dataesr/scientific-paragraphs-categorization huggingface dataset:
placebo, then 6 mg/kg vs. placebo, and finally 12 mg/kg vs. placebo), resulting in similar numbers of patients randomized to the four study arms (placebo, CER-001 3, CER-001 6, and CER-001 12 mg/kg). The randomization code used blocks of size 4 and was stratified according to site and cohort, and was generated by the Montreal Heart Institute Coordinating Center using the SAS procedure PROCPLAN and managed centrally via an interactive response system to ensure an ascending dose study design. Prior to randomization, a baseline IVUS examination of the designated target coronary artery was performed and transferred to the Montreal Heart Institute core IVUS laboratory for a quality assessment. The proximal 4 cm of the target coronary artery in which IVUS was performed at baseline needed to have a reference diameter of 2.
5 mm or more, be free of filling defects suggestive of thrombosis, not to have. 50% reduction in lumen diameter by visual angiographic estimation at baseline, and not to have undergone previous PCI nor be a candidate for intervention at the time of the baseline catheterization or over the following 12 weeks. Three weeks after the last study infusion (2-5 week window), a follow-up IVUS examination was performed in the same segment of the target artery studied at baseline. A follow-up visit occurred 6 months after the last dose of study medication to monitor for major adverse cardiovascular events and anti-apoA-I antibodies. Patient safety was monitored throughout the trial. Clinical blood laboratory data were evaluated on an ongoing basis throughout the study. An independent, unblinded safety monitoring committee met at intervals during the trial (including near the end of the recruitment of the first two cohorts to authorize dose escalation).
Assuming that the standard deviation of the change in the total atheroma volume would be 15 mm 3 in both groups and using a two-sided 0. 05 significance level, 98 subjects per treatment group were necessary to detect this difference with 90% power. To account for an attrition rate of 20 -25%, 126 subjects per group were to be randomized for a total of 504 patients. Statistical analyses were performed using SAS version 9. 3.
We normalize the NLL+NLO predictions to the rate obtained when using the best NLO PDF fit provided by the CTEQ collaboration and include the uncertainties as defined above for the CT10, MSTW 2008 and NNPDF 2. 3 fits. We observe that up to these uncertainties, all predictions agree over the covered slepton mass range at the percent level. However, while the theoretical errors associated with the production of a pair of light sleptons are of a few percents, they increase with the slepton mass, reaching about 10% for 500 GeV sleptons. This is due to the relevant regions of the (x, µ 2 F ) parameter space that lead to PDFs less constrained by data when the slepton mass increases.
[68]  and the built-in ATLAS detector setup. The simplified scenarios that have been designed in Section 2 have been implemented in MadGraph 5 via the UFO interface  [69]  of FeynRules  [70] [71] [72] [73] [74]    generated events are reweighted according to the NLL+NLO predictions as computed by Resummino  [39]. Event analysis is finally performed by means of the MadAnalysis 5 program  [75, 76] , after having reconstructed the jets with the anti-k T algorithm (using a radius parameter set to R = 0. 5)  [77]  as implemented in the FastJet package  [78]. By the time this work was being completed, additional simplified models with different slepton/neutralino definitions have been investigated by both the ATLAS and CMS collaborations  [11, 16] , extending the earlier analyses of Refs. [7, 15].
In addition, mean particle densities are presented as functions of transverse momentum, dn/dp T , and of pseudorapidity, dn/dη. The paper is organised as follows. In Sect. 2 a brief description of the LHCb detector and an overview of track reconstruction algorithms are provided. The recorded data set and Monte Carlo simulations are described in Sect. 3, followed by a discussion of the definition of visible event and the data selection in Sect. 4. The analysis method is described in Sect. 5, and systematic uncertainties are given in Sect. 6. The final results are compared to event generator predictions in Sects. 7 and 8, before summarising in Sect. 9.
Data set and simulation The measurements are performed using a minimum-bias data sample of pp collisions at a centre-of-mass energy of √ s =7 TeV collected during 2010. In this low-luminosity running period, the average number of interactions in the detector acceptance per recorded bunch crossing was less than 0. 1. The contribution from bunch crossings with more than one collision (pile-up events) is determined to be less than 4 % and is considered as a correction in the analysis. The data consists of 3 million events recorded in equal proportion for both magnetic field polarities. The low luminosity and interaction rate of the proton beams allowed the LHCb detector to be operated with a simplified trigger scheme. For the minimum-bias data set of this analysis, the hardware stage of the trigger system accepted all events, which were then reconstructed by the higher-level software trigger.
