dataesr/scientific-paragraphs-categorization huggingface dataset:
A precise measurement of B + c production as a function of transverse momentum and rapidity will provide useful information on the largely unknown production mechanism of the B + c meson and other bound states of heavy quarks, and is also important to guide B + c studies at the LHC. In this Letter we report on the first measurement of the ratio of double differential inclusive production cross-sections multiplied by branching fractions, R(p T , y) ≡ dσ B + c (p T , y) B(B + c → J/ψ π + ) dσ B + (p T , y) B(B + → J/ψ K + ) , (1) where transverse momentum p T and rapidity y refer to the b meson. The cross-section includes contributions from excited states. We use a sample of pp collision data at 8 TeV, corresponding to an integrated luminosity of 2. 0 fb -1 recorded by the LHCb experiment. The B + c and B + mesons are reconstructed in the exclusive decays B + c → J/ψ π + and B + → J/ψ K + respectively, with J/ψ → µ + µ -.
Finally, the tracks of two or more of the final state particles are required to form a vertex that is significantly displaced from the PVs. A multivariate algorithm  [35]  is also used to identify secondary vertices consistent with the decay of a b meson. The b-meson candidate selection is performed in two steps, a preselection and a final selection on the output of a multivariate classifier based on a boosted decision tree algorithm (BDT)  [36, 37]. Simulated B + c and B + decays are used to optimize the b-meson candidate selection. Production of B + mesons is simulated using Pythia 6. 4  [38]  with an LHCb specific configuration  [39]. The generator Bcvegpy  [40]  is used to simulate B + c meson production. Decays of B + c , B + and J/ψ mesons are described by EvtGen [41] and photon radiation is simulated using the Photos package  [42].
The decay products are traced through the detector by the Geant4 package  [43, 44]. Following Ref. [45] , the B + c meson lifetime is set to τ B + c = 0. 509 ps. The selection requirements are the same for B + c → J/ψ π + and B + → J/ψ K + candidates. In the preselection, J/ψ candidates are formed from pairs of oppositely charged particles with p T larger than 0. 55 GeV/c, with a good quality of the track fit and identified as muons. The two muons are required to originate from a common vertex. The J/ψ candidates with invariant mass between 3. 04 GeV/c 2 and 3. 14 GeV/c 2 are combined with a charged particle that has p T > 1. 0 GeV/c, a good quality of the track fit and is separated from any PV. The pion mass hypothesis is assigned to the track for the selection of the B + c candidate and the kaon hypothesis for that of the B + candidate.
We acknowledge support from CERN and from the national agencies: CAPES, CNPq, FAPERJ and FINEP (Brazil); NSFC (China); CNRS/IN2P3 (France); BMBF, DFG, HGF and MPG (Germany); SFI (Ireland); INFN (Italy); FOM and NWO (The Netherlands); MNiSW and NCN (Poland); MEN/IFA (Romania); MinES and FANO (Russia); MinECo (Spain); SNSF and SER (Switzerland); NASU (Ukraine); STFC (United Kingdom); NSF (USA). The Tier1 computing centres are supported by IN2P3 (France), KIT and BMBF (Germany), INFN (Italy), NWO and SURF (The Netherlands), PIC (Spain), GridPP (United Kingdom). We are indebted to the communities behind the multiple open source software packages on which we depend. We are also thankful for the computing resources and the access to software R&D tools provided by Yandex LLC (Russia). Individual groups or members have received support from EPLANET, Marie Sk lodowska-Curie Actions and ERC (European Union), Conseil général de Haute-Savoie, Labex ENIGMASS and OCEVU, Région Auvergne (France), RFBR (Russia), XuntaGal and GENCAT (Spain), Royal Society and Royal Commission for the Exhibition of 1851 (United Kingdom).
, 2010). VκJκ5 junctions were built by artificially joining the Vκ 1-135 and Jκ5 segments. To create a reading frameshift, 5 and 10 additional nt were introduced at the VκJκ junction in V PTC and C PTC constructs, respectively (Fig. 1 B ). Cells (10 6 cells/ml) were treated or not with MG132 (1 µg/ml) for 5 or 8 h, as indicated, and control cells were treated with DMSO alone (dilution factor 1/1,000). Flow cytometry and cell sorting. The frequency of apoptotic cells was determined after staining with Annexin V (BD) and propidium iodide using a LSR II Fortessa (BD). Data were analyzed with FAC SDiva software (BD). Bone marrow B cell precursors and PCs were sorted on a FAC SVantage (BD) after staining with anti-mouse B220 (RA3-6B2; BioLegend), anti-mouse Igκ (187-1; Beckman Coulter), anti-mouse Igλ (JC5-1; Beckman Coulter), and anti-mouse CD138 (281-2; BD) mAbs.
