dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Ian Beveridge  ( University of Melbourne, Australia ),  Marja Isomursu  ( Finnish Food Safety Authority Evira, Finland ),  Antti Oksanen  ( Evira, Finland ),  Ivan Seryodkin  ( Pacific Institute of Geography FEB RAS, Russia ) and  Egor Vlasov  ( Kursk State University, Russia ) are acknowledged for providing specimens for the present study. This study was supported in part by a  Grant-in-Aid for Scientific Research  (no. 26460503 ) from the  Japan Society for the Promotion of Science. Computations were partially performed on the supercomputer system of the  Research Organization of Information and Systems, National Institute of Genetics, Japan. Sampling in France and Spain was supported by  Agency for Management of University and Research Grants, Government of Catalonia  (grant  2014 SGR 1241 ).
Rationale and objective: Central and South America comprise one of the areas characterized by high incidence rates for head and neck cancer. We describe the geographical and temporal trends in incidence and mortality of head and neck cancers in the Central and South American region in order to identify opportunities for intervention on the major identified risk factors: tobacco control, alcohol use and viral infections. Methods: We obtained regional-and national-level incidence data from 48 population-based cancer registries in 13 countries and cancer deaths from the WHO mortality database for 18 countries. Agestandardized incidence (ASR) and mortality (ASMR) rates per 100,000 person-years were estimated. Results: Brazil had the highest incidence rates for oral and pharyngeal cancer in the region for both sexes, followed by Cuba, Uruguay and Argentina.
Methods The data sources and methods are described in detail in an earlier article in this issue  [60]. In brief, we obtained regional-and national-level incidence data from 48 population-based cancer registries in 13 countries, and cancer deaths from the World Health Organization mortality database for 18 countries. We estimated age-standardized incidence rates (ASRs) and mortality rates (ASMRs) per 100,000 person-years using the direct method and the World standard population  [11, 12]. We estimated national ASRs by aggregating the data from the available cancer registries using a weighted average of local rates. To describe incidence and mortality time trends, we calculated the estimated annual percentage change (EAPC) using the method proposed by Esteve et al. [13]. Trends in incidence and mortality and EAPCs were estimated for four countries (Table  1 ).
Acknowledgements The authors would like to thank sincerely all of the cancer registry directors and their staff (listed in the Appendix to the Introduction of this Supplement) for their considerable efforts in collecting the data presented in this paper, together with members of the  IARC Section of Cancer Surveillance , especially  Sebastien Antoni ,  Murielle Colombet  and  Mathieu Laversanne  for their collaboration. The authors also wish to acknowledge  Dr Marion Piñeros  and  Dr Patricia Cueva  for their valuable comments in reviewing earlier drafts of the manuscript.
Acknowledgements This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No. 633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. We warmly acknowledge the help of  J. Dahdah  from  Kapteos  for EO-probe calibrations,  E. Di Palma  from  ENEA-Frascati  for advices on CST solver,  M. Zerbini  and  P. Di Lazzaro  from  ENEA-Frascati  for article review.
7  [9]. The clustering is performed with the FASTJET package  [10]  using fourmomentum summation. The reconstructed jets require small additional energy corrections to account for various reconstruction inefficiencies in tracks and clusters in the PF algorithm. These jet energy corrections  [11]  are derived using (1) simulated events, generated with PYTHIA 6. 4. 22  [12]  with tune Z2*  [13, 14]  and processed through the CMS detector simulation based on GEANT4  [15] , and (2) measurements containing dijet, photon+jet, and Z+jet events. The jet energy corrections, which depend on the η and p T of the jet, are applied to the jet four-momentum vectors as multiplicative factors  [16]. The overall factor is typically 1. 2 or smaller, approximately uniform in η, and is 1. 05 or smaller for jets having p T > 100 GeV.
Measurement of the dijet cross section differential in ∆φ dijet The normalized dijet cross section differential in ∆φ dijet (Eq. 1) is corrected for detector smearing effects and unfolded to the level of stable (decay length cτ > 1 cm) final-state particles. In this way, a direct comparison of the measurement with corresponding results from other experiments and with QCD predictions can be made. The unfolding method is based on the matrix inversion algorithm implemented in the software package ROOUNFOLD  [21]. Unfolding uses a response matrix that maps the distribution at particle-level onto the measured one. The response matrix is derived from a simulation that uses the true dijet cross section distribution from PYTHIA6 with tune Z2*  [13]  as input, and introduces the smearing effects by taking into account the ∆φ dijet resolution.
