dataesr/scientific-paragraphs-categorization huggingface dataset:
After CUORICINO, the achievement of these goals were part of the intense CUORE R&D program. A challenge related to the detector performances is the uniformity of their behaviour. A crucial requirement is the capability of completing 988 TeO 2 bolometers and assembly them into 19 towers in clean and reproducible conditions. In CUORICINO, we observed a large spread in the pulse shape among the bolometers. This leads to a complication in the data analysis that could be more challenging in case of an experiment, like CUORE, with about 1000 single detectors. For these reasons, before starting the construction of the 19 CUORE towers, an additional tower, named CUORE-0, was produced according to the CUORE requirements. ICNFP 2016.
CUORE-0 acquired data for the 0νββsearch collecting a total exposure of 9. 8 kg•y of 130 Te. Data are grouped in month-long blocks called datasets. At the beginning and end of each dataset we calibrate the detector by placing a 232 Th source next to the outer vessel of the cryogenic system. We use the calibration line with the highest intensity and next to the ROI, 2615 keV from 208 Tl, in order to study the detector response function to a mono energetic energy deposit for each bolometer and dataset. We estimate the shape parameters of the 2615 keV line with a simultaneous, Unbinned Extended Maximum Likelihood (UEML) fit to calibration data. The physics exposure-weighted effective mean of the FWHM values for each bolometer and dataset is 4. 9 keV, with a corresponding RMS of 2. 9 keV. We evaluate the background level in the alpha-dominated region (2700-3900) keV to be 0.
Acknowledgments The CUORE Collaboration thanks the directors and staff of the  Laboratori Nazionali del Gran Sasso  and the technical staff of our laboratories. This work was supported by the  Istituto Nazionale di Fisica Nucleare (INFN) , the  National Science Foundation , the  Alfred P. Sloan Foundation , the  University of Wisconsin Foundation , and  Yale University. This material is also based upon work supported by the  US Department of Energy (DOE) Office of Science  and by the  DOE Office of Nuclear Physics. This research used resources of the  National Energy Research Scientific Computing Center (NERSC). More details can be found at: http: http://cuore. lngs. infn. it/support.
Clinical Research Paper In French daily routine, radiotherapy patients often fear radiation-induced dermatitis and frequently see distant healers, also called "biofield healers". Biofield healers are believed to be able to prevent and limit radiation dermatitis through hand imposition, therapeutic or healing touch, breathe and healing symbols. Although radiotherapy patients frequently report to see biofield healers in daily routine practice, the rate of utilization, the cost, the efficacy and the nature of the healer procedure has only been very poorly described  [11]. The main objective of the present study was to collect data on practices and frequency of use of biofield healers in radiotherapy patients. The secondary objective was to evaluate the subjective and objective benefit of biofield healers on tolerance to treatment and on toxicity.
Study design A retrospective survey was conducted at the Lucien Neuwirth Cancer Institute, based on a questionnaire (completed by the patient), and on clinical data (collected in medical records by radiation oncologists). All women undergoing a radiotherapy for a breast cancer in 2015 in the department were screened. All men treated with radiation therapy for a prostate cancer in the department in 2015 were screened. The flow chart summarizes the patient selection (Figure  1 ). The institutional ethics committee approved the study, which was conducted in compliance with the Helsinki Declaration.
Study endpoints Patients were consecutively phoned at the end of their radiation treatment, and answered the questionnaire on biofield healers (Annex 1). Patient's consent was obtained before each questionnaire completion. Phone conversations lasted approximately for five minutes. Collected data were: healer appointment, Yes/ No; Frequency; healer procedure: Appointment/Phone/ Picture; healer technique or action: Hand imposition (Therapeutic or Healing Touch)/Breathe/Healing Symbols; Remuneration; Global tolerance of treatment: 0 to 10 scale (0 = Bad tolerance; 10 = Excellent tolerance); Pain evaluation: visual analogic scale (VAS, 0 = No pain; 10= Maximal pain imaginable); Analgesics consumption; Fatigue hetero-evaluation: scale from 0 to 10 (0 = No fatigue; 10 = Extreme fatigue). Self-evaluation by patient of healer's impact on treatment tolerance was also reported (Impact: None, A little, Good or Very Good).
Genetic markers We acquire blood samples from the participant, and-where possible-both biological parents, for genomic analyses. First, in collaboration with the Autism Speaks MSSNG project (https://www. mss. ng), we carry out whole-genome sequencing of multiplex (families with two or more individuals with ASD) and simplex families (where only one person has ASD) to assess the combination of inherited and de novo genetic variation (rare and common variants, coding and non-coding variants) that may confer risk for ASD or specific traits linked to ASD. Second, we aim to identify pathways associated with ASD and assign each individual to particular molecular pathways based on their entire mutation profile. In addition, data will be pooled with other international initiatives, to improve the ability to identify new ASD-risk genes.