1-TOPO vector (Invitrogen), using V-QUE ST software (IMGT, the international ImMu-noGeneTics information system). Real-time PCR were performed on a ABI PRI SM 7000 Sequence Detection System (Life Technologies). Transcripts were quantified according to the standard 2 -ΔΔCt method after normalization to Gapdh. Sequences of primers and probes are available upon request. Statistical analysis. Results are expressed as mean ± SEM and overall differences between variables were evaluated by a two-tailed unpaired Student's t test using Prism GraphPad software. A χ 2 test was done to analyze the distribution of F, V PTC , and C PTC VκJκ junctions.
ACkNOWLEDGMENTS We thank the staff of our animal facility, as well as  C. Carrion  and  C. Ouk-Martin  for technical assistance with microscopy and cell cytometry. We also thank  K. Rajewsky  ( The Max Delbrück Center for Molecular Medicine, Berlin, Germany ) and  S. Casola  ( Institute of Molecular Oncology Foundation, Milano, Italy ) for providing DH-LMP2A mice. AID-Cre-EYFP mice were kindly provided by  CA Reynaud  and  JC Weill  ( Imagine Institute, Paris, France ). We are grateful to  J. Cook-Moreau  ( Centre National de la Recherche Scientifique UMR 7276, Limoges, France ) for proofreading of the manuscript. This work was supported by grants from  Fondation ARC  (# PGA120150202338  and  SFI20121205821 ),  Ligue Contre le Cancer (comité Haute-Vienne) ,  Comité d'Organization de la Recherche sur le Cancer du Limousin (CORC) , and  Fondation pour la Recherche Médicale.
The fibre-optic diffuser was equipped with a long-pass filter (Optical Coatings Japan, λ > 640 nm) to block the reflected light from the leaf. The LS-1-CAL calibrated tungsten halogen NIST-traceable light source (Ocean Optics, USA) was used to calibrate the spectrometer. The leaf area was illuminated for 20 min (PPF: 150 μmol m -2 s -1 ), and then the steady-state Chl fluorescence was measured. Black wool paper was set at the back of the leaf to remove the reflection of light transmitted through the leaf. Chl fluorescence spectra were recorded to computer via a USB port using SpectraSuite operating software.
The fibre-optic diffuser was set at 5 cm distance from the leaf surface, and it was held at 45° to light to reduce specular reflection from the leaf surface. An 18% Gray Card (Kodak, USA) set at the same angle and position as the leaf was used as the non-fluorescent reference standard. Both the spectra of the leaf and the non-fluorescent reference were recorded to computer via a USB port using OPwave+ operating software (Ocean Optics, USA). Black wool paper was set at the back of the leaf to remove the reflection of light transmitted through the leaf. During the experiments, a LI-COR LI-250 light meter was used to measure the PPF (800-1800 μmol m -2 s -1 ).
6 s and measuring beam of 5 Hz) to give Φ PSII, PAM. The spectral radiant intensity data were recorded by the above-mentioned computer system. The set of sequential experiments included (i) measurement of solar light radiant intensity reflected from a non-fluorescent reference (90% White Card), (ii) measurement of solar light radiant intensity from the attached leaf, (iii) measurement of saturation laser pulse radiant intensity from the leaf, (iv) measurement of Φ PSII, PAM of the leaf using JUNIOR PAM, (v) measurement of solar light radiant intensity from the leaf, (vi) measurement of the second laser pulse radiant intensity from the leaf, and (vii) measurement of solar light radiant intensity reflected from the non-fluorescent reference at each interval of 20 s in (ii) to (vi) and about 60 s in (i) and (vii) under solar light.
Standard WHO larval bioassays on late 3rd /early 4th instars larvae were conducted to detect the level of susceptibility to temephos  [17]. At least three replicates of 20 larvae were used for each concentration. Mortality was recorded after 24 hours. To determine the LC 50 s and confidence intervals, data were analyzed using the Polo plus 2002-2014 LeOra software and the R script BioRssay v. 6. 1  [18].