Predictions from fixed-order calculations in pQCD The theoretical predictions for the normalized dijet cross section differential in ∆φ dijet are based on a 3-jet calculation at NLO. The correction of nonperturbative (NP) effects, which account for multiparton interactions (MPI) and hadronization, is studied using event samples simulated with the PYTHIA6 (tune Z2*) and HERWIG++ (tune 2. 3) event generators. Small NP effects are expected, since this measurement deals with a normalized distribution. These corrections are found to be of the order of 1%, roughly at the limit of the accuracy of the MC simulations. Therefore NP corrections are considered to be negligible and are not applied. The fixed-order calculations are performed using the NLOJET++ program version 4. 1. 3 [25, 26]  within the framework of the FASTNLO package version 2.
Base set Refs. The uncertainties due to the renormalization and factorization scales are evaluated by varying the default choice of µ r = µ f = p max T between p max T /2 and 2p max T , simultaneously in the differential cross section and in the total cross section, in the following six combinations: (µ r /p max T , µ f /p max T ) = (1/2, 1/2), (1/2, 1), (1, 1/2), (1, 2), (2, 1), and (2, 2). The PDF uncertainties are evaluated according to the prescriptions for the CT10 PDF set in Ref. [35]. The CT10 PDF set employs the eigenvector method with upward and downward variations for each eigenvector. To evaluate the uncertainty due to the value of the strong coupling constant at 68% confidence level, α S (M Z ) is varied by ±0. 001 as recommended in Ref. [36]. N f α S (M Z ) ABM11 [ The results of fixed-order calculations with the CT10 PDF set are overlaid on the data for ∆φ dijet > π/2 in Fig.
2. Figure  3  shows the ratio of the normalized dijet cross section differential in ∆φ dijet to theory calculated using the CT10 PDF set, together with the combined PDF and α S uncertainty (inner band), and the scale uncertainty (outer band). Also shown are the ratios of theory derived with the alternative PDF sets ABM11 (dashed line), HERAPDF1. 5 (dashedthree-dotted line), MSTW2008 (dashed-dotted line), and NNPDF2. 1 (dotted line) compared to the prediction with the CT10 PDFs. The fixed-order calculations agree with the data for azimuthal angular separations larger than 5π/6 except for the highest p max T region, where they exceed the data. For smaller ∆φ dijet values between 2π/3 and 5π/6, in particular where the estimate of the theoretical uncertainties becomes small, systematic discrepancies are exhibited that diminish with increasing p max T.
CMS Ratio to Theory (CT10 PDF) Figure  3 : Ratios of the normalized dijet cross section differential in ∆φ dijet to LO (triangles) and NLO (squares) pQCD predictions using the CT10 PDF set at next-to-leading evolution order for all p max T regions. The error bars on the data points represent the total experimental uncertainty, which is the quadratic sum of the statistical and systematic uncertainties. The uncertainties of the theoretical predictions are shown as inner band (PDF & α S ) and outer band (scales). The predictions using various other PDF sets relative to CT10 are indicated with different line styles.
Predictions from fixed-order calculations matched to parton shower simulations The PYTHIA6  [12] , PYTHIA8  [37] , and HERWIG++  [24]  event generators complement LO dijet matrix elements with parton showers to simulate higher-order processes. Both PYTHIA versions, PYTHIA6 with the Z2* tune  [13]  and PYTHIA8 with the CUETM1 tune  [14] , employ p Tordered parton showers  [38, 39] , while HERWIG++ with the default tune of version 2. 3 uses a coherent-branching algorithm with angular ordering of the showers  [40]. The MADGRAPH program version 5. 1. 5. 7  [19]  supplies the results of LO matrix element calculations with two to four outgoing partons that can be matched to the implementations of parton showers, hadronization, and MPI of the event generators. In this analysis, it is interfaced with PYTHIA6 with tune Z2* using the MLM matching procedure  [41]  to avoid any double counting between tree-level and parton shower generated parton configurations.
