dataesr/scientific-paragraphs-categorization huggingface dataset:
The unclustered energy in the detector results in an additional resolution uncertainty of less than 1% on the E T / scale. The difference in jet energy resolution determined in simulation and data results in an uncertainty of less than 1%. The theoretical uncertainties in modeling of tt production are evaluated from dedicated simulated event samples by varying the theoretical parameters of interest around their nominal values. Such variations are used to construct alternative distributions, from which simulated events can be generated. For each variation, 4000 pseudo-experiments are generated and fitted with the standard configuration. The mean bias of the fitted tt cross section is taken as the size of the systematic uncertainty due to the source under study. These include differences in the tt signal due to renormalization and factorization scales (4%), the scale for the ME partons to PS matching scheme (2%), pileup modeling in simulation (less than 1%), and the parton distribution function model (less than 1%).
Summary The tt production cross section measurement has been performed at √ s = 7 TeV using the data collected with the CMS detector and corresponding to an integrated luminosity of 2. 3 fb -1. The tt cross section is measured using a profile likelihood fit to the number of reconstructed jets, the number of b-tagged jets, and the secondary vertex mass distribution. The measured cross section for an assumed top quark mass of 172. 5 GeV is σ tt = 158. 1 ± 2. 1 (stat. ) ± 10. 2 (syst. ) ± 3. 5 (lum. ) pb,  (8)  which is in agreement with the QCD predictions of 164 +10 -13 pb  [8, 9] , 163 +11 -10 pb  [10]  and 149 ± 11 pb  [11]  that are based on the full next-to-leading-order (NLO) matrix elements and the resummation of the leading and NLO soft logarithms. Individuals have received support from the Marie-Curie programme and the European Research Council (European Union); the Leventis Foundation; the A.
Acknowledgements 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:  BMWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MEYS (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES (Croatia) ;  RPF (Cyprus) ;  MoER ,  SF0690030s09  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NKTH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Republic of Korea) ;  LAS (Lithuania) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MSI (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MSTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  NSC (Taipei) ;  ThEP-Center, IPST  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
The nonparametric Wilcoxon test was used to compare repeated measures of pain score and OMED. A pain relapse free survival (PRFS) was calculated for the patients responding at every end points from the CT to d30 (range from 0 to 4 weeks). PRFS was analysed using a Kaplan Meyer method. p <0. 05 was considered significant. Data were analyzed using SPSS version 20 (IBM Software).
(2009)  and the pCO 2 concentration was measured in ppm units using a CO 2 gas probe (Vaisala ® ). The mixed gases were bubbled through 250 l sump tanks and water chemistry parameters and corresponding total al kalinity (TA) were measured for each treatment throughout the experiment (Table  1 ) by using a fixed end point titration method. Experimental calcite (Ω calc ) and aragonite saturations (Ω arag ), as well as actual pCO 2 (µatm) were calculated from average total alkalinity data (TA), salinity, temperature and pHNBS using CO2SYS with dissociation constants of  Mehrbach et al. (1973)  as refitted by  Dickson & Millero (1987)   (Pierrot et al. 2006). Dissolved oxy gen (DO% saturated) was measured daily to ensure sufficient oxygen flow in the treatment tanks.
Data analysis Mean differences among the 4 treatments in the proportion of eggs fertilized, time to complete gastrulation, time to motility, and the number of larvae completing metamorphosis were tested with a fully orthogonal 2-way ANOVA. The factors were Temperature (fixed, 2 levels: 27 and 29°C) and pCO 2 (fixed, 2 levels: ambient and elevated pCO 2 ). Each assay was analyzed independently (i. e. 2 to 4 assays representing the different cohorts of larvae depending on the response variable). A Bonferroni correction was used to adjust the probability of type I error (i. e. probability was considered significant when beta < alpha/n, where beta equals the Bonferoni corrected probability, alpha = 0. 05 and n = the number of assays run for the specific response variable). Any bias in these data was explored by residual analysis.
Acknowledgements. We thank  P. Munday  for the original design of the CO 2 manipulation system and  JCU Marine  and  Freshwater Aquarium  research assistants for technical assistance. We thank  E. Woolsey ,  L. Huisman ,  P. Cross  and  G. Torda  for field assistance and  L. Harrington  for CCA identification. This project was funded by  ARC  grants to W. L. and A. H. B. and the  Ministry of Higher Education of Malaysia  funded the PhD scholarship to C. M. C.
Study design A "before-after" study was carried out in the NICU of the Territorial Hospital Center of French Polynesia during two consecutive years before and after the discontinuation of EPO therapy: from 01/08/2009 to 31/07/2010, i. e. , one year before the arrest of EPO treatment (treated group) and from 01/08/2010 to 31/07/2011, i. e. , one year after the arrest of treatment (non-treated group). The data were retrieved from the medical records.
