dataesr/scientific-paragraphs-categorization huggingface dataset:
Aortic sinus quantification The lesions were estimated according to Paigen and collaborators  [8]  Briefly, each heart was frozen on a cryostat mount with OCT compound (Tissue-Tek), and stored at -80°C. Hearts were cut using a Leica CM3050S cryostat. Fifty sections of 10-μm thickness were prepared from the top of the left ventricle, where the aortic valves were first visible, up to a position in the aorta where the valve cusps were just disappearing from the field. After drying for 1 hour, the sections were stained with oil red O and counterstained with Mayer's hematoxylin. Five sections out of the 50, each separated by 100 μm, were used for specific morphometric evaluation of intimal lesions using a computerized Leica image analysis system, consisting of a Leica DMRE microscope coupled to a video camera and Leica Qwin Imaging software (Leica Ltd, Cambridge, UK).
Acknowledgments We thank the technical service of the animal facility ( Genotoul Anexplo Platform, Toulouse ). We thank  J. Bertrand-Michel  and  V. Roques  ( MetaToul-Lipidomic Core Facility INSERM UMR 1048, France, part of Toulouse Metatoul Plateform ) and  Eric Lacoste  ( Synelvia, Prologue Biotech, Labège, France ) for lipidomic analysis, advice and technical assistance.
0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. processed through a GEANT4  [19]  simulation of the full ATLAS detector  [20] , and the same reconstruction and analysis software is used for both the data and the MC events. Signal t " t events are simulated by the next-toleading-order (NLO) generator MC@NLO 3. 41  [21]  with the NLO parton distribution function (PDF) set CT10  [22] , assuming a top quark mass of 172. 5 GeV. Parton showering is modeled with HERWIG 6. 510  [23] , and JIMMY 4. 31  [24]  is used for the underlying event. A t " t production cross section of 167 þ17 À18 pb is used, calculated at approximate next-to-next-to-leading order (NNLO) in QCD using HATHOR 1. 2  [25]. Backgrounds are simulated using the MC@NLO, ACERMC  [26] , ALPGEN  [27] , and HERWIG generators, as detailed in Ref.
In this work, we introduce and evaluate a data assimilation framework for gauged and radar altimetrybased discharge and water levels applied to a large scale hydrologic-hydrodynamic model for stream flow forecasts over the Amazon River basin. We used the process-based hydrological model called MGB-IPH coupled with a river hydrodynamic module using a storage model for floodplains. The Ensemble Kalman Filter technique was used to assimilate information from hundreds of gauging and altimetry stations based on ENVISAT satellite data. Model state variables errors were generated by corrupting precipitation forcing, considering log-normally distributed, time and spatially correlated errors. The EnKF performed well when assimilating in situ discharge, by improving model estimates at the assimilation sites (change in root-meansquared error rms = -49 %) and also transferring information to ungauged rivers reaches ( rms = -16 %).
g. Thielen et al. , 2009; Alfieri et al. , 2013)  still do not incorporate advanced data assimilation systems for updating model initial states. Also, the assimilation of discharge and water levels from in situ and remotely sensed observations into regional/global hydrologichydrodynamic models is still uncommon. In this paper, we present the development and evaluation of a data assimilation framework for both gauged and radar altimetry-based discharge and water levels into a large scale hydrologic-hydrodynamic model of the Amazon River basin using the EnKF. We also explore the usefulness of such system to provide streamflow forecasts when forced by past remotely sensed precipitation data and based mostly on model initial conditions. This paper is in the context of recent developments of techniques for integrating information from hydrological models with newly remotely sensed data such as the forthcoming SWOT mission and also in the context of regional/global hydrological forecast systems including large, poorly gauged river basins.
Model implementation We used a MGB-IPH implementation on the Amazon basin developed by  Paiva et al. (2013b) , as briefly described below. The model was forced using meteorological data obtained from the CRU CL 2. 0 dataset  (New et al. , 2002)  and remotely sensed precipitation estimates from the TRMM 3B42 v6 product  (Huffman et al. , 2007) , with spatial resolution of 0. 25 • × 0. 25 • and daily time step for a period spanning 12 yr  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009). The model parameters related to soil water budget were calibrated using daily discharge data from stream gauges (see next section for description of gauged data). Then, the model was validated against daily discharge and water level data from stream gauge stations, water levels derived from ENVISAT satellite altimetry data (Santos da Silva et al.
