dataesr/scientific-paragraphs-categorization huggingface dataset:
edu/bioinformatics-in-biomed/velvet-and-oasestranscriptome). Anti-sense chimeras accidentally produced during the assembly step were cut with a home-made script. Then, independent assemblies were pooled, and duplicate/ similar transcripts build by close k-mers were removed by a cd-hit-est  [61]  step (parameters: -M 0 -d 0 -c 0. 98) and merged by a TGICL  [62]  step (parameters: -l 60 -p 96 -s 100,000). After this assembly process, all input reads were mapped back to the set of transcripts using BWA.
ORFs prediction and functional annotation Open Reading Frame (ORFs) were predicted by Fra-meDP software  [63]  using a home-made reference database consisting of an enriched SwissProt database  [64]  with venom peptides from Hymenoptera phylum. Blast search (version 2. 2. 26+) against nr protein and SwissProt databases was performed for ORFs annotation  [65]. In addition, domain annotation was achieved with the standalone version of InterProScan  [66]. Under Blast2Go software (version 2. 5. 0) [67] , a final annotation was performed by combining Blast and InterProScan results. Furthermore, contigs with no predicted ORFs were also annotated. In order to detect putative toxins, reviewed databases from Uniprot annotation program Tox-Prot  [68]  and home-made mature peptide sequences database were used.
See figure on previous page. ) The predicted phospholipase A1-like (PLA1) from T. bicarinatum. (A) Amino acid alignment of the sequences of PLA1-like from T. bicarinatum Tb31783 with PLA1 from S. invicta [GenBank: Q68KK0], Vespa crabro [GenBank: P0CH87], Vespula vulgaris [GenBank: P49369], Vespula maculifrons [GenBank: P51528] and Vespula germanica [GenBank: Q3ZU95]. Red boxes indicate PLA1 active sites according to [38] and green arrowheads indicate unique features for T. bicarinatum PLA1-like. Strictly conserved and non-conserved cysteine residues are shaded with black and grey, respectively. (B) Phylogenetic relationships of PLA1 from insect species based on amino acid sequence alignment. Numbers at nodes indicate bootstrap supports based on 100 replicates. Protein codes are as described in Figure 8A.
Acknowledgements The authors thank  Jérome Orivel  for providing and identifying ant specimens. We are grateful to  Beckman Coulter Genomics  services for cDNA libraries construction and sequencing and to the  GenoToul bioinformatics facility, INRA of Toulouse  for providing computational resources. Funding for this work was provided to WB by a grant from the  Région Midi-Pyrénées, Toulouse (France).
(Figure 8 8 See figure on previous page. ) The predicted phospholipase A1-like (PLA1) from T. bicarinatum. (A) Amino acid alignment of the sequences of PLA1-like from T. bicarinatum Tb31783 with PLA1 from S. invicta [GenBank: Q68KK0], Vespa crabro [GenBank: P0CH87], Vespula vulgaris [GenBank: P49369], Vespula maculifrons [GenBank: P51528] and Vespula germanica [GenBank: Q3ZU95]. Red boxes indicate PLA1 active sites according to [38] and green arrowheads indicate unique features for T. bicarinatum PLA1-like. Strictly conserved and non-conserved cysteine residues are shaded with black and grey, respectively. (B) Phylogenetic relationships of PLA1 from insect species based on amino acid sequence alignment. Numbers at nodes indicate bootstrap supports based on 100 replicates. Protein codes are as described in Figure 8A.
The European Union Committee of Experts on Rare Diseases was entrusted with aiding the European Commission in a number of tasks, ranging from the monitoring of initiatives, to recommending improvements and actions to be pursued in the future, in addition to helping strengthen liaison at both European and International levels in the field of rare diseases. The three-year mandate of the EUCERD drew to a close in July 2013 with an impressive record. The EUCERD has laid down the foundations for future work so as to continue to advance in the key areas that have been identified as of interest for the rare disease community at large: centres of expertise, European Reference Networks, patient registries and databases, newborn screening, and indicators for national rare disease plans/strategies.
Acknowledgements The authors would like to thank all the Members of the EUCERD for their implication in the work described in this article. The work of the Scientific Secretariat of the European Union Committee of Experts on Rare Diseases was financed by two consecutive Joint Actions between Member States from the  European Commission's Executive Agency for Health and Consumers: the Scientific Secretariat of the European Union Committee of Rare Diseases Joint Action   N°2008 22 91  and  European Union Committee of Rare Diseases Joint Action: Working for Rare Diseases   N°2011 22 01.
