dataesr/scientific-paragraphs-categorization huggingface dataset:
The back-to-back configuration together reduces a systematic bias related to the forward-to-backward emission ratio  [23]. The response C ϕ was obtained from a weighted sum of the response of the two chambers. The weights were based on the relative thickness of the layers, which was deduced from a TOF spectrum at low energies obtained from measurements at 50 Hz. The data processing was carried out using the AGS (Analysis of Geel Spectra) package  [24, 25]. This package includes all basic spectrum operations and performs a full uncertainty propagation accounting for both correlated and uncorrelated uncertainty components and results in a substantial reduction of space for data storage. The AGS concept is recommended by the Network of Nuclear Reaction Data Centres to store experimental data in the EXFOR data base  [26].
Normalization The data for the 0. 52 mm and 1. 01 mm thick Au samples were internally normalized based on the peak of the wellisolated and saturated resonance at 4. 9 eV, which has a low scattering to capture ratio. The normalization was obtained by a least squares adjustment of the experimental yield with only the normalization factor as a free-fitting parameter. The REFIT code  [27] , which is based on the Reich-Moore approximation of the R-Matrix theory, was used. This code accounts for various experimental effects such as the Doppler effect, neutron self-shielding, multiple interaction events, and the response function of the TOFspectrometer. To account for the impact of the neutron flux and γ-ray attenuation in the sample on the weighting function a special procedure has been implemented in REFIT  [11, 18].
For the M 1 strength a parametrization proposed by Heyde et al. [33]  that results from an adjustment to experimental data was used. For E2 strengths the recommendation of the RIPL3 data base  [34]  was followed. In the calculation the characteristics of the first ten excited states of 198 Au, i. e. their spin and parity as well as their transition strengths and branching ratios, were taken from the ENSDF database  [35]. For the 4. 9 eV resonance the absolute transition strengths of primary transitions (i. e. absolute intensity per capture event) to these low lying states have been determined experimentally and were taken from  [35]. Their relative contribution to the total primary emission intensity is about 17%. In the unresolved resonance region (URR) absolute intensities of primary transitions to low-lying excited states are not available.
[49]. The generalized ENDF-6 model together with the standard boundary conditions has been used (see Sirakov et al. [49] ). The capture channel transmission coefficients for d-wave neutrons have been determined from the s-wave coefficient. The strength function for d-waves S 2 was taken from ref. [50]. In the calculations the cor-related and uncorrelated components reported by Poenitz et al. [47]  and Sirakov et al. [49]  were taken into account. In the paper of Purtov et al. [48]  the different uncertainty components are not discussed. For these data a 2% normalization uncertainty was included. To avoid bias effects due to Peelle's Pertinent Puzzle  [51] , the uncertainties due to the normalization of the capture data and the data of refs. [47, 48]  were based on the parameterized cross section  [52].
The data reported in this work are on average 6% higher than the cross sections adopted for astrophysical applications. However, they are about 1. 5% lower compared to a recommended cross section which resulted from a least squares analysis of experimental data that was available in the literature prior to this work. Given the uncertainty reached in the present experiment, the results in this work confirm the bias in the cross sections used for astrophysical applications. We are grateful to the GELINA operators for the dedicated and skillful running of the accelerator as well as to the Nuclear Energy Agency of the OECD and the Nuclear Data Section of the IAEA for their interest in the present research. This work was partly supported by the European Commission through the projects EFNUDAT (FP6-036434) and ERINDA (FP7-269499).
Conclusion: The population of ENERGY reflects real-world conditions with bare metal stents being mainly used in simple lesions. In this setting, percutaneous coronary intervention using a cobalt-chromium thin strut bare metal stent with a passive coating showed very good results up to 24 months. (ClinicalTrials. gov:NCT01056120) Summary for annotated table of contents: The ENERGY international registry evaluated the safety and benefits of a cobalt-chromium thin strut bare metal stent with passive coating in 1016 patients under real-world conditions until 2 years. Results were encouraging with a low composite rate of cardiac death, myocardial infarction and clinically driven target lesion revascularization, even in the pre-defined high risk groups of diabetes, stents ≤2. 75 mm and acute coronary syndrome.
