dataesr/scientific-paragraphs-categorization huggingface dataset:
The n_TOF facility for science and technology As previously pointed out, nuclear reactions induced by neutrons play a fundamental role in nuclear technology, being at the basis of reactor physics, and are of great importance in other fields such as Nuclear Astrophysics and fundamental Nuclear Physics. Many of the existing experimental data have been evaluated and made available through nuclear data libraries and databases. Nevertheless, new developments in emerging nuclear technologies, as well as the newest astrophysical and nuclear models, require more precise and accurate data together with new measurements for a large number of isotopes. For nuclear technologies, the prediction of the behaviour of reactor cores depends strongly on capture and fission cross-section data. In particular, the R&D of new reactor concepts, as Generation-IV  [20]  and Accelerator Driven System (ADS)  [21] , require precise and accurate measurements of neutron capture and fission cross sections of actinides in the thermal (meV), epithermal (eV-keV) and fast (MeV) energy regions.
At n_TOF different measurements of reaction cross-sections addressed as urgent nuclear data requirements by the Nuclear Energy Agency  [22]  have been performed. As an example, the measurement of the 238 U radiative capture cross-section  [23]  and of the 240,242 Pu(n, f ) cross-section  [24, 25] , with the purpose of obtaining a very high accuracy to solve present inconsis- tencies and improve the performances of new reactors. All these measurements have been performed in EAR1, but the 240 Pu(n, f ) one has suffered from several problems due to the damage of the Mi-croMegas detector caused by the very high α-activity of the sample. For this reason the measurement has been repeated in 2014 exploiting the n_TOF EAR2, which has allowed to reduce the measurement time, thus preventing the damage of the detector.
The total energy detection technique has been used, in combination with the pulse-height weighting method  [34]  to obtain the required proportionality between the the efficiency of the detection system and the total radiative energy emitted by the capture event. The measurement has been carried out in EAR1, where single resonances could be resolved up to about 500 keV. The experimental yield has been analyzed with the resonance shape analysis (RSA) code SAMMY  [35]  to obtain the parametrization of the capture cross-section in terms of resonance parameters. A simultaneous resonance shape analysis of both capture and transmission data is performed, which leads to much more precise resonance parameters as if the two data sets would have been analyzed separately. In Fig. 4  comparisons between capture and transmission data in different energy ranges are shown.
Comparison of electrophoretic profiles of The Pl SA and Pl K 1D electrophoretic profiles were very similar with presence/absence or intensity variation detected for only a few bands (Fig. 3 ). The distribution of 2D spots evidenced more differences, in particular the absence of the Pl SA spot number 10 (55 kDa) in the Pl K strain (Fig. 4a, b ). We observed a greater variation at the interspecific level with several profile differences in the 25-35 kDa range (Figs  3  and 4 ). Comparison of transcriptomic and proteomics data between Psyttalia wasps. All the major bands and spots on 1D and 2D electrophoretic profiles of Pl and Pc venom, and a number of minor spots (70 bands for Pl, 46 bands for Pc, from at least two 1D gels per species; 117 spots for Pl, 57 for Pc, from at least three 2D gels per species) were excised, and tryptic peptides were analyzed by LC-MS-MS.
In parallel, a transcriptomic analysis of Pl and Pc venom glands was performed, based on Illumina sequencing. De novo assembly of the Pl transcriptome was improved using additional 454 (full body) and Sanger (venom apparatus) sequence data (Supplementary Figure  S1 ) and it yielded a total of 16,943 and 16,360 unisequences for Pl and Pc, respectively (Supplementary Table  S1 ). Data suggested a similar quality of the transcriptomes, based on general features and similarity searches (Supplementary Table  S1 ), as well as GO terms comparison (Supplementary Figure  S2 ). The combined transcriptomic and proteomic data resulted in 39 and 40 unisequences for Pl and Pc, respectively, among which 32 and 36 had a putative function (Supplementary Tables  S2  and S3 ). Some of these, such as actin-5C or glyceraldehyde-3-phosphate dehydrogenase 2, were typical cellular proteins with no predicted signal peptide (Supplementary Tables  S2  and S3 ).
