dataesr/scientific-paragraphs-categorization huggingface dataset:
Sociodemographic and clinical data were also collected. results Seventy-nine (17. 2%), 19 (4. 1%), 259 (56. 3%), 44 (9. 6%) and 59 (12. 8%) women received a BRCA1/2, another high/moderate-risk pathogenic variant (PV), negative uninformative, true negative (TN) or variant of uncertain significance result (VUS), respectively. On multiple regression analyses, compared with women receiving another result, those with a VUS decreased more in psychosocial problems related to hereditary predisposition (eg, coping with the test result) (ß=-0. 11, p<0. 05) and familial/social issues (eg, risk communication) (ß=-0. 13, p<0. 05), almost independently from their problems before testing. Women with a PV presented no change in hereditary predisposition problems and, so as women with a TN result, a non-significant increase in familial/social issues.
Women were also asked whether they had lost family member(s) due to breast or ovarian cancer and if they had received psychological help after the receipt of the test result. Clinical data were collected from medical records. BC risk estimates were computed at T1 and just before the test result disclosure using the BOADICEA web application (BWA v3, University of Cambridge, Cambridge, UK)  39  (data only available for IC, Paris and UHC, Cologne). Possible gene panel testing results included: (1) a pathogenic variant on BRCA1/2 or (2) other high/moderate-risk gene, or (3) a non-informative result (no pathogenic variant in index person) or (4) a VUS. Possible targeted gene testing included either a high/moderate-risk pathogenic variant or true negative result (no pathogenic variant in predictive tested healthy woman).
Funding This project has received funding from the  European Union   Horizon 2020 research and innovation programme  under grant agreement No  634935  (BRIDGES). In France, this work has been partly financed through the  SiRIC label , which has designated the  Institut Curie , an integrated cancer research site, and a grant from  Ile-de-France Cancer Pole   N° 2015-1-EMERG-14-ICH-1.
Second, the formulation in  [12]  is complicated, involving a number of large sums and products along the IMU data sequence. Third, the way Jacobians are obtained is somewhat cumbersome, leaving the reader with insufficient intuition on what is going on behind the proposed formulae. As a whole, it does not appear easy to generalize such methods to other motion preintegration cases. In order to give a response to these topics, our approach to IMU pre-integration differs from  [12]  in the following aspects. First, we provide a clear physical interpretation to the IMU deltas. Second, we present a recursive formulation, meaning that we provide equations to be applied every time an IMU sample is acquired. Moreover, one integration step is broken down in different distinguishable stages, which contributes to clarity.
B. Pre-integrated IMU factors An IMU factor is created between consecutive keyframes x i and x m by integrating all IMU data from t i to t m (Fig. 3 ). The factor is pre-integrated during the data acquisition phase, and then used in a second phase to compute the motion residuals at each iteration of the optimization solver. Both phases are described hereafter. 1) IMU pre-integration: The following pipeline of operations is performed to recursively pre-integrate IMU data into a unique measurement. At the reception of each IMU measurement y k = (a, ω) k , start by correcting it with available bias estimates b i = (a b , ω b ) i , to produce the tangent vector τ = νδt. For this, set the velocity part of ν to zero as the IMU is by definition at zero speed with respect to the moving frame (see comment B.
D. Related works Two Apriltag based visual-inertial SLAM systems have been implemented in the previous years. In  [23] , the authors rely on a EKF in which state propagation in naturally handled by the IMU and each marker detection is used in an update step where the reprojection error of its 4 corners provides a 8D innovation vector. A closer solution to ours was very recently proposed in  [15]  and is also based on graph SLAM optimization benefiting from Forster's IMU pre-integration from GTSAM. As explained previously, the Apriltag factor formulation is different from ours and the algorithm is tested on large datasets consisting only of smooth motions.
A. Experimental setup We have gathered several datasets in the experimental arena of the humanoid robots at LAAS-CNRS, a 3D environment about 10m×5m made of flat floor, stairs of various slopes and a 30cm wide beam. The robot environment was augmented with about 20 fiducial "April-tag" markers (about 20 cm width). The tags have been randomly dispatched in the environment. They are fixed during a run, but may vary significantly between two sets of data, and their locations are not calibrated -that is, we do not have ground-truth localization of the tags. Each dataset is composed of 3 sequences: • a sequence of RGB images captured at 33 Hz • a sequence of IMU measures captured at 200 Hz • a sequence of motion-capture (MoCap) at 200Hz measurements used as ground truth. -1. 5 -1. 0 -0. 5 0. 0 0.
