dataesr/scientific-paragraphs-categorization huggingface dataset:
This recast analysis is publicly available online at  [78] , together with a validation note  [79]. This analysis targeted decays of the SUSY partner of the top quark, the stop, into a charm quark and neutralino final state, for a compressed stop-neutralino spectrum. The search tags the emission of a hard initial state radiation jet recoiling against the E miss T. The generated parton level events for the process pp → ψψj were hadronised with the PYTHIA6 [80] package. A merging scale of 30 GeV was used to perform the Matrix element-Parton Shower matching (ME-PS)  [81]  between the 0 and 1 jet samples. A fast detector simulation was performed with the MadAnalysis5 tuned version of the Delphes3 [82] package as described in  [77]. Jets were reconstructed using FastJet  [83] , via an anti-k T  [84]  algorithm with a cone size of 0.
4 and they are required to have p T > 20 GeV. Furthermore we have used the ATLAS AUET2B tune  [85]  to simulate underlying events. The reconstructed events were finally passed through the aforementioned recast ATLAS monojet analysis  [74] , which consists of three signal regions targeting (p j T , E miss T ) threshold combinations of (280, 200), (340, 340) and (450, 450) GeV respectively. To obtain the constraints arising from the ATLAS monojet analysis, we have used the exclusion-CLs. py module implemented in the MadAnalysis5 package. This module determines, given the number of signal, expected and observed background events, together with the background uncertainty (the latter three directly taken from the experimental publications), the most sensitive signal region (SR) of the analysis and the exclusion CL using the CLs prescription  [86, 87]  for the most sensitive SR.
Acknowledgements AG and SK are supported by the 'New Frontiers' program of the Austrian Academy of Sciences. The work of DS is supported by the French ANR, project DMAstroLHC, ANR-12-BS05-0006, and by the Investissements d'avenir, Labex ENIGMASS. We thank Giorgio Arcadi, Yann Mambrini and Alberto Mariotti for several useful discussions.
All included manuscripts were reviewed blindly and successively read by 2 endocrinologists in order to complete the dataset. Only articles describing in details GO evolution in patients treated by RAI therapy for differentiated thyroid cancer were retained. Cases with no GO before RAI treatment and cases with GD treated with surgery and RAI without differentiated cancer were excluded. In each retained article, clinical, biological, and ophthalmological data were available and reportable. We contacted the authors of the published articles, where appropriate, for further pertinent data. This study is retrospective and a review of previously published date, so informed consent is not required.
Data assessment The following data were assessed: age, sex, gender, smoking status, duration of GD before surgery, biological data as levels of FT4 (pmol/L), FT3 (pmol/L), thyroid-stimulating hormone (TSH) (UI/mL), TSH receptor antibodies (TRAK) before and after RAI (biological parameters were converted to fold normal values to standardize result), thyroid volume, indication of thyroidectomy (poor control of hyperthyroidism, intolerance to treatment, multi nodular goiter, volume goiter), management of GO before RAI (local treatment, surgery, radiotherapy, oral and/or intravenous glucocorticoid administration), cumulative dose of administered RAI (GBq), use of recombinant human TSH (RhTSH) stimulation or after thyroid hormone withdrawal (THW) before RAI, and thyroglobulin levels during follow-up, delay before worsening, and duration of follow-up.
Statistical analysis All the analyses were performed using Stata software (version 13; StataCorp, College Station, TX) and were done for a 2-sided type I error of a = 5%. Baseline characteristics were presented as the mean ± standard deviation (SD), median (interquartile range) for continuous data (assumption of normality assessed by using the Shapiro-Wilk test), or as the number of patients and associated percentages for categorical parameters. Quantitative variables were compared between independent groups (deterioration or improvement/stabilization of GO) by Student t test or Mann--Whitney test if conditions of t test were not respected (normality and homoscedasticity analyzed using Fisher-Snedecor test). Comparisons between independent groups were done by Chisquare or when appropriate by Fischer exact test for categorical variables.
Results Seven patients with GO, treated both by thyroidectomy and RAI for incidental thyroid carcinoma, were included from the University Hospital of Clermont-Ferrand. Six cases came from the University Hospital of Lyon-Est. The number of cases with previously published data that met the inclusion criteria for this study was 22. They are from a case report series with 3 patients,  [19]  a clinical trial with 17 cases published by De Bellis et al,  [20]  and from 2 case reports. [21, 22] Finally, 35 patients were included for analysis (Fig. 1 ).