Summary A measurement is presented of the normalized dijet cross section differential in the azimuthal angular separation ∆φ dijet of the two jets leading in p T for seven regions in the leading-jet transverse momentum p max T. The data set of pp collisions at 8 TeV centre-of-mass energy collected in 2012 by the CMS experiment and corresponding to an integrated luminosity of 19. 7 fb -1 is analysed. The measured distributions in ∆φ dijet are compared to calculations in perturbative QCD for 3jet production with up to four outgoing partons that provide NLO predictions for the range of 2π/3 ≤ ∆φ dijet < π and LO predictions for π/2 ≤ ∆φ dijet < 2π/3. The NLO predictions describe the data down to values of ∆φ dijet ≈ 5π/6, but deviate increasingly when approaching   the 4-jet region, starting at ∆φ dijet = 2π/3, particularly at low p max T.
Acknowledgments We acknowledge discussions and comparisons with  P. Sun ,  C. P. Yuan , and  F. Yuan  following the approach of [48]. We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  ThEPCenter, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Diffraction data (Table  1 ) were collected on ID29 (apo-DntR) and ID23-1 (H169TDntR) of the European Synchrotron Radiation Facility (ESRF  36, 37  ). Diffraction images were integrated and scaled using XDS  38  , symmetry-related intensities merged using SCALA  39  and structure factors derived using TRUNCATE  40. For both apo-DntR and H169T-DntR structure solution was carried out using Molecular Replacement (MR) in the program PHASER  41  using the crystal structure of acetate-bound DntR (PBD ID, 1UTB 5 ) stripped of water molecules and other ligands as a search model. Structure refinements (Table  1 ) were carried out in REFMAC5  42  and PHENIX  43  interspersed with rounds of manual rebuilding in COOT  44. The crystal structure of apo-DntR was refined to d min = 2. 64 Å resulting in a model providing R work = 19.
Individual exposures were processed automatically and independently using PyFAI  47  as implemented in the EDNA framework yielding radially averaged curves of normalized intensity versus scattering angle. For each exposure series additional data reduction used the automatic data processing tools of EMBL-Hamburg ATSAS package  48  to combine individual measurements, exclude any data points affected by aggregation induced by radiation damage, and yield an average scattering curve. Merging of scattering curves obtained at different sample concentrations and downstream analysis were performed manually as described in the literature  49. Here, the forward scattering I(0) and radius of gyration (Rg) were calculated from the Guinier approximation  50. Particle volume and the maximum particle size (D max ) were determined from the pair distribution function P(r) as computed by GNOM 51 using PRIMUS  52.
94/22. 72 18. 82/24. 03 Rmsd bond lengths (Å) 0. 004 0. 004 Rmsd bond angles (°) 0. 969 0. 863 Estimated coordinate error (Å) 0. 24 0. 36 c Molprobity clash/overall scores 14. 84/2. 66 8. 31/1. 98 Ramachandran analysis % Favoured 95. 4 97. 2 % Allowed 4. 1 2. 8 % Disallowed 0. 5 0. 0 % rotamer outlier 7. 4 3. 2 wwPDB ID code 5AE5 5AE4.
Multilocus sequence typing (MLST) MLST was performed according to the Pasteur scheme (http:// www. pasteur. fr/recherche/genopole/PF8/mlst/Abaumannii. html). The internal fragments of seven housekeeping genes (fusA, pyrG, rpoB, rplB, cpn60, gltA, and recA) were amplified, then purified and sequenced using an ABI 3130XL DNA sequencer (Applied Biosystems, Foster City, CA, USA). The allelic number and sequence types (STs) were analyzed using the A. baumannii MLST database. New STs were submitted and assigned by the Institut Pasteur MLST database. The clustering of related STs (defined as clonal complexes, CCs) was analyzed using eBURST software (http:// eburst. mlst. net). A CC is defined as a set of similar ST(s) having six identical loci among seven, so a CC is formed by the founder ST and its single locus variants (SLV).