Central data base/data access The central database comprises three layers: First, raw data from all recruitment centres (and the on-line questionnaire platform) are uploaded onto the central database using a secured web-interface. Second, for neuroimaging pre-processing and quality control procedures, analysis teams access the raw data via sftp, carry out the necessary analyses locally, and upload quality controlled (QC)/preprocessed data. The final data set is 'read-only'. A webbased interface enables users to access the database using personalized login details to search, filter, and download data. The EU-AIMS database is currently accessible for internal users but will subsequently be opened to the wider scientific community.
We will also compare several trajectories simultaneously using mixed design linear regression models  [98]  to establish whether in the ASD group, performance develops with delay or is uneven across domains or component processes. Cross-sectional age-related patterns will be compared to longitudinal (within person) trajectories once follow-up data are available. Sex differences have previously been reported both in typical development and ASD groups at multiple levels, including serum biomarkers, brain structure and function, Table  3  Governance structure of LEAP, pre-registration, quality control, and reporting of findings Data analysis is split into expert core analysis groups, broadly defined by data modality (e. g. clinical measures, cognition, EEG, structural MRI, functional MRI, etc.
Stratification by unsupervised, data-driven approaches The second approach uses data-driven multivariate analysis techniques to identify subgroups based on the pattern of the data itself. For example, cluster analyses are a widely used set of techniques to divide data into prototypical groups based on only the data points and their relationships to one another. Input variables could be multiple cognitive  [103] , eye-tracking indices  [104] , EEG values, or a combination of values from different data types. The optimal number of (meaningful) clusters can be determined based on height differences in a cluster tree, while cluster robustness will be evaluated using cross-validation techniques, such as bootstrapping. Normative modelling approaches have also recently been extended to model biological variation across the entire study sample or a typical population  [105].
Twin data Twin data are analysed by applying a statistical framework of multiply adjusted (conditional) linear regressions based on generalised estimations equations (GEE) and allowing both categorical and dimensional ASD outcomes. In addition to the GEE model, an additive genetics, common environment, and unique environmental (ACE) model will be computed to determine heritability estimates. For all analyses, probability estimates for different twin groups will be included, based on the population-based twin cohorts, which allows generalizability of the results.
For eye-tracking, acquisition rates of the four main task sets ranged between 91-86% and 91-87% in the ASD and TD groups, respectively. Two tasks that were later added to the protocol (change detection, emotion matching) had lower acquisition rates. Blood samples were acquired in 68% of ASD participants and 73% of TD participants and 'trios' (i. e. participant, biological father, and biological mother) in 29% of people with ASD. Saliva was acquired in those individuals who did not wish to give a blood sample, 39% of people with ASD and 30% of TD people. Urine was given by 82% of participants in both the ASD and TD groups, and hair samples in 43% of people with ASD and 35% of people with TD. Additional file 3 shows the data acquisition rates across the different assessment modalities and split by group and schedules.
Funding This work was supported by  EU-AIMS  (European Autism Interventions), which receives support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement no. 115300 , the resources of which are composed of financial contributions from the  European Union's Seventh Framework Programme  (grant FP7/2007-2013), from the  European Federation of Pharmaceutical Industries and Associations  companies' in-kind contributions and from  Autism Speaks.
In addition, a number of deposited layers less than five induces the formation of non-continuous film. The sample morphology was analyzed by scanning electron microscopy (SEM). The measurements were performed on a Zeiss Supra 55VP operating in secondary electron imaging mode with an accelerating voltage of 2 kV and 2. 7 mm working distance. The optical response of ZnO layers were investigated by a phase modulated Horiba ellipsometer in the 0. 6-4. 75 eV spectral range at three incidence angles of 50°, 60° and 70°. The data fit is carried out simultaneously on all incident angles to improve the confidence limit in the data fit. Temperature-dependent PL measurements were carried out, using a monochromator with a 600 grooves/mm grating combined with CCD camera (spectral resolution of 0. 23 nm) over a wide range of temperature from 10K to 300K.
Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge-which is crucial in flood simulations-has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data  within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark.
However, without a calibration for individual river basins global grid-based hydrological models (GHMs) generally perform worse in reproducing the observed discharge than calibrated regional models even at monthly timescale  (Hattermann et al 2017 , Veldkamp et al 2017). A first reason is the coarse spatial resolution and uncertainties in global precipitation datasets  (Fekete et al 2004 , Voisin et al 2008 , Biemans et al 2009 , Wanders and Wada 2015 , Müller Schmied et al 2016 , 2014). A second reason is the uncertainty related to the land surface model structure, namely the representation of local hydrological processes at the scale of grid cells, such as the partitioning of precipitation into evaporation and runoff, and representation of snow and groundwater  (Haddeland et al 2011). A third factor is the routing of the simulated gridcell runoff through the river network, which is a crucial step in simulating river discharge, a variable directly comparable to numerous observations.
