dataesr/scientific-paragraphs-categorization huggingface dataset:
The resulting cDNA was diluted 1:80 and was subjected to quantitative PCR using primer sets specific for the gene or control gene at an optimized oligonucleotide concentration of 200 nM. Forward and reverse specific primer sequences for eNOS and GAPDH were as follows: forward, 5Ј-CTCATGGGCACGGTGATG-3Ј and reverse, 5Ј-ACCACGTCATACTCATCCATACAC-3Ј; forward, 5Ј-GGT-GGTCTCCTCTGACTTCAACA-3Ј and reverse, 5Ј-GTTGC-TGTAGCCAAATTCGTTGT-3Ј, respectively. Reactions were performed in a total volume of 20 l using the Full Velocity hot start SYBR Green Quantitative PCR Mastermix according to the manufacturer's instructions (Stratagene). Amplification cycles were as follows: one denaturation cycle of 5 min at 95 °C, followed by 40 amplification cycles of 10 s at 95 °C and 30 s at 60 °C. Amplification, data acquisition, and analysis were performed using the Mx3000P instrument and the Mx300P software (version 2.
In the present study, we also showed that soluble CD146 acts as a hetero- philic ligand binding to angiomotin. Altogether, these results suggest that both soluble and membrane CD146 are able to bind different proteins as a function of the cellular and physiopathological context but that heterophilic interactions are favored. Of interest, we found angiomotin mRNA expression (data not shown) not only in endothelial cells as HUVEC and EPC but also in the trophoblastic cell line HTR-8/SVneo, in L929 fibroblastic cells and in the monocytic cell line THP1, which does not express membrane CD146 but was activated by sCD146 during transmigration (4). Identifying angiomotin expressing cells will presumably provide new target cells for sCD146. Using antibody array, we examined the signaling cascade elicited by angiomotin following sCD146 binding.
Data collection Data collection and quality control measures were processed at each site, and standard operating procedures were developed as previously described. [9, 10]  A standardized case report form was used to collect data after consent was provided. Patients were followed throughout their hospitalization and, once the outcome was known, the completed form was submitted to the data entry team at each site. Data were double entered. The data collection system combined some data scrubbing tools to systematically examine data for flaws. An error file and queries were created for data outside predefined ranges. All discrepancies and queries were resolved manually by the clerks and data managers at each site; then the data from each site were to the data coordinator, who was responsible for maintaining the pooled database for the network and for ensuring its security.
In the second step site specific model were analyzed using the same variables and outcome measures. To validate our results, we have estimated the power of each logistic regression by running in G*POWER 3. 1. 5 package (Franz Faul, Universita ¨t Kiel, Germany), the post-hoc power calculation for beta-coefficents/odds ratios in logistic regression models for early, middle and late death by site with covariates. Statistical analysis was performed using Stata, version 12 (Stata Corp, College station, Tex). Information about a variable was ''missing'' if the case record did not mention the variable and was ''unknown'' if the case record mentioned that the variable was not tested for. Analyses resulting in values of P,0?05 were considered significant. All reported p-values are twotailed.
Definition From 1997 to 2007, data from six French transplant centers were retrospectively reviewed to identify patients presenting with HA rupture following LT. HA rupture was defined as a severe hemorrhage from the trunk or from a main branch of the HA resulting in disruption of the graft's arterial blood supply. Patients with a single lesion of HA tributaries without interruption of the hepatic arterial axis (i. e. donor splenic artery stump or recipient gastroduodenal artery), and patients with an intrahepatic pseudoaneurysm were excluded.
Data collection and statistical methods A survey was sent to all centers including demographic data, clinical presentation, mechanism of the rupture, associated pathological findings, Data were recorded in a specific Microsoft Access R database (Microsoft France, Issy-les-Moulineaux) and statistical analyses were performed using the SPSS R software, version 13 (SPSS Inc. , Chicago, IL, USA). Quantitative data were expressed using a median (range). Comparison of continuous variables between groups was made using the Mann-Whitney U-test. Kaplan-Meier actuarial analyses of patient and graft survivals were performed. Differences between Kaplan-Meier survival estimates in patients with and without HA ligation were tested by using the log-rank test. A p-value <0. 05 was considered statistically significant.