Analysis of transcriptome profiling data Read pre-processing. Read quality and pre-processing (adaptor removal, quality and size trimming, low complexity filtering and rRNA removal) were performed using Scythe (https:// github. com/vsbuffalo/scythe) and SeqPrep (https://github. com/jstjohn/SeqPrep) for adaptor removal. Filtering and reads trimming (Q:25, read size>49) was performed with sickle (Joshi NA, Fass JN. 2011. https://github. com/najoshi/sickle) and low complexity sequences trimming was performed using PrinSeq  [22]. To estimate ribosomal and mitochondrial RNA, the NCBI rRNA dataset from genus Aedes, the SILVA rRNA database version 111 (http://www. arb-silva. de) as well as the NCBI Ae. albopictus mitochondrial genome were used. Due to rRNA contamination heterogeneity between libraries (1.
27%<rRNA<10. 45%) the contaminant rRNAs were removed. Assembly. The de novo assembly was performed on the pre-processed (singleton and repaired) reads of combined libraries using Trinity package  [23]  Release: 2013-11-10 with default parameters and the minimum contig size fixed to 200bp). The quality of the assembled data was assessed following four procedures: (a) align back the reads to the assembly with Bowtie2  [24] , (b) examine the similarity between the Trinity assembly and nr NCBI protein database (c) estimate the completeness of the product predicting the longest open reading frames (ORF) of the contigs using a custom perl script based on the EMBOSS program followed by a blastp (E-value< 1e-20) using the peptide datasets of the ENSEMBL v24 mosquitos and 21 NCBI full CDS of Ae. aegypti and (d) determine how many of 248 highly conserved eukaryotic genes were present in the Ae.
albopictus transcriptome with the program CEGMA  [25]. No filtering of low-abundance contigs was performed in order to maximize the detection of low expressed transcripts. Similarity searches and detection of sequences related to the insecticide target and detoxification. For sequence annotation of the assembled transcripts, a blastx similarity search against the NCBI protein database nr (e-value threshold 10 -6 ; keeping the top 20 hits) was performed using the parallel version of NOBLast  [26]. Based on the blastx results (first best Hit, Evalue <1e-10), genes of interest (ie detoxification genes, and genes encoding putative insecticide targets) were searched by a perl/SQL scripts using regular expression. Putative orthlogues were "named" based on their best blast hit with Ae. aegypti. The results were manually checked.
Differential gene expression analysis. Differential expression (DE) analysis was performed at the "gene" (component) level by pair wise comparisons between the parental and the selected strains (using the 2 biological replicates). The DE analysis was performed for each technical replicate (lane) separately. Only the differential expressed genes commonly up-or down-regulated between the two 2 technical replicates were retained. For the quantification, the paired reads of each sample were aligned to the transcriptome assembly with Bowtie2 and abundance was estimated with RSEM v-1. 2. 4, as implemented in the trinity script run_RSE-M_align_n_estimate. pl. The estimated expected counts for each sample (the sum of counts in each row >10) were extracted and used for the analysis of differential expression conducted in EdgeR-3.
The cleaned short read sequences were deposited in the Sequence Read Archive (SRA) (http://www. ncbi. nlm. nih. gov/bioproject/ 282718; SubmissionID: SUB923821; BioProject ID: PRJNA282718). De novo assembly performed using Trinity produced 254,336 contigs (mean length: 719. 98bp, N50: 1,160bp) corresponding to 146,372 unigenes (a set of contigs which are believed to belong together). Table  1. Temephos toxicity in the temephos-selected resistant strain (Tem-GR) as compared to its parental (Par-GR), a reference laboratory strain (Lab) and a susceptible field population from Italy (Field-S-IT)  [7]. To evaluate the accuracy of the assembled sequences (transcripts), all the usable sequencing reads were aligned onto the transcripts using Bowtie2. 86. 28% of reads were successfully back aligned on this assembly and at least 79.
Mosquitoes Similarity between the Trinity assembly and nr NCBI protein database was examined using Blastx and disclosed 37,414 contigs with a coverage of > 80%. In addition similarity with a dataset of the ENSEMBL v24 mosquitoes and 21 NCBI full CDS of Ae. aegypti using blastp disclosed 11,292 contigs that meet the criteria set (min. 60% identity and min. coverage of the subject 80%). The program CEGMA identified a total of 237 (95. 6%) of the 248 CEGs, and 138 of these were considered complete (75. 8% of the protein identified). The blastx search (E-value cut-off <10E-6) returned 63,978 (25. 2%) contigs with at least one blast hit corresponding to 36,455 (24. 9%).