NTUM-10819. In total, we accessed tissue samples from seven G. oblongus specimens including the two specimens from Taiwan and five specimens previously collected from New Caledonia (including the holotype and all three paratypes of the species), one G. satoi specimen, and two unidentified Gymnocranius specimens from Taiwan (Table  1 ). , Genomic DNA was extracted using an automated DNA-extractor (LabTurbo 48 Compact System with LGD 480-220 kits: Taigene Bioscience Corporation, Taipei) following the manufacturer's protocol. Protocols for cytochrome b gene amplification and sequencing have been outlined in  Borsa et al. (2013). The sequences were deposited in GenBank (http://www. ncbi. nlm. nih. gov/) as a genetic reference for future DNA-identification and research on large-eye seabreams. The sequences were aligned with homologous sequences from Gymnocranius elongatus  Senta, 1973 , G.
grandoculis, and Gymnocranius griseus  (Temminck and Schlegel, 1843)  published by Lo  Galbo et al. (2002)  and retrieved from GenBank (accession nos. AF381260, AF381275, and AF381259, respectively), and those of G. superciliosus (from New Caledonia) and G. satoi (from West Papua) that have been produced previously  (Borsa et al. , 2013). The softwares PopART (University of Otago, available from http://popart. otago. ac. nz) and PAUP*  (Swofford, 2002)  were used to compute the number of haplotypes, the minimum spanning network, pairwise p distances, nucleotide diversity, and segregating (polymorphic) sites to assess the intra-and inter-specific genetic diversity of the cytochrome b sequences. The level of genetic differentiation between populations was estimated by ɸ ST ,  Excoffier et al.
The equivalent transversal dark bar in the smaller G. oblongus specimen from Taiwan (indicated by an arrow on Fig. 1a ) ran forward and crossed with another transversal dark bar running from the base of 5th to 6th dorsal spin to ventral part of body at approximately the place of the of the tip of the pectoral fin tip; a faint dark blotch was visible at the crossing position (Fig. 1a ). These pigmentation patterns also characterize an early juvenile specimen from New Caledonia  (Borsa et al. , 2010: figure 3). Seven new cytochrome b gene sequences of G. oblongus (GenBank nos. KU597058-KU597064) being produced in this study resulted in three haplotypes. The nucleotide diversity was 0. 18%. Individual LET 439 from Taiwan possessed the same haplotype as 4/5 individuals from New Caledonia. The haplotype represented by the Penghu specimen (LET1209) diverged by two mutation steps from it.
List of Gymnocranius specimens referred to in text, with sampling details, collection nos. and GenBank accession nos. IRDN, Institut de recherche pour le développement, Nouméa; MBPL, Marine Biodiversity and Phylogenomics Laboratory, Taipei; MNHN, Museum national d'histoire naturelle, Paris; NTUM, National Taiwan University museums, Taipei; NMMBA, National Museum of Marine Biology and Aquarium, Pingtung, Taiwan.
ACKNOWLEDGMENTS We would like to thank  WJC's lab members , especially  Pei-Chun Lo , for assistance in the laboratory, and  Hsiu-Chen Lin  and  Jhen-Nien Chen , for the management of specimens. This work was supported by research grants from the  Ministry of Science and Technology, Taiwan  ( MOST 101-2611-M-002-016-MY3  and  MOST 104-2611-M-002-002-MY3  to WJC).
Acknowledgements We are grateful to  Abdelmalek Abdesselam ,  Connor Behan ,  Sheer El-Showk ,  Pronob Mitter ,  Hugh Osborn ,  João Penedones ,  Leonardo Rastelli ,  Riccardo Rattazzi  and  Kay Wiese  for the useful discussions and communications. S. R. is grateful to  Sergey Sibiryakov  for a discussion which prompted to settle this issue. This research was partly supported by the  National Centre of Competence in Research SwissMAP  funded by the  Swiss National Science Foundation. MFP was supported by a  Marie Curie Intra-European Fellowship of the European Community 's  7th Framework Programme  under contract number  PIEF-GA-2013-623606.
Materials and methods: This was an analysis of the BREATHE study, a cross-sectional survey of COPD conducted in the general population of eleven countries in the Middle East and North Africa, including Pakistan. The study population consisted of a sample of subjects with COPD for whom the presence of comorbidities was documented. Three questionnaires were used. The screening questionnaire identified subjects who fulfilled an epidemiological case definition of COPD and documented any potential comorbidities; the detailed COPD questionnaire collected data on respiratory symptoms, COPD exacerbations, and comorbidities associated with COPD; the COPD Assessment Test collected data on the impact of respiratory symptoms on well-being and daily life. Results: A total of 2,187 subjects were positively screened for COPD, of whom 1,392 completed the detailed COPD questionnaire.