Modelling limnimeters Typically, in sewer networks, sewage level is measured instead of flow. There are two reasons that can explain this fact. First since the level is measured using ultrasonic waves, limnimeters do not have contact with the sewage flow (Fig. 1 ), and consequently, the required maintenance is cheaper. Second, limnimeters are cheaper than flowmeters. Ultrasonic limnimeters generates an acoustic pulse that is transmitted from the transducer and then it is reflected back from the surface of the liquid. The transit time is then converted into the current output, which is directly proportional to the fluid level. From level measurements, the flow in a sewer can be estimated assuming steady-uniform flow and using the Manning formula calibrated using experimental data  (Duchesne, 2001).
Introduction The telemetry system of Barcelona sewer network contains 22 rain gauges that are connected to the CLABSA control centre and provide the rain intensity every 5 minutes. Figure  13  presents the location of those rain-gauges on the Barcelona map. Each rain gauge is represented by a small square and a name (black squares correspond to the rain-gauges used in the test catchment presented in Fig. 7 , while red squares are the rest of rain-gauges of the sewer network). Spatial models for raingauges are derived from the correlation analysis (based on the computation of the correlation matrix) between all the existent rain-gauges in the telemetry system. This allows deriving which are the most correlated rain-gauges with a given rain-gauge under test. These correlation analyses have been applied not only to a particular rain scenario but to a 48 rain scenarios in a 5 546 year rain-data records.
It can be noticed that P 9 will be proposed as the candidate faulty rain-gauge according to (21). Since rain-gauge models are static, factortime and factororder are not used in the rain-gauge fault isolation. In this paper, fault detection and isolation in rain gauges and limnimeters of Barcelona's urban sewer system is presented. The proposed FDI strategy is based on building a linear interval model for every instrument. Then, each instrument is tested according the prediction provided by its interval model obtained from real data using the parity equation approach. While, the real measurement is inside the interval of predicted behaviour no fault can be indicated. However, when the measurement is outside its envelope a fault can be indicated. Once the fault has been detected, a fault isolation procedure is initiated in order to isolate the faulty instrument.
The sign function is defined as in MATLAB, i. e. , ( ) 1 sign x  if 0 x  , ( ) 1 sign x   if 0 x  and ( ) 0 sign x  if 0 x . FSMsensit: Evaluation of fault sensitivities. This evaluation component uses the fault signal activation values ,max i  from the memory table and computes factorsensit using the sensitivity-based FSMsensit table for weighting the activation values. That approach can be found as well in the DMP-method  (Petti, et al. , 1990). The following equations describe how to calculate the entries FSMsensit ji ( ) 0 ( )) ( ) 0 ( ( ) ij o i o i ij ij o i o i S if r k r k sensit S if r k r k             FSM ( 24 ) with the sensitivity defined as o i ij j r S f   . Although, the sensitivity depends on time in case of a dynamic system, here the steady-state value after a fault occurrence is considered as it was also suggested in  (Gertler, 1998).
ACKNOWLEDGMENTS The authors want to thank to all the members of SAC group actively involved in this research line as  Joseba Quevedo ,  Jaume Figueras ,  Teresa Escobet ,  Belarmino Pulido  and  Jordi Meseguer. Without their help, this research has not gone so far. The authors want also to thank the people of CLABSA company that during the same period has cooperated actively to do this research possible:  Joaquim Martí ,  María Salamero ,  Gustavo Ramon Wilhelmi ,  Blanca Aznar  and  Jordi Cabot.
Rapidity distributions are presented for events containing either a Z boson or a photon with a single jet in proton-proton collisions produced at the CERN LHC. The data, collected with the CMS detector at √ s = 7 TeV, correspond to an integrated luminosity of 5. 0 fb -1. The individual rapidity distributions of the boson and the jet are consistent within 5% with expectations from perturbative QCD. However, QCD predictions for the sum and the difference in rapidities of the two final-state objects show discrepancies with CMS data. In particular, next-to-leading-order QCD calculations, and two common Monte Carlo event generators using different methods to match matrix-element partons with parton showers, appear inconsistent with the data as well as with each other.