Data and methods Nine offline routing simulations were performed with CaMa-Flood driven by daily runoff data from nine GHMs. The simulated daily and monthly discharges were then evaluated at 1701 globally distributed discharge gauges, using the data from the Global Runoff Data Centre (GRDC, 56068 Koblenz, Germany). Additionally, each GHM used in this study also provides daily discharge output from its own river routing scheme, which in general has simpler physics compared to CaMa-Flood. This group of discharge output was also evaluated against observations using the same performance metrics. For each GHM and at every discharge gauge location, the difference in performance metrics between the CaMa-Flood simulated discharge and the GHM's own discharge simulations were analysed. The following sections discuss the data and methods used here in detail.
(ISIMIP2a). Each of the GHMs used in this analysis was forced by a daily (three-hourly for MATSIRO) observation-based historical climate dataset GSWP3 (http://hydro. iis. u-tokyo. ac. jp/GSWP3). Supplementary table  S2  (available online at stacks. iop. org/ERL/12/075003/mmedia) lists some general model characteristics regarding both the water and energy budget formulations, and routing schemes of the various GHMs, which mostly rely on linear reservoir algorithms. In the ISMIP2a framework, they all used the same 0. 5°drainage direction map (DDM30,  Döll and Lehner 2002) , with the exception of CLM and PCR-GLOBWB. Note that the routing model of the MPI-HM model includes a wetland scheme with floodplains (Stacke and Hagemann 2012), and in order to have a consistent drainage direction map, the routing model of the MPI-HM model was also used to route the runoff from the ORCHIDEE model.
Comparison to the observed river discharge Observed river discharge dataset from GRDC consists of more than 9000 stations in 160 countries, with an average record length of 42 years. We made a selection from this dataset according to the following criteria: 1. A minimum of 5 year coverage during the validation period of 1971-2010. While the data do not need to be continuous, we only use years with less than 10 missing days to avoid under-sampling any month. A threshold of 5 years instead of 10 years is adopted to allow for better spatial coverage, enabling assessment at some important basins such as Indus and Ganges in Asia, and Po in Europe, although a 10 year threshold would be more robust for inter-annual variations. Analyses using stations with a minimum of 10 year coverage show similar results (not shown), nevertheless.
2. A minimum catchment size of 9000 km 2 to omit catchments whose hydrological processes are not correctly represented by GHMs operating at 0. 5°( Hunger and  Döll 2008). Using the MIRCA2000 irrigated crop area dataset (Portmann et al 2010) and the Global Reservoir and Dam (GRanD) Database (Lehner et al 2011), we separated GRDC stations with (near-)natural flow regimes from stations with highly managed flow, based on catchments characteristics. A catchment is classified as (near-) natural if its area subject to irrigation is <2%, and the total reservoir capacity in the catchment is <10% of its long-term mean annual discharge. As detailed in section 2. 4, we examined basinaverage performance metrics, in addition to metrics at individual stations to avoid statistical effects from outliers; we chose not to focus on outlet stations, because for flood applications, it is important to have a reasonable representation of high flow at every stage of the river.
We also analysed only stations located within major basins worldwide according to the DDM30 network. Grid cells in the DDM30 and CaMa-Flood's network corresponding to each GRDC station were determined through a semi-automatic procedure described in supplementary text C. In total, this selection procedure led to a set of 1701 stations with monthly data (figure  1 ) and 1205 stations with daily data. These stations belong to 198 (170 for stations with daily data) out of 321 major basins according to DDM30. These 198 basins sampled by at least one selected station cover 70 million km 2 (47% of global land area) area and host 3. 8 billion people (55% of 2010 global total according to Gridded Population of the World, version 4, http://sedac. ciesin. columbia. edu/). Of all the selected GRDC stations, 538 (526 for daily) were identified as managed (i.
e. having a managed upstream area), and 1130 (646 for daily) as (near-) natural, and the rest has no information regarding upstream area management. While most models used the DDM30 drainage direction dataset provided in ISIMIP, different networks were used in CLM and PCR-GLOBWB (the latter use an independently adjusted version of DDM30). We nevertheless include them in the analysis for the purpose of maximizing the sample size while minimizing the impact of compromised consistency. For these two models we followed a similar procedure as with DDM30, keeping only stations with no more than 30% (approximately one grid cell for the smallest catchment) difference in upstream area with GRDC, and applying manual correction separately. This led to about 10% (40%) less corresponding cells selected for PCR-GLOBWB (CLM), respectively.