NA61/SHINE (SPS Heavy Ion and Neutrino Experiment) is a multi-purpose experimental facility to study hadron production in hadron-proton, hadron-nucleus and nucleus-nucleus collisions at the CERN Super Proton Synchrotron. It recorded the first physics data with hadron beams in 2009 and with ion beams (secondary 7 Be beams) in 2011. NA61/SHINE has greatly profited from the long development of the CERN proton and ion sources and the accelerator chain as well as the H2 beamline of the CERN North Area. The latter has recently been modified to also serve as a fragment separator as needed to produce the Be beams for NA61/SHINE. Numerous components of the NA61/SHINE setup were inherited from its predecessors, in particular, the last one, the NA49 experiment. Important new detectors and upgrades of the legacy equipment were introduced by the NA61/SHINE Collaboration.
Introduction NA61/SHINE (SPS Heavy Ion and Neutrino Experiment)  [1]  is a multi-purpose facility to study hadron production in hadron-proton, hadron-nucleus and nucleus-nucleus collisions at the CERN Super Proton Synchrotron (SPS). The NA61/SHINE physics goals include: (i) study the properties of the onset of deconfinement  [2]  and search for the critical point of strongly interacting matter which is pursued by investigating p+p, p+Pb and nucleus-nucleus collisions and (ii) precise hadron production measurements for improving calculations of the initial neutrino beam flux in the long-baseline neutrino oscillation experiments  [3, 4]  as well as for more reliable simulations of cosmic-ray air showers  [5, 6]. The experiment was proposed to CERN in November 2006  [1]. Based on this proposal pilot data taking took place in September 2007.
The Memorandum of  Understanding [7]  between CERN and the collaborating institutions was signed in October 2008. The first physics data with hadron beams were recorded in 2009 and with ion beams (secondary 7 Be beams) in 2011. The first experimental results were published in  [8, 9]. NA61/SHINE has greatly profited from the long development of the CERN proton and ion sources, the accelerator chain, as well as the H2 beamline of the CERN North Area. The latter has recently been modified to also serve as a fragment separator as needed to produce the Be beams for NA61/SHINE. Numerous components of the NA61/SHINE setup were inherited from its predecessors, in particular, the last one, the NA49 experiment. The layout of the NA61/SHINE detector is sketched in Fig. 1. It consists of a large acceptance hadron spectrometer with excellent capabilities in charged particle momentum measurements and identification by a set of six Time Projection Chambers as well as Time-of-Flight detectors.
The high resolution forward calorimeter, the Projectile Spectator Detector, measures energy flow around the beam direction, which in nucleus-nucleus reactions is primarily a measure of the number of spectator (non-interacted) nucleons and thus related to the centrality of the collision. For hadron-nucleus interactions, the collision centrality is determined by counting low momentum particles emitted from the nuclear target with the LMPD detector (a small TPC) surrounding the target. An array of beam. Schematic layout of the NA61/SHINE experiment at the CERN SPS (horizontal cut in the beam plane, not to scale). The beam and trigger counter configuration used for data taking on p+p interactions in 2009 is presented. The chosen right-handed coordinate system is shown on the plot. The incoming beam direction is along the z axis.
The latter allows mass (A) determination of the ions for momenta lower than 20A GeV/c and thus a check of the purity of the 7 Be beam. This is illustrated in Fig. 6  where the to f spectra of ions for the carbon (le f t) and beryllium (right) peaks resulting from fragmentation of the primary Pb beam at 13. 9A GeV/c are shown. The carbon spectrum shows clear evidence for two isotopes, whereas only the single Be isotope ( 7 Be) is seen in the Be spectrum. We therefore decided to select 7 Be for data taking. The fragment separation method was tested in 2010 with a 13. 9A GeV/c Pb beam incident on the primary target of the H2 beamline with the 4 cm thick degrader in place. Figure  7  shows for a given rigidity setting the charge distributions resulting from two collimator settings which optimize either the 7 Be (filled purple histogram) or the 11 C (open blue histogram) content.