The rapidities y sum and y dif are effectively rotations in phase space of the y V and y jet system that yield two approximately uncorrelated quantities. The distribution in y sum depends mainly on the parton distribution functions (PDF), while the distribution in y dif reflects the leading order (LO) partonic differential cross section. Distributions in the y sum and y dif quantities were measured previously at √ s = 1. 96 TeV by the D0 Collaboration  [5]. Related angular quantities in V + jet events have been measured at the LHC by CMS  [6]  and ATLAS  [7] [8] [9] [10]. In this Letter, we compare theoretical predictions for normalized distributions in |y V |, |y jet |, y sum , and y dif with data collected by the Compact Muon Solenoid (CMS) experiment. The kinematic properties of Z + jets events at the Tevatron  [5]  were found to be well-described by the NLO MCFM program  [11, 12].
For Z + jets production at the LHC, MCFM provides predictions together with estimates of their uncertainties. For γ + jet events, the program developed by Owens  [3]  is used for NLO predictions and their uncertainties. This calculation employs fragmentation functions  [4]  to parametrize small-angle photon emission. Previous studies of the transverse momentum (p T ) distributions of photons showed agreement between LHC data and a variety of QCD predictions  [13] [14] [15] [16]. In all MCFM calculations, both the renormalization (µ R ) and factorization (µ F ) scales are set to the invariant mass of the lepton pair. For NLO prompt photon calculations, the scales are set to the p T of the photon. Programs that use matrix-element descriptions of jet systems are accurate only when the partons have large transverse momentum or are well separated, while event generators using parton showers perform well in describing soft and small-angle radiation  [17].
We also compare predictions from the two hybrid event generators with our V + jet data using MADGRAPH 5. 1. 1. 0 [21] , which implements the MLM scheme, and SHERPA 1. 3. 1  [22] , which uses the CKKW method. For MADGRAPH, matching scales are chosen to be 20 GeV for Z bosons and 9-12 GeV for photons. The µ R and µ F scales are set to m 2 Z + Σ jets p 2 T and p γ T 2 + Σ jets p 2 T for Z bosons and photons, respectively, where m Z is the mass of the Z boson and p γ T is the photon transverse momentum. The PYTHIA 6. 4. 24 event generator is used for parton showers and hadronization  [23]. For the SHERPA events, the matching scales are 20 and 10 GeV for Z bosons and photons, respectively. The µ F and µ R scales are set to m Z and p γ T for Z bosons and photons, respectively. The parton-shower module APACIC++ 2.
In addition to the barrel and endcap detectors, CMS has extensive forward calorimetry. A more detailed description of CMS is given in Ref. [28]. A right-handed coordinate system is defined in CMS, with the origin at the center of the detector, the x axis pointing to the center of the LHC ring, and the y axis perpendicular to the plane of the LHC. The polar angle θ is measured from the positive z axis and the azimuthal angle φ is measured in the x-y plane in radians. Pseudorapidity, which is given by η =ln[tan(θ/2)], is used for specifying acceptance requirements. The data were collected during 2011 at a pp center-of-mass energy of 7 TeV, corresponding to an integrated luminosity of 5. 0 ± 0. 1 fb -1  [29]. Because of limitations in data handling, the triggers used for photon candidates had to be partially suppressed, and the effective luminosity for prompt-photon production was 4.
2 and 2. 2 GeV, respectively. The background resulting from fragmentation of jets into collimated neutral mesons that mimic a photon is estimated using the "matrix" method of Ref. [38]. The transverse spatial distribution of the energy in a cluster is used as a discriminant. Templates for the spatial transverse distributions of photon showers are taken from PYTHIA events, and reconstructed through the full CMS detector simulation via GEANT4  [39]. The templates for background are obtained from data using events passing all selection requirements, but with less stringent charged-particle isolation criteria (set between 2 and 5 GeV). Following the selection requirements, the fraction of photons in the sample is determined individually for each bin in y dif. The photon fraction decreases from (61 ± 2)% at y dif = 0 to (36 ± 5)% at y dif = 1.