A large Ae. albopictus mosquito larvae transcriptome dataset, consisting of 254,336 contigs and 146,372 unigenes was produced. The full set of the raw reads have been deposited in the Sequence Read Archive (SRA) (http://www. ncbi. nlm. nih. gov/ bioproject/282718; Submission ID: SUB923821; BioProject ID: PRJNA282718). The transcriptome was found to contain a number of detoxification enzymes and a number of transcripts that encode putative insecticide target subunits (Tables  2  and S2 ). These genomic resources will be useful to the community investigating this major vector. Differential expression analysis revealed a number of genes with significantly differently expressed transcripts between the temephos selected (Tem-GR) strain and the parental (Par-GR), a comparison that was chosen in order to minimize stochastic variation and isolate the resistance trait in the comparison.
Acknowledgments We thank  Tony Nolan  ( Imperial College, UK ),  Daniel Lawson  ( European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, UK ) and  Manolis Lirakis  ( University of Crete-UoC, Greece ) for their assistance with the Illumina transcriptomic analysis, as well as  HCMR-Crete , for providing access high-performance computing bioinformatics platform to analyze the transcriptomic data. Sincere thanks to  Charles Wondjii  (Liverpool School.
Sampling and analysis During continuous fermentations, effluent samples (10 mL) were collected daily from each reactor. Because the different models were also used for experimental trials with Clostridium difficile and antibiotic and probiotic treatments, only samples obtained during periods of control conditions are reported for model assessment. Analyses of microbial composition by quantitative polymerase chain reaction (qPCR) and pyrosequencing (model 2 and 3) were performed on samples from three days at the end of stabilization: days 9, 11 and 13 of model 1, days 16, 17 and 18 of model 2 and days 10, 11 and 12 of model 3. Metabolite concentrations in effluents of all reactors were tested daily during the entire fermentation by high-performance liquid chromatography (HPLC). Data corresponding to control condition periods (no treatment applied) are reported.
Microbiota profiling by 454 pyrosequencing 454-pyrosequencing analysis of total genomic DNA of fecal and effluent samples was carried out at DNAVision (Gosselies, Belgium). The V5-V6 hypervariable 16S RNA region was amplified using specific primers 784F (5'-AGGATTAGATACCCTKGTA-3') and 1061R (5'-CRR CACGAGCTGACGAC-3')  [32]. The forward primer contained the sequence of the Titanium A adaptor and a unique barcode sequence. Pyrosequencing was carried out using primer A on a 454 Life Sciences Genome Sequencer FLX instrument (Roche Applied Science, Vilvoorde, Belgium) following Titanium chemistry. The data obtained was analyzed using the open source software package Quantitative Insights Into Microbial Ecology (QIIME), v1. 7  [33]. Raw sequencing reads were filtered based on selected quality criteria such as: (1) no mismatch with the primer sequences and barcode tags; (2) no ambiguous bases (Ns); (3) read-lengths not shorter than 200 base pairs (bp) or longer than 1000 bp; (4) the average quality score in a sliding window of 50 bp not to fall below 25; (5) excluding homopolymer runs higher than 6 nt.
Sequences that passed quality filtering were clustered into OTUs at 97% identity level using cd-hit  [34]. Representative sequences (the most abundant) for each OTU were aligned using PyNAST and taxonomically assigned using Greengenes v_13_08 database. Chimera-Slayer was used to discard chimeric sequences, based on a reference data set of sequences  [35]. This led to 8245 +/-1924 (mean +/-SD) reads per sample. These phylogenies were combined with absence/presence or abundance information for each OTU to calculate unweighted or weighted UniFrac distances, respectively, using rarefaction of 7000 sequences per samples. Unifrac measures the phylogenetic distance between sets of taxa in a phylogenetic tree as the fraction of the branch length of the tree that leads to descendants from either one environment or the other, but not both  [36, 37].
