been investigated in pp collisions at the LHC using the ATLAS detector. The analysis is based on an integrated luminosity of 4. 9 (19. 2) fb -1 of pp collisions at a centerof-mass energy of 7 (8) TeV. A new state is observed at Q = 288. 3 ± 3. 5 ± 4. 1 MeV (calculated as the error weighted mean of the 7 TeV and 8 TeV mass values) corresponding to a mass of 6842 ± 4 ± 5 MeV, where the first error is statistical and the second is systematic.
acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWF 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; EPLANET, ERC and NSRF, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, DFG, HGF, MPG and AvH Foundation, Germany; GSRT and NSRF, Greece; ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; BRF and RCN, Norway; MNiSW and NCN, Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZ Š, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of America.
1 ). This species has become a classic cnidarian model organism for studying symbiosis, physiology, cell biology, the calcification process and photosynthesis  [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44]. In the present study, 521,460 reads were generated from the coral holobiont, and were assembled as 15,052 contigs. We searched for putative coral protein homologs within a comprehensive non-redundant proteome database that included the complete genomes of six metazoan organisms: the cnidarians Nematostella vectensis (starlet sea anemone) and Hydra magnipapillata, the nematode Caenorhabditis elegans (worm), the arthropod Drosophila melanogaster (fruit fly), the echinoderm Strongylocentrotus purpuratus (purple sea urchin), the urochordate Ciona intestinalis (sea squirt) and the vertebrate Homo sapiens (human).
Sequence, Data Assembly and Analysis The sequences were submitted to Newbler assembler version 2. 6 for de novo assembly of 454-sequenced EST libraries using the default parameters. The assembled sequences were first automatically annotated with the SwissProt databases and then with several species-specific databases using the BLAST program. The species utilized in this analysis are as follows: the sea anemones Nematostella vectensis, Aiptasia pallida, Metridium senile, and Anemonia viridis; the stony corals Acropora millepora, Acropora palmata, Acropora digitifera, Montastraea faveolata, and Porites astreoides; the hydrozoans Clytia hemisphaerica and Hydra vulgaris; and the protist Symbiodinium sp. The best matches obtained using the following blast parameters: e-value #1e-10 , best-hit-overhang = 0.
Phylogenetic Analyses The alignments of all amino acid sequences were performed with the Multalin server  [46]  and phylogenetic relationships were investigated using Bayesian techniques as implemented in the computer program MrBayes v3. 1. 2 [47] , starting from a random tree, generating 3,500,000 generations with sampling every 1000 generations, and with four chains in order to obtain the final (consensus) tree and to determine the posterior probabilities at the different nodes.
EST Library Construction and Assembly The normalized and non-normalized cDNA libraries constructed from S. pistillata holobiont RNA starting materials were based on an RNA pool collected from different environmental conditions to maximize the diversity of rarely expressed genes. Normalization of the library decreased the amounts of abundant transcripts and maximized the chances of finding new genes. We divided the 454 plate to run normalized and non-normalized cDNA libraries to obtain both abundant and rare transcripts. The two datasets were merged before assembly to generate a database of 523,533 sequenced reads. Assembly of these reads produced in 15,052 contigs with a mean length of 1,078 bp and N50 1,256 bp. These results are available from the NCBI and from (http://data. centrescientifique.
Comparative Analysis S. pistillata transcript similarity searches were first performed against proteome libraries using blastX (see methods for specific parameters) (Table  1 ). At this level, 10,050 out of the 15,052 contigs (,67%) exhibited a positive match to proteins from one or more species. As expected, most of the S. pistillata transcripts matched sequences from the coral A. digitifera  [7]  and N. vectensis. The human proteome ranked third in terms of the number of hits, followed by the third diploblast Hydra magnipapillata. The two protostomes (the fruit fly and nematode) presented the lowest numbers of hits and were ranked last. The general tendency of the homolog presence across lineages is even more apparent if we group S. pistillata homologs across taxonomic groups, as shown in Fig 3.
Functional Characterization After the evolutionary comparisons of homology between related and non-related lineages were complete, we successfully classified 7,764 S. pistillata transcripts using the STRING database v. 6. 3  [49]  and a stringent assignment process. The graphical distribution of functional classes is illustrated in Figure  5. When comparing functional data between S. pistillata transcripts and the human proteome, we observed the presence of equivalent functional elements. These elements are sufficient to reconstitute key processes in signaling and cell adhesion, which we chose to further explore by focusing on Wnt and BMP pathways (Fig. 6 ). The wingless (Wnt) and bone morphogenetic protein (BMP) pathways are essentially independent signaling mechanisms, although they often regulate similar biological processes.