Discharge and water level observations We evaluated the assimilation of three types of data: (1) in situ discharge observations; (2) remotely sensed water levels derived from the ENVISAT radar altimeter; and (3) remotely sensed discharge estimates derived from radar altimetry water levels and rating curves. In situ daily discharge from 109 stream gauges were provided by the Brazilian agency for water resources ANA (Agência Nacional das Águas), the Peruvian and Bolivian national meteorology and hydrology services SENAMHI (Servicio Nacional de Meteorología e Hidrología) and the French ORE-HYBAM programme (Hydrologie, Biogeochimie and Geodynamique du Bassin Amazonien, http: //www. ore-hybam. org). We also used stage data from 66 ANA gauge stations, but only for validation purposes. Remotely sensed water levels were obtained from the EN-VISAT satellite altimeter.
The ENVISAT satellite has a 35day repeat orbit and an 80 km inter-track distance at the Equator. The database used is an extension of the one presented in Santos da  Silva et al. (2010) , consisting in 212 altimetry stations (AS -deduced from the intersection of a satellite track with a water body) with water level time series for the 2002-2009 period. ENVISAT data selection techniques preconized by Santos da Silva et al. (  2010 ) result in ∼ 10 to 40 cm water level accuracy. Due to differences in water levels datum reference, the comparisons between simulated and observed water levels were performed in terms of anomalies, i. e. after removing the long-term average. Altimetry-based discharge data was developed by Getirana and Peters-Lidard (2013) for the Amazon basin. This dataset was constructed using a rating-curve-based methodology deriving water discharge from ENVISAT altimetry data at 475 altimetric stations.
The stage-discharge relations at each AS were built based on satellite altimetry and outputs from a global flow routing (GFR) scheme  (Getirana et al. , 2012). A second experiment was performed by Getirana and Peters-Lidard (2013) using observed discharges at gauge stations to force the GFR scheme at downstream reaches. Validation of the methodology against observed discharges at 90 sites showed a mean relative error of 27 % for the experiment using in situ discharge within the GFR scheme. We assimilated data only from the 287 ASs located downstream of a gauging station where the accuracy of discharge estimates from Getirana and Peters-Lidard (  2013 ) was generally better.
Parameters of the DA scheme The first sensitivity experiments used the following standard parameters of the DA scheme. Ensemble size of the EnKF was set as N = 200. Precipitation fields were corrupted considering the following error parameters: precipitation relative error E = 25 %, and precipitation relative bias β = 1. 0 following  Andreadis and Lettenmaier (2006) ; temporal decorrelation length of precipitation errors τ t = 10 days; and spatial decorrelation length of precipitation errors τ x = 1. 0 • , similarly to  Andreadis and Lettenmaier (2006)  and  Clark et al. (2008). The parameter of water level measurements error was set as σ z = 0. 20 m, based on the accuracy of EN-VISAT estimates provided by Santos da  Silva et al. (2010). We computed the mean relative error between in situ discharge measurements and values provided by rating curves at 87 gauging stations from the ANA database as a surrogate of the discharge error parameter σ Q.
Data assimilation experiments We performed three data assimilation experiments, namely: (i) in situ discharge assimilation (Exp 1) (ii) Radar altimetry assimilation (Exp 2) and (iii) Assimilation of discharge series based on satellite altimetry (Exp 3). In the first experiment, we tested: (Exp. 1a) the assimilation of discharge from almost all gauge stations (80 %) using a few of them for validation (20 %), where these stations where randomly chosen; (Exp. 1b) the assimilation of only 12 stations (∼ 10 %) located at some of the major tributaries to emulate the situation of using only telemetric stream gauges for real time applications -we choose 1 or 2 stations located at the downstream reach of the largest Amazonian rivers; (Exp. 1c) the assimilation of discharge from almost all gauge stations, similar to (Exp.
1a), but without transforming discharge into the log space (Sect. 2. 4. ). Moreover, we explored the sensitivity of the DA scheme to some of its parameters, namely the ensemble size N, precipitation relative error E and temporal and spatial decorrelation lengths of precipitation errors τ t , and τ x. The second experiment (Exp. 2) evaluated the assimilation of ENVISAT radar altimetry water level anomalies from all altimetry stations. Stage data from all in situ gauges were used for model verification. Simulations were also compared in terms of discharge using in situ data to evaluate the impact of water level assimilation in discharge estimates. In the third experiment (Exp. 3), we assessed the assimilation of discharge derived from radar altimetry water level at all altimetry stations. Discharge data from all stream gauges were used for verification.