Sequencing and assembly. Illumina RNA-Seq sequencing (HiSeq 2000, 2 × 75 pb), 454 sequencing (454 GS-FLX Titanium platform) and trimming were performed by Beckman Coulter Genomics. Quality of Illumina raw reads was controlled using FastQC software and reads were cleaned by removing low quality sequences and reads containing N or adaptor sequences. For Sanger sequencing, a total of 2,000 clones were analyzed by the Genoscope (CEA, Evry, France) on an ABI sequencer using the standard M13 forward primer and BigDye terminator cycle sequencing kit (Applied Biosystems, Foster City, CA, USA). Sanger ESTs were then trimmed using TIGR SeqClean software. For each species, we performed de novo transcriptome assembly using Velvet/Oases assembler (https://www. ebi. ac. uk/~zerbino/oases/) after the filtering process of Illumina raw reads.
The first assembly step used a multiple kmer approach with kmer size ranging from 45 to 65 (k = 45, 55, 65 and coverage = 2). A meta-assembly (kmeta = 51, coverage = 1) was then performed using all previously obtained transcripts (> 100 bases long) (Supplementary Fig. S1 ). At both assembly steps, we used CD-HIT-EST to remove the shorter redundant transcripts entirely covered by other transcripts with more than 99% identity. Finally, a clustering of transcripts was performed using TIGR-TGICL. To improve the quality of the assembly for Pl, we included the cleaned 454 and Sanger sequences as long sequences (minimum size of 200 bases) or otherwise as short sequences, in addition to the short Illumina reads. Sequence annotation and analysis. To identify similarities with known proteins, the unisequences were compared to NCBI non-redundant protein sequence database, UniProtKB/Swiss-Prot database, insect predicted proteome databases (Drosophila melanogaster v5.
sanger. ac. uk/pub/databases/Pfam/Tools/) and CD-Search against Conserved Domain Database (CDD) at NCBI (http://www. ncbi. nlm. nih. gov/Structure/ cdd/cdd. shtml). Identification of leucine-rich repeats (LRR) in protein sequences was done using LRRfinder (http://www. lrrfinder. com/). Search for DUF4803 domain-containing, GH1 β -glucosidase and protein disulfide isomerase sequences was performed using blastp at NCBI (http://www. ncbi. nlm. nih. gov/blast/) and HMMsearch from the HMMER package (http://hmmer. org/). Pairwise and multiple amino acid sequence alignments were respectively obtained using the Needleman-Wunsch algorithm and MAFFT implemented in Geneious software (Biomatters). Phylogenetic analyses were performed using maximum likelihood (ML) with PhyML (http://phylogeny. lirmm. fr/).
ProtTest was used to select the best fit model of amino acid substitution for ML phylogeny (https://github. com/ddarriba/ prottest3). Gene functions and GO terms were automatically assigned to the predicted proteins based on the identification of domains with PfamScan. Only the root domain of the hierarchical domain organization available from EBI was conserved. Comparison of GO terms between Pc and Pl unisequences and homogenization of the annotation level were performed using GO slim terms.
Identification of proteins by mass spectrometry was performed on 1D bands and 2D spots excised from the gels as previously described  7, 13. MS/MS data analysis was performed with the Mascot software (http://www. matrixscience. com) licensed in house using the combined Pc and Pl unisequences and non-redundant NR (NCBI). Data validation criteria were (i) one peptide with individual ion score above 50 (the Mascot significant identity threshold corresponding to p < 0. 05 is 45 in our case) or (ii) at least two peptides of individual ion score above 20 (corresponding to 1% probability that a peptide spectrum match is a random event). The Mascot score was calculated as -10*LOG10(P). The calculated FDR (based on an automatic decoy database search) ranged from 0 to 1. 4% depending of the individual gel analysis.
Acknowledgements We are grateful to  M. Thaon  for help in insect rearing,  N. Ris  and  T. Malausa  for fruitful discussions,  M. C. Bon  for providing P. lounsburyi samples, and  K. Varikou  and  V. Caleca  for providing P. concolor samples. We also thank  A. Schmitz ,  J. Villalba , and the  Microscopy Platform of the Sophia Agrobiotech Institute (ISA)  for technical advices. We are grateful to the anonymous reviewers for their careful reading of our manuscript. This work received support from the  Genoscope  ( Parasitoid venoms  project), the  Association Française Interprofessionnelle de l'Olive (AFIDOL) , the  Department of Plant Health and Environment (SPE)  from the  French National Institute for Agricultural Research (INRA) , the  French National Research Agency  through the  PARATOXOSE  project ( ANR-09-BLAN-0243-01 ), and the  "Investments for the Future" LABEX SIGNALIFE: program  reference  ANR-11-LABX-0028.