Statistical analysis Statistical analysis was performed based on available data for the total subject cohort (intention-to-treat population) and the following subgroups: diabetes (insulin dependent and non-insulin dependent), stent sizes ≤ 2. 75 mm in diameter, and ACS  [19]. Continuous variables are presented as mean ± standard deviation (SD), and compared using the Student's t-test. Categorical variables are presented as frequencies and percentages, and compared using the Fisher's Exact test or Chi-Square test. 2-year survival was analyzed using the Kaplan-Meier method. A p-value less than 0. 05 was considered statistically significant. For MACE, effect size between subgroups was studied using odds ratios. All statistical analyses were carried out using SAS 9. 3 (SAS Institute Inc. Cary, NC, USA).
Role of the funding source The study was sponsored by BIOTRONIK AG, Bülach, Switzerland. The sponsor was involved in the design of the study, data collection, monitoring, and data analysis and interpretation. The corresponding author had full access to all data in the study and together with the co-authors had final responsibility for the decision to submit for publication. Ethics committee approval was obtained for all participating institutions and the registry was conducted in accordance with the Declaration of Helsinki, Good Clinical Practice and ISO1455, where applicable. All patients provided written informed consent. This study is registered at ClinicalTrials. gov (NCT01056120).
9% compared to 8. 7%  [19]  and 5. 6%  [11]. Further, the revascularization rates of the ENERGY registry compared well to those of registries, randomized studies and meta-analyses of other BMS and DES. At 12 months, the revascularization rate was 4. 9% versus 4. 6%-17. 3% for BMS  [2, 4, 20, [22] [23] [24]  and 2. 2%-10. 4% for DES  [2, 4, 20, 23, 25, 26]. Definite stent thrombosis occurred in 0. 6% of our patients compared to 0. 3%-1. 2% in BMS as well as DES trials  [2, 4, 20, [23] [24] [25] [26]. Notably, the antiplatelet regime was left to the discretion of the treating physician and the relatively high rate of DAPT at follow-up might have influenced event rates. Overall, the low revascularization rates support the hypothesis of a recent analysis of 1. 5 million PCI procedures from the National Cardiovascular Data Registry, suggesting that a less frequent use of DES in patients with a low risk of restenosis has the potential for significant cost savings while minimally increasing restenosis rates  [27].
Blood sample protocol Blood samples were sought from all participants. After the interview and the physical assessment, dedicated nurses performed a phlebotomy for consenting participants. Blood was drawn on two 10 ml polypropylene EDTA tubes for DNA extraction and biological analyses. Samples were registered in a database, centrifuged and aliquoted within few hours (to avoid degradation due to high temperatures), and then transferred to a laboratory to be frozen at -20°C or -80°C. Plasma and buffy-coat aliquots were stored at the Pasteur Institute of Bangui and the National Laboratory of Public Health in Brazzaville before dry-ice shipping to the University of Limoges, where the biobank is located.
Data management The interviewers collected data directly onto laptop computers during the study. Questionnaires were computerized using EpiData (version 3. 1) software, including conditional skips and interactive checking for data consistency. Identical Epidata files were used in each setting, ensuring facilities to data processing and merging. The 10/66 algorithm was processed using SPSS (version 17. 0, IBM SPSS Statistics). Data cleaning, and processing of variables was done using Stata (version 10. 1, Stata-Corp, College Station, Texas, United States) and SAS (version 9. 2, SAS Institute, Cary, NC) softwares. Datasets are stored on secured servers at Limoges University, and declared to the 'Commission Nationale de l'Informatique et des Libertés' (CNIL) according to our national regulations on personal data.