Prospects of streamflow forecasting Hindcast streamflow forecasts were generated using an ensemble streamflow prediction (ESP) approach  (Day, 1985) , as described below. The model uses an estimate of initial conditions derived from the DA scheme and runs forced by an ensemble of observed meteorological data from past years. An estimate of initial conditions is computed during the spin-up period using a hydrological model driven by observed meteorological forcings, updated using data assimilation of observations up to the time of forecast (e. g. forecast starts with model states from 1 June of 2010). Then, an ensemble forecast is obtained using observed meteorological data resampled from past years (e. g. meteorological data from 1 June to 1 September of  years 1998, 1999,. , 2009). Precipitation from TRMM Merge was used during spinup period, while during forecast the model was forced with TRMM 3B42 data for the period spanning 12 years  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009)  and, consequently, the forecast ensemble had 12 members.
The DA scheme used the configuration from Exp. 1b where in situ discharge data were assimilated to update model states before starting a forecast. ESP runs generated decadal forecasts up to 90 days lead time and starting at every 1st, 10th and 20th day of the month for the two year period of 2004-2005. Aiming at exploring the usefulness of such system to provide streamflow forecasts in future applications, we chose to test it for a past period  (2004) (2005)  where a large amount of discharge data is available for verification. For simplicity reasons, forecasts were evaluated only by deterministic means by averaging ensemble values into a single forecast. We used the skill score SS cli which compares the performance of the model forecasts with a control forecast based on climatology  (Wilks, 2006) : SS cli = 1 - t (Q t obs -Q t for ) 2 t (Q t obs -Q t cli ) 2 ( 12 ) where t is the time interval, Q obs is daily discharge observed at stream gauge stations, Q for is forecasted discharge, Q cli is the climatological value of discharge on day t computed from observations.
b Exp. 1a -data assimilation using discharge from 80 % of the stream gauges. c Exp. 1b -data assimilation using discharge from 10 % of the stream gauges. d Exp. 1c -equal Exp. 1a but without transforming discharge into the log space. to precipitation errors, although providing better results for data assimilation, may not realistically represent errors in the TRMM Merge dataset. For example, Tian and Peters-Lidard (  2010 ) developed a global map of errors in daily satellite-based precipitation estimates that show features not represented here: errors that are spatially and seasonally variable and that decrease with precipitation rate (from ∼ 100 % at 1 mm d -1 to ∼ 20 % at 100 mm d -1 in South America). On the other hand, the DA scheme had better performance with E = 50 %, which is only slightly larger than the values found by  Tian and Peters-Lidard (2010)  for the Amazon region, that ranges from ∼ 20 % to ∼ 70 %.
Radar altimetry data assimilation In this section, the assimilation of water levels derived from ENVISAT altimetry is evaluated (Exp. 2). Stage and discharge data observed at in situ gauging stations were used for validation purposes. The DA scheme improves water level simulations at altimetry stations used for assimilation (Fig. 4a ), as illustrated in Fig. 5a. On average, rms decreases by 56 % and E NS values increase from 0. 66 to 0. 96 (Table  2 ). Simulated water level accuracy also increases when compared to in situ stage data ( rms = -13 %). However, the improvement is more evident if only gauging stations located at rivers where altimetry data were examined (Figs. 4b  and 5b ). In this case, mean rms equals -43 % and E NS changes from 0. 75 to 0. 94 (Table  2 ), similar to what was obtained at altimetry stations.
Assimilation of discharge series based on satellite altimetry In the last DA experiment (Exp. 3), we evaluate the assimilation of discharge data derived from ENVISAT water level. Therefore, the data assimilated into the model has the same high spatial coverage and low temporal sampling as altimetry water levels have, but it also contains discharge informa-  tion which is the most important hydrological variable of the model. In situ discharge data were also used for validation. The DA scheme was able to assimilate altimetry-based discharges increasing the agreement between these observations and model results in most of the altimetry stations (Fig. 6a ), as exemplified in Fig. 7a. In average, the rms error between altimetry-based discharges and model results decreased 23 % (Table  3 ), which represents a smaller improvement if compared to the assimilation of in situ discharge ( rms = -49 % in Exp.