Material and methods The study was conducted on the River Frome, England (508419 N; 28119 W). We measured individual summer HR and mean daily travel distance using radio telemetry of 43 pike ranging in body mass (M) from 7 to 12 060 g between June and September 2001-2005. Fish were located at dawn, midday and dusk every day over a 13 day period, resulting in standard summer HR datasets of 39 locations per fish. Armstrong et al. [14]  published a scaling relationship of metabolic rate of pike with body mass and we used the log-transformed data from all individuals in that study to generate confidence intervals around the scaling exponent and test for a significant difference between the metabolism and HR scaling exponents. Metabolic data were collected at 158C, while average summer water temperatures of the River Frome varied between 15 and 17.
Transcriptomes of Homologous Genes The two fungi P. al and P. ch are not closely related in the fungal kingdom, both belonging to the phylum Ascomycota, but P. al is of the class Sordariomycetes while P. ch is of class Eurotiomycetes. Based on the sequenced transcripts we used two sided BLASTp searches to find 837 homologous (orthologous and paralogous) gene pairs between P. al and P. ch (with e-values of less than 10E-20). In this group of genes 73 and 76 genes of P. al and P. ch are differentially regulated. These numbers are higher than expected since 7. 6% (P. al) and 3. 2% (P. ch) of the total transcriptome is differentially regulated with expected gene numbers in the homologous gene set of 54 and 23 for P. al and P. ch. In other words, especially for P. ch the majority of differentially regulated genes have a homologous gene in P.
We tested the assay using B5VIT medium to obtain results comparable to the data of the interaction study and the transcriptome analysis. But the B5VIT medium itself caused an inhibition of luminescence except for the positive control, which was induced by H 2 O 2. After several modifications the best result was obtained in water with the rest of medium included from the washing step. To correlate the amounts of ROS produced by the grapevine cells and the equivalent of hydrogen peroxide the positive control was diluted to a final concentration of 1 μM H 2 O 2. By adjusting the control the counts per minute detected by the luminometer were similar to the luminescence of the samples containing fungal supernatant and plant tissue. In correlation to the samples of mRNA isolated for the transcriptome analysis the elucidation was adjusted, additionally.
Data Processing After quality control, all remaining sequences were used for genome based de-novo transcriptome assembly by Trinity  [46]. The obtained transcripts were filtered for low coverage and mRNA, ORF and peptide sequences were calculated using the Trinity plugin Transdecoder. For P. ch and P. al 2661 and 2008 complete ORFs and Protein sequences were found as being sufficiently transcribed for detection in this experiment in at least one condition. The ORFs were mapped back on the respective genomes using BLAT  [70]  and gff files were constructed containing exon positions of the respective ORFs. Novoalign (Novocraft) was used for mapping the sequencing raw files onto the respective genomes and the python script HTSeq (Anders et al. , 2014) was used to obtain coverage counts per exons.
Normalization and statistics of the coverage counts per transcript were done using R/Bioconductor packages limma  [71] , EdgeR and voom  [72]  to identify differentially regulated genes using mean-variance weighting (voom) and TMM normalization  (Gentleman et al. , 2004). A significance cut-off of p < 0. 01 (adjusted for multiple testing by the false discovery rate method) was applied for analysis. Data have been deposited at NCBI GEO under the accession number The protein sequences were annotated using InterProScan  [73]  to receive also GO (gene ontology) and KEGG (pathway) information.
7 -6. 5 domain. Our performance assessment of MUSE in the field of A1689 confirms the idea that this instrument is particularly well suited for the study of faint and distant populations of galaxies (see also  Richard et al. 2015; Bacon et al. 2015). The outline of this article is as follows. In Sect. 2 we describe the observations carried out on the field of A1689 and provide a summary of the data reduction and analysis process. Section 3 addresses the properties of the cluster galaxy populations as seen by MUSE. The identification of background sources lensed by A1689 is discussed in Sect. 4, in particular the spectroscopic confirmation of known multiply imaged sources as well as the discovery of new multiple systems. Implications of these results on the mass modeling of A1689 are also discussed in this section.