B. Localization precision We consider four datasets which are summarized in table I. They cover different tasks on which a consistent estimation of the robot movement is necessary. The first one is a relatively long sequence consisting of two loops with the VIS handheld. This is used to test the long term localization of the robot, which is interesting for navigation. Secondly, we made the LAAS Gepetto team HRP2 walk and turn around on a short distance to evaluate the resilience of the filter to the vibrations of the robot. Finally, two more challenging datasets are recorded while the robot is climbing and descending stairs. Especially on the latter, the locomotion causes impacts that on one hand bring the IMU close to its dynamic range saturation, and on the other hand provokes images with motion blur.
Note that during these experiments, the estimator was not used for feedback control. In order to compare our results with the ground truth, we used methods described in  [24]  to align trajectories given that 4 DoFs are unobservable in VI estimation. For each case, key frames are created at a frequency of 6. 6 Hz (every 5 images) if tags are detected in the corresponding image. Figure  6  presents a quantitative evaluation of the translational errors. In all cases, our estimator achieves errors consistently below a few centimeters. The biggest errors are obtained for the walking datasets where the two humps correspond to phases where the robot is turning on itself and sees landmarks that it will not see again later in the trajectory. A high rate estimation of a humanoid robot velocity and in particular of its center of mass is critical for balance controllers.
Using artificial landmarks is an interesting solution for humanoid robots navigating indoor in an professional environments, in particular a lab, that can be easily augmented with the markers. Finally, we have proposed an experimental validation, based on 4 new datasets, that are released with the paper. We have demonstrated unprecedented accuracy during a 3D locomotion, with about 1cm error in average for climbing 5 steps of a stair. While the localization is already satisfactory with respect to the needs of the control, the proposed method is only one step toward the final localization that we aim at. As recently proposed by other teams, we finally want to also fuse the information of contact in the estimator. This should be done following the pre-integration approach to account for the high-frequency of contact information.
This work has been supported by the  MEMMO European Union  project within the  H2020 Program  under Grant Agreement No. 780684 , the  Spanish Ministry of Science, Innovation, and Universities  project  EB-SLAM  ( DPI2017-89564-P ), by the  EU H2020  project  GAUSS  ( DPI2017-89564-P ) and by the  Spanish State Research Agency  through the  Maria de Maeztu Seal of Excellence  to  IRI MDM-2016-0656.
In the Middle East, a major dust source on a global scale, this mechanism has not been studied extensively most probably because of the lack of sufficiently high resolution observational data. In this study, we investigate, for the first time the formation of a cyclone over Iraq (Figure  1b ) and deep convection generating density currents over Syria (Figure  1c ) to highlight the mechanisms leading to their occurrence and their role in the observed high dust activity in September 2015 in the Middle East and surroundings areas. The paper is organized as follows: After the introduction in Section 1, the datasets used is this study are described in Section 2. The dust storm associated with the cyclone is presented in Section 3 including the synoptic situation prior to cyclogenesis (3. 1), the characteristics of the cyclone (3.
Reanalysis Data To investigate the atmospheric dynamics that triggered the cyclone formation, we use the European Center for Medium Weather Forecast (ECMWF) ERA 5 reanalyses  [41]  for their higher temporal and spatial resolution (hourly at approximately 31 km with 137 levels to 0. 01 hPa). ERA5 also has a more recent model and data assimilation system. The 6-hourly datasets of near-surface dust concentration (in µgm-3) and Aerosol Optical Depth (AOD) were obtained from the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) reanalysis database at spatial resolution of 0. 5 × 0. 625 • and at 72 hybrid-eta levels from surface to 0. 01 hPa  [42]. MERRA-2 reanalysis has been increasingly available for studying dust events and their interrelation with meteorology over the globe  [43, 44].