Summary and conclusions We presented the development and evaluation of a data assimilation scheme for both gauged and satellite altimetrybased discharge and water levels into a large scale hydrologic-hydrodynamic model of the Amazon River basin using the Ensemble Kalman Filter -EnKF. We also evaluated hindcast forecasts based on this system using the ensemble streamflow prediction approach, where the model was forced by an ensemble of past precipitation forcing from TRMM mission. According to our results, the data assimilation scheme performed well in assimilating in situ and remotely sensed discharge and water levels into the large scale hydrologichydrodynamic model. The assimilation of in situ discharge showed that EnKF can improve discharge estimates at assimilation gauges, but differently from previous studies at smaller basins (e.
Acknowledgements. The authors are grateful for: the financial and operational support from the  Brazilian agencies FINEP and ANA  (" Projeto de Integracão e Cooperacão Amazônica para a Modernizacão do Monitoramento Hidrológico "  ICA-MMH ) and  CNPq-IRD  (" Assimilacão de Dados de monitoramento Espacial para a análise do regime hidrológico da Bacia Amazônica e a previsão de curto e médio prazos "); the ENVISAT satellite altimetry data supplied by  ESA ; the TRMM data supplied by  NASA  and associated agencies; the TRMM Merge data provided by  CPTEC/INPE ; the discharge and stage data provided by  ANA, Hybam, SENHAMI-Peru and SENHAMI-Bolivia ; the EnKF Fortran codes provided by  Geir Evensen ; the support from  Fernando Fan  in processing precipitation data; as well as for the constructive comments from  Adalberto Meller  and  Jeffrey Neal , and also from  Ross Woods  (editor from HESS) and two anonymous reviewers.
BW, MY and feeding After each milking, cows were automatically weighted (software RIC version RW1. 7). Only the morningLBW was used for weight analyses, because the afternoon BW was more variable. As LBW is affected by digestive contents, the estimation of EBW was corrected for the digestive tract content. A change of 4. 5 kg of digestive contents per kg of DMI was assumed  (Remond, 1988 ). Variation of EBW was calculated day after day: EBW of the previous day was taken as reference weight. Energy balance was calculated according to the INRA method  (INRA, 2007). All cows were milked twice daily. At the entrance of the milking parlour, the cows were identified by an electronic collar and MY of each cow was automatically recorded (software Manufeed 500 pro, vc5 version 2. 011. 14). Twice weekly, samples composed of 1/3 of the afternoon milking, and 2/3 of the following morning's milking were analysed to Eating behaviour and metabolism in dairy cows determine milk fat and protein contents (g/l).
Statistical analyses Mean age at calving, mean EBW at the first wkpp and mean BVs for milk production and protein/fat content were compared between haplotypes with a Student T-test (TTEST procedure of the SAS software; SAS Institute Inc. , 2009). Results are presented as means 6 s. e. Data for zootechnical parameters (BW, milk yield, food intake, energy balance and eating behaviour) were available from the 1st to the 40th wkpp. Data for plasma concentrations of NEFA, glucose and insulin were available from 4 weeks before calving to the 16th wkpp. All these data were analysed using a linear mixed model, with the MIXED procedure of the SAS software (SAS Institute Inc. , 2009) for repeated measurements. The model included the fixed effects of haplotype, week postpartum or peripartum and the interaction between haplotype and week postpartum or peripartum.
Acknowledgements The authors thank the  ANR Genanimal and Apis-Gene  for financial support for the project. They also thank the staff of the  Experimental Unit UEPAO at the INRA Nouzilly, Eric Briant ,  Mickael Dupont ,  Mickael Delanoue ,  Ludovic Me  ´tivier and  Christophe Mouaze , for animal management and their involvement in the experiment. The authors also acknowledge Sebastien Elis at the  INRA Nouzilly  for his help,  Thibaud Le Moue ¨l ,  Marie Le Tilly  and  Colette Mustie  `re at the  INRA Rennes for NEFA  and glucose assays. S. Coyral-Castel is a PhD student supported by the  'Institut de l'Elevage'  and the  'Association Nationale de la Recherche et de la Technologie '.