MUSE data were reduced using the MUSE Data Reduction System V1. 0  (Weilbacher et al. 2012 (Weilbacher et al. , 2014)) , including bias, dark, flat-fielding, and geometrical corrections, basic sky subtraction, wavelength and flux calibrations, and astrometry. The six individual exposures were reduced and combined into a final datacube. Sky subtraction and correction for sky residuals were performed with the ZAP software (Zurich Atmoshere Purge  Soto et al. 2016). Several versions of the MUSE datacube were performed, with different choices for the combination parameters and the correction for sky residuals. Flux calibration and telluric correction was achieved based on observations of the standard star HD49798 (O6 star). The final datacubes extend between 4750 and 9351Å, that is, 3681 spectral pixels with a scaling of 1.
Extraction of HST-detected sources Sources were extracted using two different approaches. First, a catalog of objects was built with SExtractor package version 2. 8  (Bertin & Arnouts 1996)  based on the detection on the F775W image. Two hundred forty-five objects were detected in this way within the MUSE field of view, with Kron-like magnitudes (SExtractor MAG_AUTO) ranging between 17. 0 and 28. 1. Before extracting background sources in the central region from the MUSE cube, background residuals and continuum emission from bright cluster galaxies (hereafter BCGs) were fitted and removed from the MUSE cube with median filtering in both wavelength and spatial directions. The BCG area hereafter refers to the central region, which is strongly contaminated by the bright galaxies and displayed in a pink color-scale in Fig.
g. , flux ∼5×10 -18 erg/s/cm 2 ). These tools used in various combinations allowed us to detect 14 new emission-line objects, most of them lacking a clear counterpart in the HST/ACS images. All line emitters extracted based on the guided-and-manual approach and discussed in the previous section were detected by one of the automated tools. All sources retained in Sect. 4 were found in all different versions of the MUSE data cubes (with and without background residuals and continuum emission subtracted). All of them yield multiple-image configurations in good agreement with lensing expectations. Based on our experience, the automated procedure was especially useful to find faint emission-line objects, with a typical flux of a few 10 -18 erg/s/cm 2 , in particular those lacking a clear detection on HST/ACS images.
Lensed sources behind A1689 Previous to our survey, 28 different lensed galaxies displaying 46 multiple images were known in the MUSE field of view, most of them based on photometric redshifts together with lensing considerations (see, e. g. ,  Limousin et al. 2007; Frye et al. 2007; Tu et al. 2008; Coe et al. 2010). Of these, we have been able to confirm 12 images corresponding to 7 different lensed galaxies be-tween z = 0. 95 and 5. 0. These systems are described in Sect. 4. 1. Fourteen new galaxies have also been spectroscopically identified in this area thanks to MUSE data, with redshifts ranging between 0. 8 and 6. 2, as presented in Sect. 4. 2. The majority of the lensed galaxies has been identified based on their Lyman-α emission. The lensing model used to analyze these data is the one presented in L07, based on Lenstool  (Jullo et al.
Conclusions To summarize, these are the main results we obtained: -Based on guided-and-manual and automated (blind) approaches, a census of sources was obtained in the field of MUSE, most of which are cluster galaxies. The spectroscopic census is complete out to m(775W) < 21. 5. The spectroscopic sample includes 63 cluster galaxies with secure redshifts (49 of them with m(775W) < 21. 5). The velocity distribution of cluster galaxies is more complex than the smooth Gaussian distribution found for this cluster on larger scales, with a main cluster component with σ = 1200 km/s, and the signature of the NE group component, with σ = 600 km/s. This demonstrates that MUSE data are able to catch the small-scale substructure in particularly dense environments, a difficult exercise for usual multi-slit spectrographs.
Acknowledgements. We thank  O. Czoske  for providing unpublished spectroscopic data on A1689. Part of this work was supported by the  French CNRS. This work has been carried out thanks to the support of the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French government program  managed by the  ANR. Partially funded by the  ERC  starting grant CALENDS (JR, VP, BC), and the  Agence Nationale de  la recherche bearing the references  ANR-09-BLAN-0234  and  ANR-13-BS05-0010-02  (FOGHAR). Based on observations made with  ESO Telescopes at the La Silla Paranal Observatory  under programme  ID 60. A-9100(B). We thank all the staff at  Paranal Observatory  for their valuable support during the commissioning of MUSE. Also based on observations made with the  NASA/ESA Hubble Space Telescope  ( PID 9289 ).