16 ata on the prevalence and the impact of COPD-related comorbidities are limited in the MENA region. BREATHE was an international study conducted in ten countries of the MENA region and in Pakistan with the aim of estimating the prevalence of COPD symptoms in the general population, and of describing smoking habits, management, and burden of COPD and related health care utilization. 17 The analysis described here addresses the presence of comorbidities in subjects with COPD identified in the BREATHE database. The objective of this analysis was to assess the frequency of comorbidities in subjects with COPD and their association with respiratory symptom severity and COPD exacerbations.
Materials and methods This analysis was performed on data from the BREATHE study, the methodology of which has been described in detail elsewhere. 17 Briefly, this study was a cross-sectional epidemiological survey of COPD conducted in a random sample of the general population of eleven countries (Algeria, Egypt, Jordan, Lebanon, Morocco, Pakistan, Saudi Arabia, Syria, Tunisia, Turkey, and the United Arab Emirates [UAE]) between June 2010 and December 2011. The present analysis concerned data related to comorbidities associated with COPD.
study procedures and data collection This study was based on the population analyzed in the BREATHE study, 17 a population-based sample aged at least 40 years contacted by telephone. The overall response rate, defined as the ratio between the number of subjects who completed the screening questionnaire and the number of potentially eligible subjects, was 74. 2%. Three questionnaires were used in the study. The screening questionnaire, completed by 62,086 subjects,  17  collected data on demographics, respiratory symptoms, smoking habits, and the presence of comorbidities. The objective of the screening questionnaire was to identify subjects who fulfilled an epidemiological case definition of COPD. 10  In this epidemiological definition, COPD cases were defined as eligible subjects fulfilling both of the following criteria: • either: 1) diagnosis criterion -already diagnosed with COPD, emphysema, or chronic bronchitis; or 2) symptom criterion -presenting either with symptoms that fulfill the definition of chronic bronchitis or with dyspnea.
• smoking criterion: lifetime smoking exposure of $10 pack-years. All subjects positively screened for COPD were included in the present analysis. The second questionnaire consisted of a detailed COPD questionnaire that was administered only to subjects screened positively for COPD (n=2,187), and collected data on symptom severity, COPD exacerbations, and comorbidities. A total of 1,392 subjects with COPD completed this questionnaire. 18 The characteristics of this population were similar to those screened positively for COPD (n=2,187 subjects) in terms of demographics and COPD symptoms. 18 ata relating to comorbidities were documented from replies to the following two questions of the detailed questionnaire. The third questionnaire, which consisted of the COPD Assessment Test (CAT),  18, 19  was administered to subjects with COPD who had completed the detailed questionnaire.
Data analysis The data presented in this study are principally descriptive. The occurrence of comorbidities was compared between subjects completing the screening questionnaire who fulfilled the criteria for COPD (n=2,187) and those that did not fulfill these criteria (n=59,707). Adjusted odds ratios were calculated controlling for potential confounding by age and sex using logistic regression analysis. The association between comorbidities on the one hand and occurrence of exacerbations, severity of COPD symptoms, and GOLD severity groups on the other was evaluated in COPD subjects who had completed the detailed COPD questionnaire (n=1,392). Study variables were compared using the χ 2 test and the Mantel-Haenszel test as appropriate. All statistical tests were two-tailed, with a probability threshold of 0.
Acknowledgments The authors would like to thank all members of the  BREATHE Study Steering Committee  for their contributions to this study:  A Alzaabi  ( Abu Dhabi ,  UAE ),  M Beji  ( Tunis, Tunisia), A Ben Kheder (Tunis, Tunisia ),  M Idrees  ( Riyadh, Saudi Arabia ),  G Iraqi  ( Rabat, Morocco), A Javed (Peshawar, Pakistan), JA Khan (Karachi, Pakistan ),  A Khattab  ( Cairo, Egypt), ML Koniski (Beirut, Lebanon ),  B Mahboub  ( Sharjah, UAE ),  S Nafti  ( Algiers, Algeria), NM Obeidat (Amman, Jordan ),  M Polatli  ( Aydin, Turkey ),  A Sayiner  ( Izmir, Turkey ),  N Shahrour  ( Damascus, Syria), MA Tageldin (Cairo, Egypt ),  S Taright  ( Algiers, Algeria ),  E Uzaslan  ( Bursa, Turkey ),  S Wali  ( Jeddah, Saudi Arabia ). Foxymed also provided medical writing support in the form of writing assistance, collating authors' comments, grammatical editing, and referencing that was paid for by  GlaxoSmithKline.