Moreover, the first description of a bla OXA-143-like strain is reported here, which was isolated in 2014 from a wound sample of a female patient admitted to TGH. Alarmingly, since the first description in Lebanon of a bla NDM-1 -positive A. baumannii  30  and Acinetobacter pittii,  31  four more isolates were found in this study. Epidemiological tools are important to track outbreaks and to investigate strain relatedness in order to control the spread of MDR isolates. The evolutionary history and the dynamic spread of A. baumannii in this study were investigated by MLST. The data revealed that CC2 (ST2, ST570, ST600, ST713) was the main clonal complex found in Lebanon over recent years. Interestingly, almost all of these isolates were resistant to carbapenems, which is in agreement with previous studies that have used MLST for epidemiological characterization of A.
Acknowledgements We thank the team of the  Institute Pasteur MLST system (Paris, France ) for importing novel alleles, profiles, and isolates at http:// www. pasteur. fr/mlst. We are very grateful to the clinical laboratory departments at all hospitals for their collaboration in this study. The authors gratefully thank  Catherine Quinqueneau ,  Catherine Ramont ,  Maryam Yehya ,  Taha Abdo ,  Majd Mouzawak ,  Marianne Ekko ,  Aicha Borghol , and  Rola Hajjar  for their excellent technical assistance. Funding: This work was supported in part by the  Lebanese University  and the  National Council for Scientific Research, Lebanon. Conflict of interest: We have no conflicts of interest.
C. Thermal analysis In order to ensure that the design is working within the operational temperature range, a thermal analysis is carried out for different slit thicknesses. In the design, the space between the slit and the slit depth are set to be 0. 2mm and 20mm respectively. Due to the high power density requirements in the design, the machine is loaded with a high current density. Oil cooling is therefore adopted for the stator, in which the stator region is isolated from the rotor region and flooded with oil, allowing the rotor to run dry and maintain minimal windage losses. A lumped parameter thermal model is used to predict the thermal performance. For accuracy, a 1/6 crosssection of the rotor is modelled initially using Computational Fluid Dynamics (CFD) software with the predicted heat transfer coefficient later implemented in the lumped model.
Exosome purification, transmission electron microscopy (TEM), and exosomes-derived PTPs purification Exosomes from MCA205 and HEK-293 cells expressing or not the Glob-intron-SL8 construct were purified as described previously. 46 For negative staining of exosomes,  47  five microliters of exosome fractions were adsorbed onto a 300 mesh copper grid coated with a collodion film covered by a thin carbon film, activated by glow-discharge. After 1 min, grids were washed with aqueous 2% (w/vol) uranyl acetate (Merck, France) and then dried with ashless filter paper (VWR, France). TEM observations were carried out on a Zeiss 912AB transmission electron microscope in filtered low loss mode. Electron micrographs were obtained using a ProScan 1024 HSC digital camera and Soft Imaging Software system (Eloise).
p. 3 h after injection of OT-1 T cells. Spleens were harvested »72 h after OT-1 T cells injection and then the proliferation of the OT-1 T cells was determined by staining for CD8 C and CD45. 1 and gating for CFSE-labeled CD8 C CD45. 1 C cells using a CANTO II flow cytometer. A total of 1 £ 10 5 events were normally acquired and analyzed with FlowJo software (Tree Star).
Acknowledgments We thank  Joan Goverman  ( Department of Immunology University of Washington, Seattle, USA ) for CD8 C T cells against the MBP class I epitope. We thank  Peter Cresswell  ( Department of Immunobiology, Yale school of Medicine, USA ) for the US6 and ICP47 expression plasmids. We also thank  Prof. Laurence Zitvogel  for valuable comments on the manuscript.
Sample assignments and analysis of 454 sequencing data All 16S rDNA gene pyrosequencing reads were analyzed using the GnS-PIPE developed by the GenoSol platform (INRA, Dijon, France) and optimized for amplicons analysis  [63] ,  [64]. First, all the raw reads were sorted according to the multiplex identifier sequences. The raw reads were then filtered and deleted if they harbored: (a) a length lower than 400 bp, (b) one or more ambiguities (Ns), (c) an error in the proximal primer sequence (the distal primer sequence must be perfect too, but can be potentially incomplete), (d) an average quality score below 20. A PERL program was then applied for rigorous dereplication (i. e. clustering of strictly identical sequences). The dereplicated reads were then aligned using Infernal alignment  [65] , and clustered into Operational Taxonomic Units (OTUs) at 97% of similarity using a PERL program that groups rare reads to abundant ones, and does not count differences in homopolymer lengths.