Correlations between microbiota composition and metabolite production Pyrosequencing data on the genus level and metabolite concentrations measured by HPLC were investigated to test significant correlations between phylogenetic groups and metabolic activity. For model 2, significant negative correlations were calculated between isobutyrate, isovalerate and valerate concentrations and the dominant genera Ruminococcus and Bacteroides (Fig  6 ). Butyrate was positively correlated with the dominant genus Roseburia and unclassified members of Ruminococcaceae and Lachnospiraceae. In contrast, a dominant unclassified member of Enterococcaceae was negatively correlated with butyrate and positively correlated with all other metabolites detected. Furthermore many genera detected at less than 1% (Dialister, Anaerococcus and unclassified genera of Rikenellaceae and Mogibacteriaceae) showed positive correlations with isobutyrate, isovalerate and valerate with the exception of Oscillospira, Peptoniphilus and an unclassified genus of Peptostreptococcaceae (with abundances above 1% but in distal colon reactors only).
The microbial composition of models 2 and 3 and corresponding fecal inocula may be considered more representative of the elderly population than the fecal inoculum of model 1, which did not harbor Roseburia spp. , although the genus Roseburia was assigned at approximately 3% in fecal samples from elderly Irish subjects  [8]. 454 pyrosequencing was performed using the V5-V6 hypervariable region that was previously used to profile gut microbiota  [44] [45] [46]. Sun et al. [47]  recently reported that intragenomic heterogeneity for the V6 region may introduce overestimation of prokaryotes diversity. However in our study pyrosequencing data were used to compare of composition of donor and reactor samples within a model which should not be affected by this possible bias. In general, similar microbial profiles between effluent samples of model 2 and 3 and corresponding fecal donors were obtained.
Though we had intended also to keep the radiologist blind, this proved impractical. Nevertheless the radiologist played no other part in the study  [8]. Participants completed measures at baseline before treatment and at follow-up clinics one and three months after randomisation. We chose the interval of one month to maximise clinical benefit and identify any adverse events following injection; and that of three months to estimate medium-term benefit. We collected the following outcome data for the trial: neuroma size, foot pain and disability by the Foot Health Thermometer (FHT) and the Manchester Foot Pain and Disability Schedule, pain by the Multidimensional Affect and Pain Survey and health-related quality of life by the European Quality of life-5 Dimensions-3 Levels (EQ-5D-3L). The primary outcome was participants' assessment of their foot health by the FHT at three months after injection.
Measurement of costs We examined costs from the perspective of the National Health Service  [11, 12]. We estimated direct primary and secondary care use from hospital records and participants' self-reported client service receipt inventories  [13]  at one and three months. Research resource constraints prevented us from asking participants to complete client service receipt inventories at baseline. At one and three months the client service receipt inventories asked patients to recall all contacts with primary care, attendances at emergency departments, and inpatient stays. From hospital records we gathered data on steroid and anaesthetic injections, outpatient visits, surgery, radiological imaging, and laboratory tests and investigations. We derived unit costs of these service contacts in pounds Sterling from national sources  [14, 15]  (Table  1 ).
Acknowledgements We acknowledge the continued support by  Public Health Wales for the Centre for Health Economics and Medicines Evaluation, Bangor University. We thank the participants for taking part in the trial. We would like to thank  Dr. Takashi Kikuchi  and  Dr. Natalia Hounsome  for early stage analysis of data. Thank you to  Eugenia Priedane  for electronic scanning of some of the data. We would also like to thank  Alison Shaw  for providing reader support to the first author, RTE.
Acknowledgments The authors would like to thank to  Professor Piotr Dobrzynski  ( Center of Polymer and Carbon Materials, Polish Academy of Sciences ) for providing us with degradable copolymer,  Marleen Kamperman  and  Malgorzata Wlodarczyk-Biegun  ( Laboratory of Physical Chemistry and Colloid Science, Wageningen University, The Netherlands ) for the access to the rheometer,  Dr Lukasz Zych  ( Department of Ceramics and Refractories, Faculty of Materials Science and Ceramics ) for the access to the Zetasizer and  Krzysztof Pietryga  for his help in laboratory work. Polish National Science Centre  (Grant No:  012/05/B/ST8/00129 ) provided financial support to this project.
The distribution of test scores is then used as the null distribution and any loci with a test score falling in the outlying 5% of the distribution are considered potentially selected. To compare the performance of the different methods, we also carried out simulations under different selection scenarios and then pooled neutral and selected replicates to estimate power at various false-positive rates. These results are then presented as ROC curves obtained using the R package 'ROCR'  (Sing et al. 2005). The most widespread approach to assess significance when analysing real data is based on the empirical distribution (approach a). The reason for this is that in most cases, we do not know with certainty the true demographic history of the species under study. Thus, we present the results of this procedure in the main text and the results of the second procedure in the supplementary information.