This analysis uses data collected from 2010 to 2013, corresponding to 6. 27 × 10 20 protons on target (p. o. t. ). The neutrino interactions at different INGRID modules, which are distributed at different positions and thus observed different beam spectra, is used to extract the energy dependence of the cross section. The topology of each event, which is based on the kinematics of the outgoing muon, is also used to further improve the sensitivity of this measurement to the neutrino energy, since the two are directly related. The different energy spectra and event topologies are combined to construct a probability density function (PDF), which is used to measure the cross section using the least χ 2 method. The paper is organized as follows. In Sec. II, we describe the T2K near detector, INGRID.
III. SIMULATING NEUTRINO INTERACTIONS IN THE INGRID DETECTOR In this analysis the cross section is measured by comparing data to a prediction, which is calculated using three sequential MC simulations: 1. Prediction of the neutrino flux at the INGRID modules. 2. Simulation of the neutrino-nucleus interactions in the iron target ( 56 Fe). 3. Propagation of final state particles through the detector and modeling of its response. After these processes, an event selection, which is detailed in Sec. IV B, is applied to the output in the same way as the data. A. Neutrino flux.
B. Neutrino-nucleus interaction simulation Neutrino-nucleus (Fe) interactions in the INGRID module are simulated by a neutrino event generator, which is a composite of different neutrino interaction models. NEUT (ver. 5. 1. 4. 2)  [18]  is used as the primary event generator in this analysis. GENIE (ver. 2. 8. 0)  [19] , a different neutrino interaction simulation package, is also used for comparison. This section first describes the various interaction models simulated in the NEUT event generator, then explains the systematic uncertainties associated with each model. Details of the event generators used in T2K can be found in Ref. [20].
Event selection: In this analysis, uncertainties in the following event selection steps are taken into account: • 3D track matching • Vertexing • Veto cut • FV cut The systematic error on the number of selected events is evaluated by varying the selection threshold for each step, picking the loosest or tightest threshold. The change from the nominal threshold to the loosest (tightest) is defined as the +1σ(-1σ) change. The resultant fractional variation in the number of selected events (≡ ∆N/N ) due to the ±1σ change is computed for both the data and MC. Any difference in ∆N/N between the data and MC is then taken as a systematic error. Uncertainties on the hit efficiency of the tracking planes, the contamination due to beam-related BGs, the hit inefficiency of the upstream veto plane, and the tracking efficiency were found to be negligible for this analysis and are not included in the final result.
VII. CONCLUSION In this paper, we have reported the measurement of the energy dependent inclusive ν µ charged current cross section on iron using the T2K INGRID detector and the T2K neutrino beam. The unique variation in the neutrino flux across the INGRID modules, along with event kinematic information, was used to produce event samples sensitive to neutrinos with energies from 1-3 GeV. These were used to extract the inclusive CC muon neutrino interaction cross section on iron at 1. 1, 2. 0 and 3. 3 GeV, using data corresponding to 6. 27 × 10 20 p. o. t. This result is consistent with the predictions of the NEUT and GENIE neutrino interaction generators. This is the first measurement to use the off-axis effect to measure neutrino cross sections as a function of energy and demonstrates the potential of this technique to provide useful information for future neutrino experiments.
ACKNOWLEDGMENTS We thank the  J-PARC  staff for superb accelerator performance and the  CERN NA61 collaboration  for providing valuable particle production data. We acknowledge the support of  MEXT, Japan ;  NSERC  (grant  SAPPJ-2014-00031 ),  NRC  and  CFI ,  Canada ;  CEA  and  CNRS/IN2P3 , France;  DFG, Germany ;  INFN, Italy ;  National Science Centre (NCN) , Poland; RSF,  RFBR  and  MES , Russia; MINECO and ERDF funds, Spain; SNSF and SERI, Switzerland; STFC, UK; and  DOE, USA. We also thank  CERN  for the UA1/NOMAD magnet,  DESY  for the HERA-B magnet mover system,  NII  for  SINET4 , the  WestGrid and SciNet consortia in Compute Canada , and  GridPP, UK. In addition participation of individual researchers and institutions has been further supported by funds from:  ERC  ( FP7 ),  H2020 RISE-GA644294-JENNIFER ,  EU ;  JSPS, Japan ;  Royal Society, UK ;  DOE   Early Career program ,  USA.