We expect the uncertainty on the extinction coefficient and on the AOD to be on the order of 30% (and may be greater for AOD greater than 2), accounting for an uncertainty of 20% on both the backscatter-to-extinction ratio and the multiple scattering coefficients. It is worth noting that all the instruments/platforms are part of the A-Train except for MSG. The A-train orbit overpasses the Middle East twice a day, once during the daytime (between 1230 and 1430 UTC, Universal Coordinated Time) and once during the night-time (between 0030 and 0230 UTC) and has a revisit time period of the same orbit of 16 days  [51]. The data is available through the ICARE Web portal (http://www. icare. univ-lille1. fr). Finally, the orography map was produced using the ETOPO1 Global Relief Model data (doi:10.
Acknowledgments: The  European Centre for Medium-Range Weather Forecasts (ECMWF)  is acknowledged for making the meteorological re-analyses available from their data server. MERRA-2 data are developed by the GMAO with support from the  NASA   Modeling, Analysis and Prediction program. We acknowledge the use of imagery from the  NASA  Worldview application (https://worldview. earthdata. nasa. gov),part of the NASA Earth Observing System Data and Information System (EOSDIS). EUMETSAT is acknowledge for the MSG-SEVIRI observations. We acknowledge the financial support of the  Centre National des Etudes Spatiales (CNES , the  French Space Agency)  through the project " IASI-TOSCA " ( Terre, Océan, Surfaces continentals, Atmosphère ) and the  Programme National de Télédétection Spatiale  (PNTS, http://www.
A relevant description of the model transition and experimental design are shown in Sects. 2 and 3. A comparison of the model performance is presented in Sect. 4. Conclusions and discussion are summarized in Sect. 5. Information regarding the code and the data availability is shown in Sect. 6. 2 Transition of the BCC climate system model from CMIP5 to CMIP6 Table  1  shows a summary of the different BCC models or versions used for CMIP5 and CMIP6; all of them are fully coupled global climate models with four components, atmosphere, ocean, land surface and sea ice, interacting with each other. They are physically coupled through fluxes of momentum, energy, and water at their interfaces. The coupling was realized using the flux coupler version 5 developed by the National Center for Atmosphere Research (NCAR).
Experimental design All BCC simulations presented in this work follow the protocols defined by CMIP5 and CMIP6. We aim for them to be comparable in spite of showing the transition of our climate system model from CMIP5 to CMIP6. The principal simulation to be analyzed is the historical simulation (hereafter historical) with prescribed forcings from 1850 to 2005 for CMIP5 (to 2014 for CMIP6). Historical forcings data are based, as far as possible, on observations and were downloaded from (https:// esgf-node. llnl. gov/search/input4mips/, last access: 1 April 2018). They mainly include the following: (1) GHG concentrations (only CO 2 , N 2 O, Ch 4 , CFC11, and CFC12 used in BCC models) with zonal-mean values which are updated monthly; (2) yearly global gridded land use forcing; (3) solar forcing; (4) stratospheric aerosols (from volcanoes); (5) CMIP6-recommended anthropogenic aerosol optical properties which are formulated in terms of nine spatial plumes associated with different major anthropogenic source regions  (Stevens et al.
, 2014). Figure  2  shows time series of the annual and global mean of the net energy flux at top-of-atmosphere (TOA) and the sea surface temperature for 600 years in the piControl simulation. The whole system in BCC-CSM2-MR fluctuates around a +0. 4 W m -2 net energy flux at TOA without an obvious trend over 600 years. The global mean surface air temperature shows a small warming after 600 years (Fig. 2b ). During the last 300 years, there are (±0. 2 K amplitude) oscillations of centennial scale for the whole globe and for the areal average of 60   (Wielicki et al. , 1996). Recently, data of CERES have been synthesized with EBAF (Energy Balanced and Filled) data to derive the CERES-EBAF products, which are suitable for the evaluation of climate models  (Loeb et al. , 2012). As shown in Table  4 , the TOA short- Clouds constitute a major modulator of the radiative transfer in the atmosphere for both solar and terrestrial radiations.