STIMULI Stimuli were 54 fearful and 54 neutral adult faces selected from the FACES database  (Ebner et al. , 2010). All faces belonged to different individuals in order to prevent subjects from subsequent false memory recognition of fearful/neutral stimuli sharing the same identity. To focus attention on facial emotional expression, we surrounded faces with a black mask, hiding hair, ears or neck as in previous studies  (Golouboff et al. , 2008; Pinabiaux et al. , 2013)  (Figure  1 ). Arousal was rated on a 5point scale by an independent group of 15 young adults (mean age 22 years ± 1. 78). As expected, fearful faces were rated as more arousing than neutral ones (mean rate: 1. 52 ± 0. 11 vs. 4. 64 ± 0. 9).
IMAGING PROCEDURE Images were acquired on a 3-T Siemens Magnetom Trio scanner. High resolution 3D IR-prepped T1-weighted anatomic scans were first acquired (repetition time = 2300 ms, echo time = 3 ms, TI : 900 ms, 256-mm field of view, matrix 240 × 256, 160 sagittal slices, 1 × 1 × 1. 1 mm3) in 7 min 46 s. During encoding, 123 T2 * -weighted EPI images were acquired per run (TR = 2400 ms, TE = 30 ms, 192-mm FOV, 64 × 64 matrix, 81 flip angle, 40 axial slices, 3 mm isotropic, 5 min 2 s). The three initial volumes were discarded to allow for T1 equilibration. Data were analyzed using SPM5 (http://www. fil. ion. ucl. ac. uk/ spm/). Differences in slice acquisition timing were corrected by resampling all slices in time to match the middle slice. Functional volumes were spatially realigned to correct for motion artifacts.
Behavioral data Data were analyzed with Statistica (www. statsoft. com). Percents of correct and false recognitions were collected and averaged in each age group and a corrected memory accuracy measure was computed as %hits-%false recognition. Global effects of age (8-12 years vs. 13-17 years) were analyzed using 2 by 2 Mann-Whitney tests. Global effects of emotional expression (fearful vs. neutral) were analyzed within each group separately using 2 by 2 Wilcoxon tests. Group x emotional expression interaction was also explored using 2 by 2 Wilcoxon tests. Accuracy on the "face parts" task and interaction between encoding and recognition accuracy were also examined using non-parametric comparison and correlation.
With a larger number of subjects, however, age could have been considered as a continuous variable and regression analyses could have been conducted without any a priori statement on age-groups definition. Indeed, our results point to age-related changes around 12-13 years, but more gradual changes may occur within 9-17 years range. Studies exploring longitudinal intra-individual changes are needed to demonstrate such progressive changes in the functional bases of memory, but are exceedingly difficult to conduct. Second, in a preliminary pilot study, we had used children and adolescents faces (5-15 years old) from our own database  (Golouboff et al. , 2008; Pinabiaux et al. , 2013 ), but we had been unable to show subsequent memory effects due to an insufficient number of item per conditions (fear hits, fear misses, neutral hits, neutral misses).
We thus employed adult faces (young, middle age and older) from a larger database  (Ebner et al. , 2010)  ignoring the reported own-age bias, in which people recognize faces of people of their own age more accurately than faces of other ages (e. g. ,  Anastasi and Rhodes, 2005). Nevertheless, recent studies have shown that this age effect changes rapidly with age  (Hills and Lewis, 2011)  and that the face recognition system is updated on the basis of recent experience and/or motivation to process faces  (Hills, 2012). We can assume that the use of adults' faces puts our two experimental groups on an equal level regarding the own-age bias, which might not have been true with a 5-15 years range database. Finally, a recent study pointed out that the use of adult-sized head coils on child-sized heads may lead to underestimation of the signal-noise ratio, especially in mesial brain regions  (McKone et al.
ACKNOWLEDGMENTS This work was supported by a PhD grant to  Charlotte Pinabiaux  and a BQR grant to  Pr. I. Jambaqué  from the  Paris Descartes University. We thank the nurses and technicians of the  Laboratoire de Recherche Biomédicale (NeuroSpin, CEA ) for their help in participant's recruitment and organization of MRI exams. We are thankful to  Dr. S. Mériaux  and  Dr. A. -D. Devauchelle  for their helpful advices on fMRI statistical analysis and comments.