Farm, field and tree were nested random effects. Models containing all of the fixed effects were compared using Akaike's information criterion (AIC) with a second order correction for small sample sizes (AICc) to find the model that best describes the data  (Burnham and Anderson 2002). The model with the smallest AICc is considered the best model, and models that differ by AICc \2 are considered to fit equally well. We also calculated Akaike weights, which give the probability that a model is the best of a set of candidate models  (Wagenmakers and Farrell 2004). Statistical analysis was carried out in software package R, version 3. 0. 3 using packages glmmADMB, stats4, bbmle and Mass  (Venables and Ripley 2002; Fournier et al. 2012; Bolker and R Core Team 2014).
Likelihood ratio tests were performed to test the significance of gall density on rate of parasitism and the marginal and conditional R 2 were calculated to assess the amount of variance explained by the fixed and random effects  (Nakagawa and Schielzeth 2013). In order to investigate HDD and HDI parasitoid aggregation at spatial scales greater than the leaf, parasitism data were first pooled for each distance within each field (the field scale) and then pooled for each field (the landscape scale). Tests for HDD and HDI aggregation were carried out using the method described above. To develop an understanding of how parasitism varied throughout fields, generalized linear mixed effects models (GLMM) (binomial error structure and logit link function) were created that again included farm and field as nested random effects, sampling event (first or second) as a crossed random effect, and a standardized random effect with a normal distribution was used to account for overdispersion when required  (Browne and Subramanian 2005).
Likelihood ratio tests were then carried out to test the significance of fixed effects. Finally the marginal and conditional R 2 were calculated for the best fitting models to assess the amount of variance explained by the fixed and random effects  (Nakagawa and Schielzeth 2013). Statistical analyses were performed using the software package R, version 3. 0. 3, and the packages lmerTest  (Kuznetsova et al. 2014) , bbmle (Bolker and R Core Team 2014) and boot  (Canty and Ripley 2014).
Acknowledgements We would like to thank  T. Carroll ,  W. Lammers ,  C. Moxley  and  M. Oddie  for assistance in the field;  Bavaria Estates  and  Mohlatsi  staff for access to farms and information on pest populations. The research was conducted with financial support to the ' NETWORK ' project from the  European Commission Marie Curie International Research Staff Exchange Scheme (IRSES)  (Grant agreement:  PIRSES-GA-2012-318929 ), and funding to CLS from the  South African Department of Science and Technology  ( DST/CON0054/2013 ) and  National Research Foundation  (Grant  90139 ). We thank the farm owners and managers who granted access to field sites.
Acknowledgments This work is supported by the  Grant-in-Aid for Scientific research  from the  Ministry of Education, Science, Sports, and Culture (MEXT), Japan  No. 26104009  and  26287039  (S. M. ), as well as by the  World Premier International Research Center Initiative (WPI) ,  MEXT, Japan  (S. M. , K. M. and Y. S. T. ). The work of K. M. is supported in part by a  JSPS Research Fellowships for Young Scientists. S. B acknowledges the support of the  Indo French LIA THEP (Theoretical high Energy Physics)  of the CNRS. S. B. acknowledges the hospitality of IPMU where the idea for this work was conceived, and also acknowledges the cluster facility at the  Harish-Chandra Research Institute  http://www. hri. res. in/cluster/. We also thank  Daniel Schmeier  for technical help regarding CheckMATE.
This reduced dataset was comprised of 453 site-years (397 for upland crops and 56 for paddy rice) at 51 sites observed from 1992 to 2013 (Fig. 1  and Data S1), which could produce 338 pairs of LR. However, only 388 site-years at 37 sites with at least three N-input levels could produce 96 pairs of DLR. To interpret the nonlinearity of nitrogen leaching to N rate , we analyzed the f k (Table  1 ) known to affect the soil N cycling and transport in the root zone when available in field experiments. However, fertilization methods (e. g. , broadcast, incorporation, injection, and deep placement) and tillage practices were not considered due to lack of information about in the surveyed literature. All site-years in the original studies were used and were averaged by replicates if necessary. Few of the missing climate and soil properties within observation periods were supplemented by the 1-km Harmonized World Soil Database (HWSD) v1.