A filtering step was then carried out to check all single-singletons (reads detected only once and not clustered, which might be artifacts, such as PCR chimeras) based on the quality of their taxonomic assignments. Finally, in order to compare the datasets efficiently and avoid biased community comparisons, the reads were homogenized by random selection of 3,000 reads for each sample. The retained high-quality reads were used for: (i) taxonomy-independent analyses, determining several diversity and richness indexes (number of clusters, Chao1 estimator, Shannon and Evenness) using the defined OTU composition, and (ii) taxonomy-based analysis of each OTU using similarity approaches against dedicated reference databases from SILVA  [66]. The bacterial communities from all samples were also compared and clustered by using unweighted UniFrac distances  [67] , based on the 16S phylogenetic trees computed with FastTree  [68].
Phylogenetic analyses of vertically transmitted bacteria Representative sequences of OTU corresponding to bacteria known to be vertically transmitted and shared by at least two species of our sample were then aligned with mafft 6. 822 using default parameters  [69]  and their phylogenetic positions compared with reference GenBank 16S rDNA genes from other arthropod hosts (S1 Table ). Maximum Likelihood (ML) trees were built for Rickettsia spp. , Spiroplasma spp. and Wolbachia spp. First, we used the Akaike information criteria corrected for small sample sizes  [70]  to select the best-fitting substitution models with jmodeltest  [71]. We then built ML trees for each genus using RAXML 7. 2. 7  [72]. Node support was assessed by bootstrapping (1000 pseudoreplicates). Ehrlichia chaffeensis (Rickettsiales: Anaplasmataceae) was added and used as outgroup for Rickettsia spp.
The PCR parameters were 94°C for 2 min; 35 cycles of 94°C for 30 s, 55°C for 45s and 72°C for 1min; and a final step at 72°C for 10 min. The PCR products were sequenced using the Sanger method, using an ABI 3730xl capillary sequencer. Sequences were aligned and analyzed using Geneious 7. 0. 6. With 16s and fbpA sequences combined from Wolbachia, we then built a ML tree using RAXML 7. 2. 7  [72]. Node support was assessed by bootstrapping (1000 pseudoreplicates). The data were partitioned into ribosomal regions (two 16S rDNA fragments) and fbpA.
Five OTUs identified as Rickettsia, respectively one in A. bilineata (central) samples and four in (western) T. rapae, were compared to Rickettsia sequences found in GenBank, the comparison being based on 375 bp. (S1 Table ). The different groups of Rickettsia are not well supported by bootstrap values but strains infecting each insect host seem to be different: A. bilineata is infected by a Rickettsia strain in the Bellii group while T. rapae sequences are more closely related to Rickettsia limonae and to endosymbionts of the parasitoid wasp Asobara tabida. (Fig  4 ). Eleven OTU retrieved in the two Aleochara samples (and a few sequences retrieved in T. rapae and D. radicum samples) belonging to Spiroplasma were compared to several sequences found on GenBank using a 365 bp length (S1 Table ).
Abundance of bacterial taxa from each samples: Abundance of dominant species in our dataset based on 97% similarity OTU assignments. 'Western', 'central' and 'eastern' populations refer respectively to Finistère, Côtes d'Armor and Ille & Vilaine regions of Brittany. Numbers indicate the number of reads associated to each genus. Known heritable symbionts are marked by an asterisk (results of both PCR replicates are pooled).
Acknowledgments We thank  Valérie Chaminade  for intensive insect rearing and maintenance,  Stéphane Derocles  in particular for triple-checking and advice on the maximum likelihood tree procedure,  Philippe Vandenkoornhuyse ,  Sophie Coudouel  and  Alexandra Dheilly  from the  Environmental genomics platform Biogenouest ,  Lionel Lebreton  for some R scripting and three anonymous referees for detailed remarks on earlier versions of this manuscript.