The second survey was conducted in 2017 providing input from European exposure science practitioners in preparation of the first ISES Europe Workshop, held on 19-20 June 2018 in Dortmund, Germany. This workshop was hosted by the Federal Institute for Occupational Safety and Health (BAuA) and attended by 119 professionals from the European Commission, European agencies, national authorities, industry, academia, consultants, and insurance companies. In the preparatory survey, practitioners were asked for their top five priority areas for advancing exposure science in Europe until 2030. Reported data from both surveys were anonymised, pooled, and categorised using text analytics and expert judgement methods, which are discussed elsewhere (e. g. [58] ). Based on analysing the survey results, six thematic focus areas for exposure science in Europe were identified, namely (1) data repositories and These six thematic focus areas constitute the starting point for identifying key needs and main capacity building blocks for successfully developing a European Exposure Science Strategy.
Of course, the checklist was not intended to replace individual medical judgment or individual patient needs, and the attending physician was not enforced to use or follow it for all patients with HF. A control cohort was included retrospectively in our institution from June 2014 to December 2014, with the same inclusion and exclusion criteria. The checklist was not used in the control cohort (Fig. 2 ). For each patient in this control cohort, a checklist was completed retrospectively, using medical records with the latest available data at the time of discharge.
Considerable time was spent interrogating each chronicle and resolving errors that included data-entry issues, transcription errors from chart recorders, and datum problems (e. g. Maurice et al. this issue). Indeed, a substantial proportion of collated chronicles had to be discarded from further analysis. Closer monitoring of data collection and greater use of observational records are required to improve the quality of these datasets. A capacity-strengthening and knowledge co-production workshop was held at Sokoine University of Agriculture in Morogoro (Tanzania) in February 2017. The workshop involved 40 participants from 11 countries in Africa (Fig. 1 ) who analysed multi-decadal groundwater-level records collated from 9 countries including Benin, Burkina Faso, Ghana, Niger, Sénégal, South Africa, Tanzania, Uganda and Zimbabwe.
The assembled chronicles provide invaluable datasets to facilitate direct assessments of past impacts of climate variability-e. g. ENSO, Atlantic Multi-decadal Oscillation (AMO)-  (Carvalho et al. this issue)  and abstraction (e. g. Maurice et al. this issue) on groundwater storage. Such records, when continuously updated, can also provide key input to water resources management by tracking emerging risks to water security from groundwater storage decline or groundwater flooding (e. g. Murray et al. 2018). Regional-scale (>50,000 km 2 ) networks of long-term piezometric records (e. g. Shamsudduha et al. 2012; Kolusu et al. 2018; Carvalho et al. this issue)  can also be used to test the reliability of largescale, satellite observations from the Gravity Anomaly and Climate Experiment (GRACE).
An unintentionale leak can also be created before test lungs. Hygrometric measurement sites: 1 = Hygrometer measuring ambient conditions around the ventilator; 2 = Hygrometer with high humidity level probe (heated probe); 3 = Second hygrometer Y-piece. Circuits were also changed and dried for 15 min after each configuration. These two hygrometers have a response time of 30 s. Hygrometers were connected by a USB port to the computer and data were acquired by Testo Comfort Software X35 (Testo, Lenzkirch, Germany). Unintentional leaks were simulated by a 4 mm diameter calibrated orifice resulting in a leak of 24 L/ min at 10 cmH2O as found in other studies  [27]. The orifice was placed between the test lungs and the corrugated tube. Two types of ventilatory circuits were used: by using an expiratory valve for the valve circuit, or by using a specific connector to create an intentional leak for the vented circuit (Whisper Swivel 2, Respironics, Murrysville, Pennsylvania, USA).
Statistical analysis Statistical analysis was performed on absolute humidity. Mean values for the various situations were compared by analysis of variance using a nonparametric test: Fig. 2  Graph showing the measurement protocol. After a warm-up period of aroud 25-30 min, the relative humidity reaches a plateau (no temperature variation greater than 1 °C and relative humidity variation greater than 5%). The measurement period begins 5 min after the plateau and lasts 10 mint = time Kruskal-Wallis test using Matlab software with statistics toolbox (Mathworks, Natick, MY). A probability level p < 0. 05 was considered significant. All configurations were compared to each other one by one for the same humidifier and the various humidifiers were then compared to each other using the same configuration.