The surface of the pipes did not show any excessive charging-up which could distort the drift field in the active VTPC volume. Gas tightness of the fixations of the pipes to the Mylar foils closing the VTPC field cage was tested by monitoring of the oxygen content in the VTPC gas. The oxygen contamination of the working gas decreased to a few ppms during the first 48 hours of purge (see Fig. 13 ) and remained constant at this level. The measured level of oxygen impurity after gas stabilization in the VTPCs was the same as that observed during the operation before the installation of the He beam pipes. Figure  14  shows the interaction vertex distribution along the beam axis (data for p+p interactions at 158 GeV/c) before the He beam pipe installation (2009 and 2010 data) and with the He beam pipes installed and filled with helium gas (2011 data).
Feedback to and control of the Busy logic is performed via RCB8047 CORBO register units in the pertinent VME crates. The readout of the PSD, is designed along the principles of the TPC readout, and acts similarly to the Mother Boards in the TPC readout system. Due to the push-data mode method, the event data in the different hardware channels arrive at the Central DAQ software asynchronously. The synchronization is performed via trigger counters in the sub-events on the final event-building level. For periodic checks the data stream is halted each minute, the data pipelines are drained and the trigger counter synchronicity is verified. In addition, the Busy signal of each hardware channel is monitored via a custom made galvanically isolated TTL to RS232 register in order to make sure that the event stream is never halted due to a stuck Busy signal caused by a hardware failure.
The Central DAQ software runs on a single Central DAQ PC (X7DB8-X motherboard, 64 bit, total 8 cores of Intel Xeon CPU @ 2 GHz, 8 GB memory, 10 PCI-X slots, 4 USB ports, and a serial port). The main components of the software and their functions are as follows: (i) The core software of the Central DAQ written in C. Its user interface is a command line interpreter written using the GNU libreadline library. (ii) The Central DAQ GUI script written in Tcl/Tk language. (iii) The event server, created upon Central DAQ start for event monitoring. This is a fork-server serving up to 16 monitoring clients. (iv) The following processes created upon run start: (i) The logger and monitoring / consistency checking process. (ii) The communication unit with the trigger system for summary information. This periodically gets scaler status information from the trigger server.
(iii) The communication unit with the DCS system for summary information. This periodically gets information from the DCS server. (iv) The recorder process building the event, writing to disk and forwarding to the above event server upon request. This starts the following two processes for efficient parallelization of data receiving. (i) The receiver process for the DDL channels. This looks for data on any DDL channel on a first-come first-served basis. (ii) The receiver process for the VME channels. This looks for data on any VME channel on a first-come first-served basis. The GUI and the command line interpreter communicate via standard Unix pipes and log files. The processes of the forked C program communicate via Linux shared memory. The data itself gets read into a special fast shared memory, called Physmem, outside the reach of the Linux kernel.
) via various interfaces (RS232, Caenet, TCP/IP, GPIB, etc. ), performs all the measurements and makes the results accessible for the clients via the Channel Access protocol. The server runs constantly regardless of the presence of the clients or the database availability. All measured data is stored in the relational database (PostgreSQL) by one of the EPICS clients. It can be accessed only via the Database Access Server by issuing ASCII commands. There are two GUIs available. One of them, which controls the Measurement Server and monitors its performance, is an EPICS client and can be run only in the experimental area. The other one can be run anywhere and is graphically presenting results of measurements retrieved from the database.
Figure1. Schematic layout of the NA61/SHINE experiment at the CERN SPS (horizontal cut in the beam plane, not to scale). The beam and trigger counter configuration used for data taking on p+p interactions in 2009 is presented. The chosen right-handed coordinate system is shown on the plot. The incoming beam direction is along the z axis. The magnetic field bends charged particle trajectories in the x-z (horizontal) plane. The drift direction in the TPCs is along the y (vertical) axis.
Acknowledgements: This work was supported by the  Hungarian Scientific Research Fund  (grants  OTKA 68506  and  71989 ), the  Polish Ministry of Science and Higher Education  (grants  667/N-CERN/2010/0 ,  NN 202 48 4339  and  NN 202 23 1837 ), the  National Science Center of Poland  (grant  UMO-2012/04/M/ST2/00816 ), the  Federal Agency of Education of the Ministry of Education and Science of the Russian Federation  (grant  RNP 2. 2. 2. 2. 1547 ), the  Russian Academy of Science  and the  Russian Foundation for Basic Research  (grants  08-02-00018 ,  09-02-00664 , and  12-02-91503-CERN ), the  Ministry of Education, Culture, Sports, Science and Technology, Japan ,  Grant-in-Aid for Scientific Research  (grants  18071005 , 19034011, 19740162, 20740160 and 20039012), the  German Research Foundation  (grants  GA 1480/2-1 ,  GA 1480/2-2 ),  Bulgarian National Scientific Fondation  (grant  DDVU 02/19/ 2010 ),  Ministry of Education and Science of the Repub-.