The simulation is used to determine correction factors for the detector acceptance and resolution as well as for quantifying background contributions and reconstruction performance. The measurements are compared to predictions of two classes of generators, those that have not been optimized using LHC data and those that have. The former includes the Perugia 0 and Perugia NOCR  [21]  tunes of Pythia 6, both of which rely on CTEQ5L  [22]  PDFs, and the Phojet event generator  [23]. Phojet describes soft-particle production by relying on the dual-parton model  [2] , which comprises semi-hard processes modelled by parton scattering and soft processes modelled by pomeron exchange. Pythia 8  [24]  is available in both classes. An early version of Pythia 8 is represented by version 8. 145. In more recent versions, the default configuration has been changed to Tune 4C, which is based on LHC measurements in the central rapidity region.
Only tracks traversing the full tracking system are considered. The kinematic criteria are explicitly applied to all tracks to restrict the measurement to a kinematic range in which reconstruction efficiency is high. The track reconstruction requires a minimum number of detector hits and a successful track fit. To retain high reconstruction efficiency, no additional quality requirement for suppressing the contribution from misreconstructed tracks is applied. To ensure that tracks originate from the primary interaction, it is required that the smallest distance of the extrapolated track to the beam line is less than 2 mm. The position of the beam line is determined independently for each data taking period from events with reconstructed primary vertices. Additionally, a track is required to originate from the luminous region; the distance z 0 of the track to the centre of this region has to fulfil z 0 < 3σ L , where the width σ L is of the order of 40 mm, determined from a Gaussian fit to the longitudinal position of primary vertices.
The parametrisation model yielding the best χ 2 -value is chosen as the central result, the other models are considered in the systematic uncertainty determination. Both the binned and total event unfolding procedures using simulated data are found to reproduce the generated distributions satisfactorily. The uncertainty of the unfolded distribution is determined through pseudo-experiments. Each pseudo-experiment is generated from the analytical model with the parameters randomly perturbed according to the best fit and the correlation matrix. As a consistency check, a Bayesian unfolding technique  [30]  is used. The unfolded distributions of both methods in all kinematic bins are found to be in agreement. The unfolded distribution for the total event is truncated at a value of 50 particles and the binned distributions at a value of 20 particles.
The descending slopes towards small and large pseudorapidities are also insufficiently modelled. The Perugia NOCR tune shows a slight improvement in shape and in the amount of charged particles; Perugia 0 predicts an even smaller mean particle density over the whole kinematic range. Predictions of the Phojet generator are similar to the tunes of Pythia 6. In this group of predictions, the LHCb tune of Pythia 6 provides the best agreement with the data but still underestimates the charged-particle production rate by 10 -40 %. This behaviour is also observed in the p T dependence, where all configurations underestimate the number of charged particles. The aforementioned generator predictions were optimized without input of LHC measurements. Predictions from the more recent generators Pythia 8 and Herwig++ are shown in Figs.
1b  and 2b. Pythia 8. 145 with default parameters was released without tuning to LHC measurements and is not better than the LHCb tune of Pythia 6. In contrast, Pythia 8. 180, which was optimized on LHC data, describes the measurements significantly better than the previous version. The predictions of Herwig++ are also in reasonably good agreement with data, although the charged-particle production rate is underestimated at small pseudorapidities. The Herwig++ generator version 2. 7. 0, which uses tune UE-5, overestimates the number of prompt charged particles in the low p T range but underestimates it at larger transverse momenta. The predictions of Herwig++ in version 2. 6. 3, which relies on tune UE-4, show a more complete description of the data. Both event generators, Pythia 8 and Herwig++, describe the data over a wide range.