Dissemination Results from this programme will be disseminated at both international and national levels. They will be communicated to international agencies of concern, in particular to the World Health Organization (African office, Brazzaville) and Alzheimer Disease International (ADI, London). These findings will be used in each country to raise public awareness, stimulate clinical training and practice, and influence social and health care policies. A strategy for dissemination was established between all the investigators during a meeting held in Limoges in May 2013. Dissemination through peer-reviewed articles will be prioritized. All the investigators have access to the full anonym database. The final dataset will be approved by the coordinator of the programme and stored securely on Limoges University network.
Acknowledgements - French National Research Agency (ANR)  for funding this study through the  ANR-09-MNPS-009-01  grant, - Universities of Bangui (CAR) and Marien Ngouabi in Brazzaville (Congo) , as well as both  Facultés des Sciences de la Santé in Bangui and Brazzaville, -Institut Pasteur in Bangui and Laboratoire National de Santé Publique in Brazzaville, -Health ministries of the Central African Republic  and the  Republic of Congo , for their moral support, - University of Limoges, Doctoral School of Limoges University, -Limousin Regional Council , -Town halls, mayors, and chiefs of districts of the 2 central African countries for their collaboration, -Investigators working for the programme, and staffs of  Bangui and Brazzaville hospitals  for their assistance, -All the elderly and their families who participated to these surveys.
Source of data All patients who received at least one dose of oxaliplatin in the Medical Oncology Department of the Lyon Sud University Hospital (Hospices Civils de Lyon, France) over a 6-year period from October 2004 to January 2011 were identified using the pharmaceutical software dedicated to antineoplastic preparation. Routine premedication before oxaliplatin infusion included 120 mg of methylprednisolone given intravenously and antiemetic prophylaxis. The following clinical data were collected from medical files: sex, age, history of allergy, oxaliplatin HSR, type of cancer, previous exposure to platinum agents, and oxaliplatin exposure during the study period (total number of courses, doses and cumulative dose), and treatment line number. Data from patients who experienced oxaliplatin-associated HSR were compared to that of controls patients who received oxaliplatin without experiencing HSR.
For the purposes of the present study, HSR was defined as any unexpected adverse manifestation including nonallergic drug hypersensitivity and drug allergy reactions like the typical symptoms of IgE-mediated reactions, including cutaneous symptoms such as palmar or facial flushing, respiratory symptoms such as shortness of breath, and cardiovascular symptoms such as any alteration in pulse or blood pressure  [16]. Expected side-effects, such as chemotherapy-induced nausea or vomiting and diarrhea, were excluded. Severity was graded according to the Ring and Messmer's classification  [17]  and the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE v 4) because of its frequent use by medical oncologists worldwide (Table  1 ). The outcome data was also recorded for each patient.
Statistical analysis Clinicopathological variables potentially associated with oxaliplatin HSR were subjected to univariate analysis. Statistical analysis was carried out using Wilcoxon's test to determine whether age or mean dose were associated with hypersensitivity. The other factors (sex, prior platinum exposure and atopic background) were evaluated using Fisher's exact test. For multivariate analysis, a logistic regression model was applied. All variables with a p-value ≤ 0. 1 on univariate analysis were considered candidates for multiple logistic regression. Two-sided p values exceeding 0. 05 were considered statistically significant. Statistical analysis used SAS software, version 8. Since data were collected retrospectively and that patients' management was not modified, according to the French law (n°2004-806, 9th august 2004), this study did not need to be approved by a research ethics committee  [18].