0; see Additional file 5), and the same geographic cline was apparent over the whole distribution range of the North Atlantic eels (data not shown). The geographic groupings were statistically robust as revealed by pairwise non-parametric tests. Based on the inferred ancestry proportions in STRUCTURE, significant differentiation was apparent among four out of the six comparisons among groups within continents. In Europe, differentiation was apparent among Southern and Northern European samples before and after Boxcox transformation (P = 0. 0190 and 0. 00003, respectively), and the same trend was found between the intermediate ranges and the southern locations (P = 0. 0233). The intermediate ranges were only marginally different from the northern locations (P = 0. 094). Among American eels, both southern and northern groups were statistically different from the intermediate group after Boxcox transformation (P = 0.
Hence, in a second, more realistic attempt, we performed a new set of simulations accounting for the observed diversity of admixture values derived from STRUCTURE (see Additional file 3). Following this method we did not produce F1 genotypes with intermediate admixture values, but admixed genotypes satisfying admixture values of near to Q expected = 0. 5 (the actual values of virtual admixed individuals equalled Q observed = 0. 55 given the real dataset). As a result we were able to recapitulate the same observation and pattern as in analysis 1 (Figure  6 ). Finally, assuming an IM model as implemented in IMa2, we were able to refute the possibility that incomplete lineage sorting and thus ancient polymorphism skewed our data. We then directly estimated the reciprocal migration rates among the two Atlantic eel species.
Microsatellite genotyping and summary statistics Nearly all North Atlantic eel specimens (n = 1042) have been successfully genotyped using nine microsatellite markers. Original genotypes for seven microsatellite loci  [39, 40]  were supplemented with two additional loci, Ang075 (Genbank AF237903; Primer sequences, Ang075-F, TATCAGGAACTCGATACGCC, and, Ang075-R, ACG CATCACCAGCCCTTGC), and Aro146 (AF237904; Aro1 46-F, CAGTTATCCATCTACAGGTG, and, Aro146-R, GAAATAAGAGAATGAGACTCTG). The same genotyping procedure was applied to the other eel species. The fragment sizes were determined by reference to a size standard using the software GENESCAN version 2. 1 and GENOTYPER version 2. 0 (Applied Biosystems Inc. , Foster City, CA). Allelic diversity, genetic variation and deviation from Hardy-Weinberg Equilibrium (HWE) were calculated with GENEPOP on the web  [107]  and GENETIX version 4.
Mitochondrial DNA sequencing and species identification All North Atlantic eel samples were screened by PCR-RFLP analysis of a 362 bp segment of the Cytochrome b gene (cytb)  [111]. This test is based on a diagnostic HinfI restriction site, specific to American eels. In order to confirm this quick screening approach, the cytb amplicon was directly sequenced in all Iceland individuals with American haplotypes using an ABI 377 automated sequencer (GenBank accession numbers: KJ546041, KJ546042, KJ546043, KJ546044, KJ546045, KJ546046, KJ546047, KJ546048, KJ546049, KJ546050, KJ546051, KJ5x46052, KJ546053, KJ546054, KJ546055, KJ546056). Incorporating known sequence data from Genbank, a haplotype network based on maximum parsimony was constructed in TCS version 1. 20  [112]  relying on an alignment of 278 bp (GenBank accession numbers: AB021767, AB021776, AF006714, AF006715, AF006716, AF006717, AF165069, AF172394, AF368238, AF368239, AF368240, AF368241, AF368242, AF368243, AF368244, AF368245, AF368246, AF368247, AF368248, AF368249, AF368250, AF368251, AF368252, AF368253, AF368254, AF485271, AF485272, AF485273, AF485274, AF485275, AF485276, D28775, D84302, EF427617, EF427618, EU223996, EU 223997, EU315235, EU315236, EU315237, EU315238, EU315239, EU315240, EU315241, EU315242, EU315243, EU492326, EU492327, M85080).