Multiplicity distributions The charged particle multiplicity distribution in the full kinematic range of the analysis is shown in Fig. 3 , compared to the predictions from the event generators. The corresponding mean value, µ, and the root-mean-square deviation, σ, of the distribution, truncated in the range from 1 to 50 particles, is measured to be µ = 11. 304 ± 0. 008 ± 0. 091 and σ = 9. 496±0. 006±0. 021, where the uncertainties are statistical and systematic, respectively. Using the full range gives consistent results with the value obtained from the particle densities. All generators that do not use LHC data input underestimate the multiplicity distributions. In this comparison, the Phojet generator predicts the smallest probabilities to observe a large multiplicity event, being in disagreement with the measurement.
This can be understood since Phojet mostly contains soft scattering events. All Pythia 6 tunes underestimate the charged particle production cross-section significantly. The prediction from the LHCb tune is closest to the data, but the mean value of the distribution is still about 15 % too small. Calculations from more recent generators are in better agreement with the measurement. While Pythia 8. 145 gives the same insufficient description of the data as its predecessor, the prediction of version 8. 180 using Tune 4C shows a reasonable agreement. The Herwig++ event generator using the underlying event tune UE-4 shows good agreement with the measurement and reproduces the data better than the more recent UE-5 tune. Charged particle multiplicity distributions for bins in pseudorapidity are displayed in Figs.
4  and 5  of Phojet and Pythia 6 all underestimate the particle multiplicity. The difference in particle production is most prominent at small η, where the minimum p requirement in this analysis significantly reduces the amount of particles. Even though the LHCb tune is in better agreement with the data, the difference remains large. Recent generator predictions match the data better. Both Pythia 8 and Herwig++ show good agreement with data at larger pseudorapidity, only the range from 2 < η < 3 being still underestimated. Charged particle multiplicities for bins of transverse momentum are shown in Figs. and 7. The LHCb tune describes the data better than the other tunes. It is interesting to note that at large transverse momenta, where the discrepancies are most prominent, Pythia 6. 426 in the default configuration matches the shape of the distribution.
Summary The charged particle multiplicities and the mean particle densities are measured in inclusive pp interactions at a centre-of-mass energy of √ s = 7 TeV with the LHCb detector. The measurement is performed in the kinematic range p > 2 GeV/c, p T > 0. 2 GeV/c and 2. 0 < η < 4. 8, in which at least one charged particle per event is required. By using the full spectrometer information, it is possible to extend the previous LHCb results  [12]  to include momentum dependent measurements. The comparison of data with predictions from several Monte Carlo event generators shows that predictions from recent generators, tuned to LHC measurements in the central rapidity region, are in better agreement than predictions from older generators. While the phenomenology in some kinematic regions is well described by recent Pythia and Herwig++ simulations, the data in the higher p T and small η ranges of the probed kinematic region are still underestimated.
Furthermore, one should note that the quite accurate description of the interference found at the integrated level for NWF < 0. 1 remains true at differential level too. Finally, we remark that the multi-parametric scan was done using CalcHEP3. 4 on the HEPMDB database  [38] , where the model studied here can be found under the http://hepmdb. soton. ac. uk/hepmdb:1113. 0149 link.
Acknowledgments The authors would like to thank  M. Buchkremer ,  G. Cacciapaglia ,  A. Deandrea  and  S. De Curtis  for useful discussions. They also thank  A. Pukhov  for a quick response which helped to improve the interference treatment within CalcHEP. DB, AB, SM and LP are financed in part through the  NExT Institute. DB and LP would like to thank the  Galileo Galilei Institute (GGI) in Florence  for hospitality while part of this work was carried out. JB thanks the  University of Southampton  for the Summer Student programme support. AB and JB acknowledge the use of the HEPMDB and  IRIDIS HPC Facility at the University of Southampton  in the completion of this study.
ASE Extraction Accelerated Solvent Extraction of phenolic compounds was performed using a Dionex ASE 100 extraction system (Dionex, Sunnyvale, CA, USA) at 100 bars pressure. 38 g of wet or dry milled pomace were placed into a 100 mL extraction cell. The ASE variables, static time (incubation time between the solvent and the grape pomace inside the extraction cell) (0 min), flush volume (the volume of solvent to flush through the extraction cell after heating step to the required temperature) (100%), purge time (the time in which the cell is purged with nitrogen) (120 s) and static cycles (number of times in which the heating and flushing processes occurred) (five) were used as basic conditions. The choice of the ASE variables was done based on preliminary studies (Data not shown). Grape pomace is therefore rinsed by the passage of the heated solvent through the extraction cell.