Trigger and data selection The data used in this analysis were recorded in 2012, while the LHC was operating at a centre-of-mass energy of 8 TeV. The integrated luminosity is 20. 3 fb -1. The uncertainty on the integrated luminosity is ±2. 8%. It is derived, following the same methodology as that detailed in ref. [31] , from a preliminary calibration of the luminosity scale derived from beam-separation scans performed in November 2012. Events selected by single-electron and single-muon triggers under stable beam conditions and for which all detector subsystems were operational are considered. Un-prescaled single-lepton triggers with different p T thresholds are combined to increase the overall efficiency. The thresholds are 24 GeV and 60 GeV for electron triggers and 24 GeV and 36 GeV for muon triggers.
Monte Carlo simulation Monte Carlo (MC) simulated events are used to help determine SM backgrounds and signal yields in the analysis. Background MC samples are processed through a detector simulation  [32]  based on GEANT4  [33]  or a fast simulation using a parameterised response of the showers in the electromagnetic and hadronic calorimeters  [32]. Additional scale factors are applied to bring the simulation into better agreement with the 2012 dataset. These include factors for lepton trigger, reconstruction and identification efficiencies. Samples of W and Z/γ * 4  Monte Carlo events with accompanying jets are produced with Sherpa 1. 4. 1  [34] , using the CT10  [35]  set of parton distribution functions (PDFs). Events generated with Alpgen 2. 14  [36]  use the CTEQ6L1  [37]  PDF set and are interfaced to Pythia 6.
426  [38]  for parton showers and hadronisation with the Perugia2011C tune; these Alpgen samples are used to assess modelling uncertainties. The cross-section normalisations are set to the inclusive next-to-next-to-leading-order (NNLO) prediction from the DYNNLO program  [39]. The production of top quark pairs (t t) is modelled using POWHEG r2129  [40]  for the matrix element using the CT10 PDF set, with the top quark mass set to 172. 5 GeV. Parton showering and hadronisation are performed with Pythia 6. 426 with the Perugia2011C tune. Modelling uncertainties are assessed using events generated with Alpgen 2. 14, using the CTEQ6L1  [37]  PDF set and interfaced to Herwig 6. 5. 20  [41]  for parton showers and hadronisation. The t t cross-section is normalised to 253 +13 -15 pb, calculated at NNLO in QCD including resummation of next-to-next-to-leading logarithmic (NNLL) soft gluon terms with Top++ 2.
Backgrounds from misidentified objects and non-prompt leptons The backgrounds from misidentified objects and non-prompt leptons are estimated with a data-driven matrix method, described in detail in ref. [76]. In the electron channel, this contribution is dominated by the misidentification of hadronic jets, resulting in "fake" electrons. In order to make an estimate using this method, a sample enriched in multi-jet events is obtained by relaxing the electron selection criteria so as to increase the contribution from fakes. This is achieved by loosening the leading electron identification criteria from "tight" to "medium". The contribution from two or more fake electrons is found to be negligible. The numbers of data events in the sample which pass (N pass ) and fail (N fail ) the nominal tighter lepton selection requirements are counted.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZ Š, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
Maerl beds are sensitive biogenic habitats built by an accumulation of loose-lying, non-geniculate coralline algae. While these habitats are considered hot-spots of marine biodiversity, the number and distribution of maerl-forming species is uncertain because homoplasy and plasticity of morphological characters are common. As a result, species discrimination based on morphological features is notoriously challenging, making these coralline algae the ideal candidates for a DNA barcoding study. Here, mitochondrial (COI-5P DNA barcode fragment) and plastidial (psbA gene) sequence data were used in a two-step approach to delimit species in 224 collections of maerl sampled from Svalbard (78u96'N) to the Canary Islands (28u64'N) that represented 10 morphospecies from four genera and two families.
First, the COI-5P dataset was analyzed with two methods based on distinct criteria (ABGD and GMYC) to delineate 16 primary species hypotheses (PSHs) arranged into four major lineages. Second, chloroplast (psbA) sequence data served to consolidate these PSHs into 13 secondary species hypotheses (SSHs) that showed biologically plausible ranges. Using several lines of evidence (e. g. morphological characters, known species distributions, sequences from type and topotype material), six SSHs were assigned to available species names that included the geographically widespread Phymatolithon calcareum, Lithothamnion corallioides, and L. glaciale; possible identities of other SSHs are discussed. Concordance between SSHs and morphospecies was minimal, highlighting the convenience of DNA barcoding for an accurate identification of maerl specimens.