Data collection The epidemiological, clinical and biological data for each infant included were recovered from the medical records by one person (O. B. ). Intrauterine growth retardation (IUGR) was defined as a birth weight of less than the 10th percentile for the gestational age on reference curves  [21]. The achievement of respiratory autonomy was defined as spontaneous respiration in ambient air without tracheal or nasal support. Postnatal steroid therapy was defined as systemic therapy targeting the lungs and administered ≥15 days of life. Severe cerebral lesions were defined as intraventricular hemorrhage (IVH) of a grade ≥3 of the Papile classification  [22]  or as cystic periventricular leukomalacia. The presence of retinopathy of any stage, of a nosocomial infection suspected or confirmed by clinical, biological or bacteriological data, or of necrotizing enterocolitis of stage ≥3 of the Bell classification was likewise retrieved.
Statistical analyses Statistical analyses were performed using Minitab 13. 3® software (Minitab Inc. , Pennsylvania, USA). Qualitative variables were expressed as percentages and quantitative variables as medians and extremes. The variables "gestational age at birth" and "birth weight" were divided into the following classes: <28 weeks, 28-29. 9 weeks and 30-32 weeks for the gestational age and <1000 g and ≥1000 g for the birth weight. Discontinuous data were compared with the Fisher exact test and continuous data with the Mann-Whitney U test. To determine the factors that affect the need for transfusion after the 15th day of life, the factors presenting a degree of significance of p ≤ 0. 1 in the univariate analysis (i. e. , intrauterine growth retardation, gender, gestational age) were included in a binary logistic regression model together with the volume of phlebotomy losses and the treatment with EPO.
showed that of 8109 TB patients who were assessed for DM, 1084 (13%) were found to have DM, based on fasting blood glucose (FBG) measurements. Based on these data, a policy decision was made by India's Revised National TB Control Programme (RNTCP) to implement countrywide screening of TB patients for DM. 11 One limitation of this study, however, was that it published only aggregate data from all sites, and may have missed site-specifi c variations, and other socio-demographic and clinical factors affecting the effectiveness and feasibility of the screening programme. We therefore analysed individual patient data and described the effectiveness of screening all TB patients for DM in one tribal TU. The tribal area is remote and diffi cult to access due to poor connectivity and lack of other basic infrastructure.
Data variables, collection and validation The data variables relating to the study objectives were sourced from the TB register and an additional TB-DM register that was developed and used to record data from the screening questionnaire used for the purpose of the pilot study. These data were extracted to pretested, structured collection sheets, which were checked for completeness and consistency by TB laboratory supervisors of the programme once a week, and by the principal investigator once a fortnight. All staff involved in data collection, data validation and data entry were trained in performing the respective procedures using the study protocol and data collection sheets.
Data entry, analysis and reporting The data were double-entered by two data entry operators into a pre-designed data entry form using EpiData software, Version 3. 1 (EpiData Association, Odense, Denmark), with inbuilt checks to minimise data entry errors. Both databases were compared and discrepancies resolved by referring to the original data collection sheets. All analyses were done using EpiData analysis software, Version 2. 2. 2. 180. A descriptive analysis was performed to determine DM prevalence. Comparisons were then made between TB patients with and without DM using the χ² test. Levels of signifi cance were set at 5%. The NNS to diagnose an additional case of DM is the reciprocal of the proportion of newly detected DM cases. We adhered to STROBE guidelines for reporting observational studies in writing this manuscript.
Public Health Action TB-DM screening in a tribal TU, India S45 Characteristics associated with DM are shown in Table  2. Age >40 years was the only factor signifi cantly associated with a higher prevalence of DM, and no differences were observed with respect to sex, smoking status, human immunodefi ciency virus (HIV) status or type of TB. Of the 19 TB-DM cases, 12 were new smear-positive TB cases, 4 new smear-negative cases, 2 new extrapulmonary cases and one was a previously treated extra-pulmonary case (data not shown). The NNS to detect a new case of DM is shown in Table  3. The NNS was 31 for all patients; among those aged ⩾40 years, it was 20, and among smokers it was 21.
SIMPLIFIED MODELS FOR EXOTIC DOUBLY-CHARGED STATES Following the approach of the LHC New Physics Working Group  [71] , specific final state topologies are described by means of dedicated simplified models. They consist of minimal extensions of the Standard Model where the number of new states and operators is maximally reduced. Moreover, the model parameters are translated in terms of relevant products of cross sections and branching ratios so that LHC data can be easily reinterpreted in terms of constraints on these quantities. In this work, we construct a set of simplified models describing all the mechanisms yielding the production of doubly-charged particles, followed by their subsequent decays into pairs of charged leptons (possibly together with additional neutral states when relevant) with the same electric charges.