Identification was based on comparing retention times of the peaks detected with those of original compounds, and on UV-Vis online spectral data. Quantification was accomplished using the phenolic standards solutions. Results were expressed as mg/mL of grape pomace extract volume  [45]. All chromatograms were treated using ChromGate (version 2. 8. ) software. All peaks were identified and quantified using the corresponding phenolic compounds as internal standards. Peaks overlapping was taken into consideration by the software during the calculation.
Experimental Design A three-level central face composite design was developed to assess the main impact of two factors: solvent mixture and extraction temperature as well as their interaction on total phenolic compounds yield. The extracts were obtained from Cabernet Sauvignon grape byproducts by Accelerated Solvent Extraction. Temperature values varied between 60 and 140 °C and solvent mixture between 30% and 70% ethanol/water. Both temperature and solvent mixture intervals used in the study were deduced from preliminary trials realized previously. The two independent variables were coded at three levels (-1, 0, 1) resulting in an experimental design of nine experimental points. Considering two parameters and one response, experimental data were fitted to obtain a second order regression equation of the form: Y = β0 + β1T + β2SM + β12T.
SM + β11T2 + β22S2 ( 3 ) where Y is the predicted response parameter, T is the temperature, SM is the solvent mixture, β0 is the mean value of responses at the central point of the experiment; β1 and β2 are the linear coefficients, β11 and β22 the quadratic coefficients and β12 the interaction coefficient. Experimental design and statistical treatment of the results were performed using STATGRAPHICS Plus 4. 0 for Windows. The software was used to generate response surfaces and contour plots.
By using MIRA3 software (http://www. chevreux. org/projects_mira. html), the whole reads (285,647) were initially assembled in 924 contigs, of which only 16 had a length greater than 10 kb. Based on the available sequences of GAS, these contigs were oriented and ordered and finally assembled using CLC Workbench version 6 software (http://www. clcbio. com). The resulting chromosomal scaffold consisted of 14 separated contigs, and the sequence gaps were filled by PCR and sequencing. The final single circular genome of 1,795,608 bp, with an average GϩC content of 38. 5%, was fully annotated using the RAST server (  6 ) and NCBI-PGAP (http://www. ncbi. nlm. nih. gov /genome/annotation_prok/). We identified 1,812 genes (67 tRNA, and 18 rRNA) and 1,358 expected coding sequences (CDSs). Using the PHAge Search Tool (PHAST)  (7)  we identified one putative phage and one intact integrated prophage that harbors the superantigen speH.
The ratio between the dimension-4 vector and the axial-vector couplings of the t → Zq FCNC model is assumed to be SM-like  [23]. In all cases, hadronization and showering are simulated with PYTHIA 6  [24] , while τ decays are simulated with TAUOLA  [25]. The set of parton distribution functions (PDFs) used is CTEQ6L  [26]. The CMS detector response for all MC samples is simulated using a GEANT4-based  [27]  model, and the events are reconstructed and analyzed using the same software used to process collision data. The simulated events are weighted so that the trigger efficiencies, reconstruction efficiencies, and the distribution of reconstructed vertices observed in data are reproduced. The search is performed by looking for tt events where one top quark decays into Zq and the other decays into Wb with both vector bosons decaying leptonically, which provide a very clear signature.
The analysis follows closely the search performed at 7 TeV  [16]. Several of the event selection requirements have been re-optimized before the complete dataset was collected, based on the expected signal and background yields at 8 TeV with B(t → Zq) = 0. 1%. Events are required to pass at least one of the ee or µµ high transverse momenta (p T ) dilepton triggers. Events with two opposite-sign, same-flavor, isolated leptons (e or µ) having an invariant mass between 78 GeV and 102 GeV, consistent with a Z-boson decay, and one extra charged lepton (e or µ) are selected. When there is more than one lepton pair forming a Z candidate, the pair with invariant mass closest to the nominal value is taken. All three leptons must satisfy the following kinematic requirements: p T > 20 GeV and |η| < 2.