Simulation of hybridization clines and gene flow quantification We applied a simulation approach to test our hypothesis, that the IBD signal detected in Wirth and Bernatchez  [39]  for the European eel could have been generated by the steady increase either inter-species F1 hybrids or admixture clines from Southern to Northern Europe. Instead of the real dataset, which comprised eels from 12 sampling locations along the coastlines of Europe, we used a simple mating scheme to generate 12 virtual hybrid populations differing by a constantly increased level of inter-species hybridization. In essence, for assessing the impact of F1 hybrids on IBD signals, we generated the data in five steps: A) We identified the purest sampling localities of each eel species by considering average admixture coefficients as derived from STRUCTURE, thereby reducing any confounding effect due to actual admixture.
B) We generated 12 synthetic hybrid populations. For the first hybrid population, we did not allow for admixture, and thus generated it by randomly mating 50 "parental" pairs drawn at random from the purest European eel sample (sampling with replacement). C) For the second and each subsequent population, we increased the proportion of American eel parents sampled at random by 1%, that is, we added one additional American eel parent per population for the 50 crossings. D) We followed the same approach for a total of four different gene flow scenarios: linear increase of interspecies hybridization from one population to the next by 1%, 2% 3% and 4%. For each of the four datasets, pairwise genetic D CE distances  [117]  were calculated among populations using PHYLIP version 3. 68  [118]. E) Given both significant IBD and that populations follow a cline of admixture from South to North in the real data, we assigned the hypothetical pairwise genetic distance data to the real geographic distances among the genotyped populations.
Finally, in order to estimate the gene flow necessary to establish the degree of correlation (and significance) observed in the original dataset  [39] , both correlation coefficients (r) and the slope of the trend line (b) were matched against the simulated gene flow levels. In a second more realistic simulation approach, we created different degrees of admixed genotypes by sampling alleles randomly from the two species gene pools and this in direct proportion given by the actual admixture values apparent from the STRUCTURE output. Following this approach, we obtained virtual admixed genotypes nearly satisfying an average admixture proportion of 50% (expected for F1 hybrids), at Q = 0. 55. We performed this simulation using an R script relying on packages adegenet  [119, 120] , ade4  [121] , ape  [122, 123] , pegas  [124]  and plyr  [125].
The simulation consisted of these principle steps: i) We first sorted the genotype dataset according to Q-values inferred using the aforementioned best run in STRUC-TURE and set cut-offs for "pure" American (Q < 0. 1) and "pure" European (Q > 0. 9) eels. From this we calculated the respective allele frequency spectrum for both species. ii) We used the remaining individuals to estimate the distribution of all admixture proportions, i. e. , we assessed the probability density of Q, as discretized in bins of equal sizes of 0. 05 ranging [0. 10; 0. 90]. In order to create admixed individuals, we randomly sampled the Q-proportions using multinomial sampling from the observed proportions of Q-values in each bin. More specifically, for any given admixed genotype with a certain Q-value the appropriate proportion of alleles inherited from either A.
rostrata, or A. anguilla were determined for all nine loci using binomial sampling, and then, using multinomial sampling, alleles were randomly drawn from the allele frequency spectrum of each locus in each "pure species" pool. iii) Finally, we assigned linearly increasing fractions of admixed genotypes of 1%, 2%, 3% or 4%, analogous to the simplified simulation using F1 hybrids, and this again for 100 virtual individuals per population with European eels being the focal species receiving introgressed American alleles. Lastly, we used the Isolation-with-Migration model implemented in the software IMa2  [85]  to test the hypothesis that the pattern observed in Wirth and Bernatchez  [39]  results from varying gene flow rates between European and American populations as opposed to ancestral polymorphism due to incomplete lineage sorting.
rostrata and A. anguilla. Data sets included all genetic markers, namely, the nine microsatellite loci, and the cytb fragment reduced to the part containing the recognition site, because this 4-base pair stretch was available for the total dataset. Mutation models used for analyses were a stepwise mutation model for microsatellite loci and the HKY model for the cytb fragment. We used an exponential prior on migration to explore both low and high migration rates, even with the modest number of microsatellites available. Moreover, this model makes tests of migration more conservative than a uniform prior  [85, 126]. Preliminary runs were performed to set the upper bounds (or the mean for migration rate) of the prior distribution of each parameter. The upper limit for the prior for the population size and divergence time parameters were set to 500 and 100, respectively (except for the population pair northern A.