massiliensis SIT 12 and Murdochiella asaccharolitica are listed in Table  1. The antibiotic susceptibility was studied using antibiotics discs (i2a, Montpellier, France). Murdochiella massiliensis SIT12 was resistant to fosfomycin, tobramycin, naxidic acid and colistin but was susceptible to gentamicin, ciprofloxacin, trimethoprim-sulfamethoxazole, teicoplanin, rifampicin, ceftazidime, erythromycin, imipenem, tazocillin and aztreonam. Using 16S rRNA phylogeny analyses, we demonstrated that strain SIT12 exhibited a 97% 16S rRNA sequence identity with Murdochiella asaccharolytica (EU483153) and Levyella massiliensis (HM587324) species (Fig. 3 ). Investigation of the most closely related described species revealed that the novel species strain was member of the phylum Firmicutes. Its 16S rRNA sequence was deposited in GenBank under accession number LN866998.
Cellular fatty acid methyl ester analysis was performed by gas chromatography/mass spectrometry  [10]. The major fatty acids for strain SIT12 are mainly composed of 16 or 18 carbons: 16:0 (34%), 18:1n9 (28%), 18:2n6 (19%) and 18:0 (12%). Moreover, several fatty acids are described with unusual longer chains such as 20:4n6, 20:5n3 and 22:6n3 (<1%) (Table  2 ). Extended feature descriptions MALDI-TOF MS protein analysis was carried out as previously described  [11]. The SIT12 spectra were imported into MALDI BioTyper 3. 0 software (Bruker Daltonics, Leipzig, Germany) and analysed by standard pattern matching (with default parameter settings) against 7765 spectra of bacteria. The method of identification included m/z from 3000 to 15 000 Da. A maximum of 100 peaks were compared with spectra in the database for every spectrum.
The resulting score enabled the identification (or not) of tested species: a score of 2 with a validly published species enabled identification at the species level, a score of 1. 7 but <2 enabled identification at the genus level and a score of <1. 7 did not enable any identification. No significant MALDI-TOF MS score was obtained for strain SIT12 against the Bruker database, suggesting that our isolate was not a member of a known species. We added the spectrum from strain SIT12 to our database.
Genome analyses The resistome was analysed with the ARGANNOT (Antibiotic Resistance Gene-ANNOTation) database  [15]. The exhaustive bacteriocin database available in our laboratories (Bacteriocins from the URMITE database) was performed by collecting all currently available sequences from the databases and from the National Center for Biotechnology Information. Protein sequences from this database allowed putative bacteriocins from human gut microbiota to be identified using BLASTp methodology  [16]. Analysis of presence of polyketide synthases-nonribosomal peptide synthetases (PKS/NRPS) was performed by discriminating the gene with large size using a database realized in our laboratory; predicted proteins were compared against nonredundant GenBank database using BLASTP and then examined using antiSMASH  [17].
Conclusion On the basis of taxonogenomic analyses, we formally propose the creation of Murdochiella massiliensis sp. nov. that contains the strain SIT12. Description of Murdochiella massiliensis sp. nov. The Murdochiella massiliensis name come from Massilia, the ancient Roman name for Marseille, France, where the type strain was isolated. The strain was anaerobic, Gram positive, non-endospore forming, nonmotile and coccus shaped. Growth was achieved anaerobically between 25 and 42°C after 48 hours' incubation. Growth occurred in the presence of 5 g/L NaCl. Catalase and oxidase were negative. The genome is 1 642 295 bp long, and G+C content is 48. 9%. The 16S rRNA gene sequence and whole-genome shotgun sequence of M. massiliensis strain SIT12 are deposited in GenBank under accession numbers LN866998 and FIZW00000000.
NMR conformational analysis Experiments have been performed on a Bruker Avance III 700 MHz spectrometer equipped with a 5 mm diameter BBI Gradient probe. Water suppression was achieved with a watergate sequence. Experiments were processed with the Bruker software (Bruker BioSpin, Courtaboeuf, France) and analyzed with the Sparky program (T. D. Goddard and D. G. Kneller, University of California, San Francisco). Chimeras 1 and 2 were dissolved in 100% CD 3 OH to a final concentration of 4 mM. For each peptide, a complete series of 2D homonuclear and heteronuclear spectra was acquired at 293 K. A number of scans of 16, 8, and 32 were used, respectively, for COSY-DQF, TOCSY and ROESY experiments. The mixing times were set up to 60 ms for TOCSY and 300 ms for ROESY experiments. The 2D homonuclear spectra were acquired with 215 increments in F1 dimension and 2048 points in F2 dimension.