5 GeV. The mass requirements are the same as in the 7 TeV analysis  [16]. Figure  2  shows the comparison of the m Zj and m Wb distributions in data and simulation, while Table  1  summarizes the signal efficiencies determined from simulated events. Table  1 : Signal selection efficiency, in percent, for each dilepton channels with respect to events from all channels. The efficiency is calculated as the fraction of events with leptonically (e, µ, τ) decaying W and Z bosons passing the selection. Only MC statistical uncertainties are shown. Events passing the basic event selection, with two jets to be paired with W and Z bosons are divided into three samples: (a) events with no b-tagged jets; (b) events with exactly one btagged jet; and (c) the rest of the events. The numbers of events in those three samples can be related to the yields of the three groups based on the b-tagging efficiencies for b jets, c jets, or other jets, which are measured using data.
Process Estimation from data MC prediction t → Zq (B = 0. 1%) -6. 4 ± 0. 1 ± 1. 3 WZ 1. 4 ± 0. 1 ± 0. 3 0. 9 ± 0. 1 ± 0. 3 ZZ < 0. 1 Drell-Yan < 0. 1 tt 1. 7 ± 0. 8 ± 0. 4 0. 7 +1. 1 -0. 4 ± 1. 2 ttZ 1. 1 ± 0. 1 ± 0. 8 ttW 0. 1 ± 0. 1 ± 0. 1 tbZ 0. 3 ± 0. 1 ± 0. 2 Total background 3. 1 ± 0. 8 ± 0. 8 3. 2 ± 1. 2 ± 1. 5 Observed events 1 - To calculate the expected upper limits, the systematic uncertainties from the dilepton trigger efficiency, lepton selection efficiency  [28] , pileup modeling  [34] , b-jet tagging efficiency  [33] , jet energy scale and missing transverse energy resolution  [35]  are included, with the b-jet tagging efficiency being the dominant one for the background estimation. Additionally, several sources of uncertainties in the signal yield are evaluated: the choice of PDFs, generator parameters, and uncertainty in the tt cross section.
Molecular Density Distributions from Averaged Fluorescence Intensity Profiles To gain insight into how EB1 could influence microtubule growth and catastrophe, we examined the fluorescence profiles of growing microtubule end regions using dual-color TIRF microscopy with subpixel precision. We imaged purified GFP-tagged human EB1 or, for comparison, XMAP215-GFP on growing Cy5-labeled microtubules (Cy5-microtubules). We tracked the growing Cy5-microtubule ends using software that we developed starting from an available MATLAB program  [26]  (see Supplemental Experimental Procedures available online). To improve the signal-to-noise ratio of the data, we averaged hundreds to thousands of images. To achieve this, images of growing individual Cy5-microtubules and images of their associated GFP-labeled end tracking proteins were cropped around the detected microtubule end position with subpixel precision so that the detected end position was in their center (Figures  1A  and S1A ; Movie S1).
Acknowledgments We thank  Per O. Widlund  for XMAP215 expression constructs,  Jeanette Seiler  for insect cell expressions, and  Stefan Diez  and  Rob Cross  for sharing their microtubule tracking and catastrophe time determination programs. This work was supported by  Cancer Research UK. S. P. M. acknowledges support from an  EC Marie Curie fellowship  ( PIEF-GA-2009-253043 ), and T. S. acknowledges support from the  Deutsche Forschungsgemeinschaft.
Dosimetry The absorbed dose in the thyroid (in Gy) was estimated based on the MIRD formalism  [26]. The cumulated activity in the thyroid (Bq. s) was calculated based on quantitative imaging and multiplied by the thyroid self S-value (Gy. Bq 21. s 21 ) obtained from Monte Carlo simulation of a representative mouse model. The kinetics of 99m TcO 4 2 activity in the thyroid are presented in Figure  1B. Activities were not corrected for nuclear decay. Cumulated activity was calculated for each mouse, using a linear fitting model for the uptake phase, and a mono-exponential fitting model for the washout, both implemented in Root software (http://root. cern. ch). No activity was considered at t = 0 and the endpoint for cumulated activity calculation was taken at t = 24 hours. S-values were calculated based on the realistic-digital-mouse (Moby) whole-body phantoms (version 2), representing a 16-weekold male C57BL/6 mouse  [27].