For this, the parton-level events as generated by Madgraph 5 have been showered and hadronized by means of the Pythia 6 package  [99] , tau lepton decays being handled by using the Tauola program  [100]. We start our analysis by preselecting events after imposing a set of basic selections ensuring that most (nonsimulated) background contributions are well under control. • We start by removing from the event final states all charged leptons not having a transverse momentum p T ≥ 10 GeV and a pseudorapidity satisfying |η| ≤ 2. 5. • Jets are reconstructed by means of the anti-k t algorithm  [101]  as implemented in FastJet  [102, 103]  after we set the radius parameter to R = 0. 4. We only consider jet candidates with p T ≥ 20 GeV and |η| ≤ 2. 5 which are not too close to an electron, i. e. , which lie outside a cone of radius R = 0.
Database ICE-PCS has been described previously  [11, 12]. Briefly, participating members from 64 sites in 28 countries reported patients prospectively on a standard case report form from June 2000 through to September 2006. The case report form included 275 variables and was developed by ICE collaborators according to standard definitions  [11]. All patients were included from sites that met performance criteria for participation. These criteria include the following: minimum enrolment of 12 patients per year in a centre with access to cardiac surgery; the presence of patient identification measures to ensure consecutive enrolment and to minimise ascertainment bias  [11] ; high quality data with query resolution.
Multiclass sample classification and marker selection are cutting-edge problems in metabolomics. In the present study we address the classification of 14 raspberry cultivars having different levels of gray mold (Botrytis cinerea) susceptibility. We characterized raspberry cultivars by two headspace analysis methods, namely solid-phase microextraction/gas chromatography-mass spectrometry (SPME/GC-MS) and proton transfer reaction-mass spectrometry (PTR-MS). Given the high number of classes, advanced data mining methods are necessary. Random Forest (RF), Penalized Discriminant Analysis (PDA), Discriminant Partial Least Squares (dPLS) and Support Vector Machine (SVM) have been employed for cultivar classification and Random Forest -Recursive Feature Elimination (RF-RFE) have been used to perform feature selection.
Multiclass sample classification and "explanatory" variable selection are cutting-edge problems in metabolomics. Often metabolomic studies concentrate on two-class problems, considering for instance treated and non-treated samples. In the vision of integration with genomic and transcriptomic studies for the investigation of gene functions, metabolomic approaches should deal with metabolite differences within a specie by considering populations of individuals. A possible first approach could be the evaluation of different cultivars of a single species. It is therefore important to develop and test methodologies for addressing multiclass problems in this context. Typical datasets from metabolomic experiments have a number of variables considerably exceeding the number of measured samples. This poses a serious limitation to the strategy that may be used for sample classification or response prediction.
Such techniques, on one hand, allow screening a broader number of samples and, on the other hand, minimize the potential artefacts due to the extraction and concentration procedures  (Han et al. , 2009). Moreover, machine learning methods on such datasets are often more robust given the larger number of measured samples  (Han et al. , 2009). Here we employ two different headspace techniques: on the one hand the well-established GC-MS, on the other hand an innovative, rapid and noninvasive (no sample treatment) methodology that allows VOC fingerprinting and measurement repetitions during product shelf life. Proton transfer reaction -mass spectrometry (PTR-MS) is a hyphenated technique that was developed about two decades ago by Lindinger and co-workers  (Lindinger, Hansel, & Jordan, 1998). Already in its original versions it was equipped with a quadrupole mass analyzer, having about unit mass resolution.
GC-MS and PTR-MS analysis of raspberry cultivars This study is a derivative work following our recent study on GC-MS profiling of raspberry cultivars  (Aprea et al. , 2010). We therefore refer to  (Aprea et al. , 2010)  for a detailed description of samples and give only a brief summary here. Fruits were produced under standard conditions  (Aprea et al. , 2010)  and collected from the Edmund Mach Foundation experimental orchard located in Vigolo Vattaro (Trentino, Italy). In order to take into account possible variability two different seasons  (2006 and 2007)  were considered, and three batches for each of the 14 cultivars were collected on three different days in each year. The actual number of measured samples depends on the analytical technique. The GC-MS dataset obtained for the 14 cultivars has extensively been described in  (Aprea et al.
, 2010). Briefly, ripe berries (a batch of about 250 g per each variety) were harvested manually, placed in plastic container and immediately transported to the laboratory, in ice packs, where samples were stored at 4 °C for 1 day before analyses. From 4 to 5 fruits (about 20 g) per each variety where grouped together and used for GC analysis, obtaining six data points for GC dataset (one data point X three different sampling days X two years) per each variety. When.