Humidifier performances Absolute humidity results are summarized in Fig. 3  and Additional file 1. All differences were statistically significant (data not shown). Absolute humidity (AH) in the valve circuit was between 30 mg/L and 40 mg/L. This humidification was sufficient in all configurations for three humidifiers (HC550, D900 and AIRcon), while the MR810 failed to achieve sufficient humidification in two out of four configurations and the HC150 failed to achieve sufficient humidification in three out of four configurations. AH below 33 mg/L  [19]  was observed more frequently with the vented circuit. For all humidifiers except HC150, under optimal conditions with a tidal volume of 600 mL without unintentional leak, humidification was sufficient (lowest AH was 35 mg/L), but, with a higher tidal volume or an unintentional leak, only the D900 humidifier provided sufficient humidification in all situations (AH equal to 38 mg/L).
Generalities about incore measurement campaigns For each CALMOS device, a comprehensive measurement campaign has been carried out inside the core. The -Test of all possibilities offered by the HMI system to set the parameters before the scanning (scan step from 88 to 11mm, moving-up or moving-down, waiting time at each step, data acquisition for a long time…). All these tests required, for the only CALMOS-2 device, the total following durations: -3 monitored cycles (F282, F283 and F284), -A total 14 hours recording time of the start-up phase, from 0 to the nominal power, -A 74 hours total duration of automatic scanning time, thanks to the specific software and the human machine interface (HMI), -More than 19 days monitoring time of the residual power decay.
The intensive use of CALMOS-2 in the core, cumulating a total 70 hours automatic scanning time, has demonstrated its reliability for OSIRIS in-core conditions up to a 12 W. g -1 heating rate and up to a 2. 5 x 10 14 n. cm -2. s -  thermal neutron flux. While meeting the reactor safety requirements, the CALMOS device becomes an operational equipment suited to the surveillance and the qualification of the incore radiation field in an MTR reactor, a key data for the dimensioning and the surveillance of any experimental device irradiated inside.
Acknowledgments This R&D program was carried out in the framework and with the financial support of the  MCIS/INSNU  project of CEA/DEN. Authors wish to thank the DEN/DER/SPESI and SRJH services of  CEA/Cadarache  for their support, and the  LIMMEX joint laboratory between CEA and Aix-Marseille University  for fruitful and constructive discussions along this R&D program. The constant support of the OSIRIS operating personnel from CEA/DEN at the Saclay center was crucial for performing irradiation campaigns inside the reactor. Finally, authors wish to address special thanks to  Valérie Lepeltier ,  Véronique Clouté-Cazalaa  and  Jean-Marie Guyot ,  Romain Illiaquer ,  Eric Bouard ,  Jacques Bubendorff  and  Cedric Courteaux  from the  CEA/DEN  of the Saclay center for their help through their respective specialties, without which we could not have conducted this research program.
Both experiments contribute to a more precise identification of the parameters to take into consideration when interpreting the 17 O-excessPhyto. They also brings elements to further understand and model the d 18 O and d 17 O of grass leaf water, which influences the isotope signal of several processes at the soil/plant/atmosphere 430 interface. Table  1. Growth chamber experiment 1 : a) Experimental set-up, phytolith content and triple oxygen isotope data obtained for phytoliths (Phyto) and leaf water (LW). Isotope.
, https://doi. org/10. 5194/bg-2019-73 Manuscript under review for journal Biogeosciences Discussion started: 27 March 2019 c Author(s) 2019. CC BY 4. 0 License. Table 2. Growth chamber experiment 2a and 2b. Experimental set-up, phytolith content and triple oxygen isotope data obtained for phytoliths (Phyto), leaf water (LW) and irrigation water (IW). Isotope fractionation (and l) between leaf water and irrigation water (LW-IW) and phytoliths and irrigation water (Phyto-IW) are presented. Samples are named according to the climate chamber # they were collected in (e. g. F4), the date of sampling (dd/mm/yy) and the sampling after day or night (Day vs Night in experiment 2a) or after constant climate conditions or day/night alternation (Cst vs DN in experiment 2b). n : number of replicates ; SD : standard deviation calculated on the replicates; Phyto Conc.