On the one hand, the Automatic Barcode Gap Discovery (ABGD)  [30]  is a fast method that uses distances to split the sequence alignment into a set of PSHs following a recursive procedure until there is no further partitioning. This procedure automatically finds breaks in the distribution of genetic pairwise distances, referred to as the 'barcode gap', even when intra-and interspecific distances overlap. On the other hand, the General Mixed Yule Coalescent (GMYC) model  [29]  is based on detecting the shift of the branching rate that takes place in clock-constrained calibrated trees at the point of transition from species-level (speciation) to population-level (coalescence) evolutionary processes. Using a likelihood criterion, the GMYC method permits an automated species delineation with appropriate statistical measures of confidence.
Study area and sample collection As the study did not involve endangered or protected species, no specific permissions were required for sampling at most locations (see Table  S1  for coordinates). Still, sampling at two locations situated within a national park in NW Spain (lat 42. 211u long 28. 896u and lat 42. 394u long 28. 815u) was conducted with the permission of the park authority (Parque Nacional Marı ´timo Terrestre de las Islas Atlanticas de Galicia) and the park authority has signed a document stating its interest in the results of this study. Collection information for all the specimens used in this study is available at the Barcode of Life Data Systems (BOLD: www. boldsystems. org; project ''maerl-NE Atlantic''). From 1999 to 2011, maerl specimens were extensively sampled by SCUBA diving or dredging within 4 out of the 5 regions of the OSPAR maritime area (Table  S1 ); sampling ranged from the low intertidal to 40 m depth.
The holotype of M. sphaericum in SANT was collected shortly before the present study (October 2008) and processed alongside topotype material of the same species. Attempts to acquire sequence data from type material of other species included L. corallioides, L. fornicatum, N. brassica-florida, and S. fruticulosa but proved unsuccessful. DNA was extracted with the DNeasy Blood & Tissue Kit Spin-Column Protocol (Qiagen) following manufacture's recommendations. Two gene fragments were amplified: (i) a fragment of 664 bp of the standard DNA barcode (the 5' end of the mitochondrial gene cytochrome oxidase I, COI-5P) with primers GazF1 and GazR1 from  [14] , and GCorR3 (5'TGATTYTTYGGA-CATCCTGA3'), and (ii) a fragment of 892 bp of the plastidial gene photosystem II reaction center protein D1 (psbA) with Table  1.
Data analyses Sequences were aligned and edited using the program Geneious 5. 6. 6. As we aimed to delimit species based on sequence data rather than to assess their phylogenetic relationships, we chose not to run maximum-likelihood or maximum parsimony analyses. Instead, COI-5P sequences were partitioned into a set of PSHs using two bioinformatics tools: ABGD  [30]  and GMYC  [29, 31]. For ABGC, genetic distances between specimens were calculated using the Kimura two parameters (K2P) model, a standard metric in DNA barcoding studies. ABGD was remotely run at http:// wwwabi. snv. jussieu. fr/public/abgd/abgdweb. html using default values except for the relative gap width (X) which was set to 10 to avoid the capture of small local gaps. For the GMYC method, duplicate haplotypes were removed from the alignment using DnaSP 5.
Both the single-threshold and the multiple-threshold versions of the GMYC model  [29, 31]  were optimized onto the output tree with the help of the SPLITS v. 1. 0-19 package for R. AIC-based support values for the GMYC clusters were calculated following  [32]. BEAST and TreeAnnotator were also employed to reconstruct a phylogeny for the psbA gene with the same options used for the COI-5P sequences but a different best-fitting model (GTR+G with G = 0. 175).