Photon efficiencies are obtained from simulation, and rescaled using the measured electron efficiency, which is assumed to have the same η dependence as photons. All rapidity distributions are corrected for the effects of detector resolution using simulated events in an iterative unfolding method  [40] , as implemented in the ROOUNFOLD package  [41]. For all rapidity variables, the size of the correction is smaller than 1%. Only the distribution of |y jet | has significant bin migration due to effects of resolution. The other variables have a correction factor consistent with unity. The sources and relative experimental uncertainties in the rapidity distributions for the largest y values of binned rapidity for the three analyses are shown in Table  1. The contributions include the uncertainty in jet energy scale, unfolding of rapidity distributions, and the scaling Table 1: Summary of the relative experimental uncertainties in the yields for the bins of largest |y|.
For Z + jet and γ + jet events, the differences in the normalized distributions as a function of y sum , |y jet |, and |y V | are <2%, while the change in y dif is ≈8%. Moreover, the changes are similar for both LO and NLO calculations, although the normalization factor can be quite different. The NLO calculations do not include effects from final-state photon radiation, parton showering, and hadronization, since these are estimated as negligible using the PYTHIA program. The normalized rapidity distributions for |y Z |, |y jet |, y dif , and y sum , along with predictions from theory, are shown in Fig. 1  for Z + jet events. The data for the |y Z | and |y jet | distributions agree to better than 5% accuracy with SHERPA, MADGRAPH, and MCFM over the full range of the measurement. The SHERPA simulation reproduces the features of the y sum distribution better than the MADGRAPH or MCFM programs.
0, and is best described by hybrid calculations that employ NLO PDF. The distribution in the difference in rapidities (y dif ) is described to better than 10% by the MCFM prediction. The two hybrid event generators differ by as much as ≈50% in their predictions for distributions in y dif , but SHERPA is significantly closer to data than MADGRAPH. Nevertheless, SHERPA overestimates the cross section at higher y dif. We attribute the difference between the hybrid event generator distributions to the respective methods by which partons from matrix elements are matched to parton showers. The authors thank J. Campbell, R. Frederix, and J. Owens for their substantial contributions to this work. 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.
Sound localization As expected, sound localization performance decreased with decreasing SNR (repeated measure ANOVA F(6,108) ¼ 206. 16; p < 0. 001) (Fig. 2 ). Sound localization errors occurred mainly in the front/back and/or up/down dimensions; the percent-correct localization score for the left/right dimension (also represented in Fig. 2 ) was relatively high up to the most adverse SNR. Localization psychometric curves were fitted to the percent correct scores (across all dimensions) across conditions. For each listener, the sound localization threshold (SLT) was determined by the SNR yielding a performance halfway between chance (12. 5%) and 100%, i. e. (56. 25%). Information concerning the mathematical function used for data fitting can be found in a previous study  (Andeol et al. , 2011).
Acknowledgments This work was supported in part by the  French Procurement Agency (DGA). Author EM acknowledges funding from the  National Science Foundation (USA)  and the  Natural Sciences and Engineering Research Council of Canada. Author AS acknowledges funding from  National Institutes of Health (USA)  Grant  F31DC9549. We thank  Jean Christophe Bouy  for help with software development,  Lionel Pellieux  for HRTF measurements and signal processing manipulations and  Brian C. J. Moore , and two anonymous reviewers for many helpful comments.
6; however, as long as λ remains sizeable and tan β small, the masses and mixing angles depend little on these specific values. Masses and mixing angles for additional points in this plane can be provided by the author, or be obtained with the help of the public code NMSSMTools  [59, 60]. The present constraints in the M χ 0 1 -M χ ± 1 plane from the ATLAS search for trileptons  [16]  (assuming heavy sleptons) have been obtained using the simplified detector simulation DELPHES  [66, 67] ; this simulation should actually be redone by the LHC collaborations themselves. In any case it shows that the signal rates in this scenario are measurably large, and that relevant constraints are obtained in the absence of signals. In addition to the trilepton final state, the simplified higgsino-singlino scenario can also be tested in searches for four leptons plus E miss T  [17, 70].