A C C E P T E D M A N U S C R I P T enough material was available more data points where added. From the same batch, three to six fruits (according to availability) per each variety were sampled and individually measured by PTR-MS, obtaining at least 20 data points for PTR-MS dataset (data points X three different sampling days X two years) per each variety. Only for Josephine variety we collected 15 points (over the two years) being the available material scarcer. The SPME/GC-MS analysis procedure has already been described elsewhere  (Aprea et al. , 2009). PTR-MS measurements were conducted using a high sensitivity PTR-MS manufactured by Ionicon (IONICON Analytik GmbH, Innsbruck, Austria). The conditions in the drift tube were 2. 04 mbar pressure, 520 V drift tube voltage and 50 °C temperature, corresponding to a E/N of about 120 Td.
Statistical analysis We analyzed two datasets. The GC-MS dataset consisted of 94 rows, corresponding to the measured samples, and 45 columns, each corresponding to an identified compound. The PTR-MS dataset consisted of 358 rows (samples) and 141 columns, each corresponding to the normalized intensity of a PTR peak. The datasets were built to take into account the intraseasoning (three different day of sampling spanned over six weeks, see  Aprea et al. 2009  for more details) and inter-seasoning (two years,  Aprea et al. 2010 ) variability of the fruits, in order to build more robust classification models. Supervised classification models were built using RF, PDA, dPLS and SVM on both GC-MS and PTR-MS datasets. All methods were described in previous works on PTR-MS fingerprints analysis  (Granitto et al.
, 2007). In all cases, we used implementations available as free packages for the R statistical environment software (R Development Core Team, 2009). To evaluate the results of the classification methods we used a leave-one-out (LOO) procedure: we iterated the process of leaving a sample out as test set and using the remaining of the data set to fit the models. The free parameters of each classifier, such as the C constant of SVM or the number of dimensions considered in dPLS, were selected at this step by internal cross validation using only the training data set. After that, those models were used to individually classify the sample in the independent test batch. We analysed the classification results using confusion matrices, in which rows correspond to the true classes and columns to the predicted ones.
Classification of raspberry cultivars In the present section we aim at presenting the raspberry cultivar multiclass problems addressed by the selected data analysis methods on both datasets. The confusion matrix reported in Table  2 provides   come as a surprise that the model is not able to distinguish between them and can be seen as a sort of validation of the method. Caroline and Heritage are confused in about 17% of the cases. In fact, Caroline is genetically close to Heritage, being a crossing between Heritage and Geo-1  (Aprea et al. 2010 ). The discriminate model is not able to correctly classify the Josephine samples. Moreover, a marked confusion with Autumn Bliss is evident. Autumn Bliss has a complex parentage including Rubus strigosis, R. arcticus, R. occidentalis, and 6 red raspberry varieties (US Plant Patent 6597).
Botrytis susceptibility The basic question addressed in this paragraph is whether moving from GC-MS profiling of the selected raspberry cultivars to their PTR-MS fingerprints leads to a cultivar characterization which is still related to the corresponding gray mold susceptibility level; or whether, at the contrary, it leads to a loss of information that disrupts that relationship. We recall that in the case of the PTR-MS dataset a larger number of samples per each raspberry cultivar have been considered, given the highly reduced measurement time required by PTR-MS compared to GC-MS. We first apply a multiclass analysis approach to both GC-MS and PTR-MS data in order to grade the samples into classes of equal susceptibility level. Table  6  and 7 report the confusion matrices for RF models in the case of GC-MS and PTR-MS data, respectively.
The corresponding average prediction errors are 0. 21 and 0. 19, meaning that in both datasets the correct Botrytis susceptibility level is assigned by the model to about 80 % of the samples. A comparison between the performances of the various classification methods is reported in Table  8. In general RF is found to reliably provide good results. In fact, RF (prediction error 0. 19) outperforms all other methods in the case of the PTR-MS, the other methods showing poorer results. For the GC-MS dataset RF (prediction error 0. 21), PDA (0. 20) and SVM (0. 22) display similar performances, while PLS gives a higher error (0. 27). As this is a A C C E P T E D M A N U S C R I P T grading problem, it is important to consider also the type of error produced by the diverse classifiers. Beside classification error, Table  8  also reports the fraction of samples that are assigned to a class distant 1 level (denoted as "1 Level") from the correct one or more than 1 level (denoted as "> 1 Level").
Interestingly, the results are rather different for the two datasets. In fact PTR-MS not only produces better results in general, i. e. lower prediction errors, but also the confusion is more related to neighbour classes than in the case of GC-MS data. Again, this fact could be probably attributed to the larger number of samples considered for PTR-MS investigations. In conclusion both PTR-MS and GC-MS are suitable to addressed the presented grading problem and when experiment time is an issue PTR-MS should be preferred. Variable selection was performed in order to unveil which variables are more important for dissecting gray mold susceptibility in the selected raspberry cultivars. Table  9  reports variable selection results for RF-RFE applied to the GC-MS dataset. The four most relevant variables, selected in between 80 % and 100 % of the LOO experiments, are phellandrene, p-cymene, 4-terpineol, dehydro--ionone.