Methods Actual aboveground biomass, grain yields (Y a ) and grain yield components were measured over two to three agricultural seasons in selected study areas in Brazil, Senegal and Vietnam. Yields were measured in a network of plots delineated within farmers' fields, covering the diversity of soils and management techniques. Potential (Y p ) and water-limited (Y w ) yields were calculated for each plot using a simple dynamic crop simulation model calibrated through an approach combining parameters retrieved from the literature and the use of observed data sets. This allowed us to calculate for each plot the absolute yield gap (Y w -Y a ), the relative yield gap (Y w -Y a )/Y w and the relative yield Y a /Y w , and to study their distribution within and across sites.
Model used We opted for building an ad hoc crop growth model as recommended by  Sinclair and Seligman (1996, 2000). Our aim hereby was to keep the model as simple as possible, not overloaded with unnecessary details, with minimum data requirements and with the re-use of existing model components, and to facilitate virtual experiments based on principles of interfacing between models and databases  (Affholder et al. , 2012). The model built, PYE (potential yield estimator, http://ur-sca. cirad. fr/produits-et-services), was written in VBasic under Microsoft Access. It simulates potential crop growth and yield as defined by van Ittersum and Rabbinge (1997) i. e. , under idealised conditions where the crop is maintained free of any growth limitation other than temperature, solar radiation, and rainfall in case of rainfed crops, and considering a standard stand density.
The maximum maize grain yields were achieved with threshold seasonal rainfalls of around 1500-1600 mm both in Brazil and Vietnam while maximum grain yields of pearl millet were obtained with around 600 mm in Senegal. Biomass and grain yields of upland rice in Vietnam were less sensitive to seasonal rainfall, presumably due to strong yield limitations by other factors, such as nutrients or biotic constraints. The dashed line in Fig. 4a  and b  was fitted to the entire dataset, all crops and locations confounded, and provides a good estimation of the water-limited yield that may be attained across these tropical environments with current technology and resources. In particular, the difference in maize productivity between Brazil (one cropping cycle per agricultural season) and Vietnam (two cropping cycles per agricultural season) may be to a large extent attributable to the effects of agronomic practices, genetic material and inputs.
It is evident that the quality of these aboveground biomass yield potential estimates is directly determined by the accuracy of the temperature and solar radiation records. In our case studies, weather recordings were done with accurate and well-located automated weather stations. However, in many tropical regions reliable meteorological data are missing because the geographic coverage of weather stations is incomplete or sometimes even non-existent. Assembled global meteorological databases using satellite imagery such as the database developed by NASA-Power (http://power. larc. nasa. gov/index. php) are a promising alternative source of continuous daily weather data, provided that they are properly evaluated for the region where yield potentials are to be estimated  (White et al. , 2008).
We assumed zero runoff in our estimates of waterlimited aboveground biomass and grain yield, thus considering runoff among the factors that are manageable by farmers. This is only partially true, since in some cases the investment necessary to fully control runoff may be economically unjustifiable. On the other hand, defining a representative level of runoff for a given region seems difficult and would be somehow arbitrary. Soil water holding capacity is far less subject to modifications by management practices, but is highly variable across landscapes. Therefore its spatial distribution is key information required when assessing yield potentials under rainfed conditions in a given region. However, few databases, especially for the tropics, incorporate this soil property. The high variability of water-limited yields of maize in Brazil and Vietnam and of millet in Senegal (Fig.
Acknowledgements The data from Vietnam were collected within the framework of a joint project of  CIRAD (Centre de Coopération Internationale en Recherche Agronomique pour le Développement, France)  with  IRRI  ( International Rice Research Institute, Philippines ) and  VAAS-NOMAFSI (Vietnam Academy of Agricultural Science-Northern Mountainous Agriculture and Forestry Science Institute, Vietnam). The data from Brazil were collected with support from  EMBRAPA  ( Empresa Brasileira de Pesquisa Agropecuaria, Brazil ). The data from Senegal were collected with support from  ISRA (Institut Sénégalais de Recherche Agronomique, Senegal). We thank  Jacques Wery  ( Montpellier Supagro , France) and  Pascal Clouvel  ( CIRAD, France ) for their encouraging discussions on the initial version of this paper.