Number of GRDC stations Based on the location of the stations in the 198 major river basins, we then computed the average performance metrics for each GHM and the performance difference between GHM and CaMa-Flood over every river basin. We considered the stations as samples of the rivers and computed the basin averages without weighting, and it is worth noting that the sampling may not be representative especially in data sparse basins. Multi-model ensemble mean of the performance differences were also calculated, for daily and monthly values respectively. For biases, where values close to 0 indicate good agreement with observation, performance difference was computed based on absolute values (table 1), so that any positive difference indicates an improvement. Finally, multimodel mean and best-model performance metrics were computed to show where the models can simulate a reasonable discharge compared to the GRDC observations.
Further improvement can be achieved by improving routing alone, for example, CaMa-Flood currently uses relatively low resolution in the terrain data north of 60°N for generating routing direction, limiting the accuracy of simulations in boreal regions; implementation of a higher quality topography input is currently under development. Neither evaporation over floodplains nor transmission loss is considered in CaMa-Flood, which could possibly lead to an overestimation of peak amplitude in basins under hot climate, such as in the inner Niger delta (Zwarts We also found that CaMa-Flood routing often shows considerable improvement in the cases where NSE<0 or biases >100% (results not shown), beyond which the performance metrics were capped for the analyses. It could be interesting from a model development perspective to also examine such improvement; however, regarding on the potential of model applications limited value can be gained if the improved NSE is still below 0.
Acknowledgments The  Global Runoff Data Centre  ( GRDC, 56068 Koblenz, Germany ) are thanked for providing the observed discharge data. This work has been conducted under the framework of phase 2 of the  Inter-Sectoral Impact Model Intercomparison  Project (ISIMIP2a: www. isimip. org). The work was supported within the framework of the  Leibniz Competition  ( SAW-2013 P IK-5 ), from  EU   FP7  project  HELIX  (grant no. FP7-603864-2 ), and by the  German Federal Ministry of Education and Research (BMBF , grant no. 01LS1201A1  and  01LS1201A2 ). G Leng  and  M Huang  were supported by the  Integrated Assessment Research program  through the Integrated Multi-sector,  Multiscale Modeling  ( IM3 ) Scientific Focus Area (SFA) sponsored by the  Biological and Environmental Research Division of Office of Science, US Department of Energy (DOE).
The  Pacific Northwest National Laboratory  (PNNL) is operated for the US DOE by  Battelle Memorial Institute  under contract  DE-AC05-76RL01830. P Ciais  and  M Guimberteau  were supported by the  European Research Council Synergy  grant  ERC-2013-SyG 610028-IMBALANCE-P. The publication of this article was funded by the  Open Access Fund of the Leibniz Association. We thank the two reviewers for their constructive and insightful comments.
All the wavefield simulations are performed using CSEM, and anelastic behaviour is introduced by means of standard linear solid models. SEM simulations provide high accuracy in addressing (de)focusing effects due to elastic heterogeneities, thus providing a realistic basis for the comparison of the relative performance of the three misfit functionals considered. We have presented 1-D and 3-D synthetic tests in a period range of 60-400 s, as was considered in building previous upper-mantle  seismic velocity models in our group. In the 3-D tests, inversions were conducted using a realistic and limited ray coverage replicated from a subset of Z-component Rayleigh wave data (R 1 , R 2 , XR 1 , XR 2 ) used in building the SEMUCB-WM1 global elastic model. Using the synthetic waveform data set computed for target models with Q μ and, in some tests, V S anomalies, we tested the three misfit functionals for recovering the anomalies starting with a spherically symmetric Earth model.
A C K N O W L E D G E M E N T S The authors thank Gabi Laske and three anonymous reviewers for their constructive comments on the manuscript. This work was supported by the European Research Council under the EC's 7th Framework Programme (FP7-IDEAS-ERC)/ERC Advanced Grant WAVETOMO. The computations were performed on OCCIGEN of the HPC national facility CINES in France, on EDISON and CORI at the National Energy Research Scientific Computing Center (NERSC), USA, and the local cluster (MALBEC) of Institute de Physique du Globe de Paris. BR acknowledges support from NSF grant EAR-1497229.
In the case of NACT, coupling across multiplets k = k is considered to first order, and the expression for u(t) becomes where A kk is the amplitude term of the across branch multiplet coupling contribution, and the integral is calculated along the great circle path containing source (S) and receiver (R). The expressions for Q 1 kk and Q 2 kk are given in the appendix A of  Li & Romanowicz (1995). Also, δα kk (θ, φ) = 1 ω k + ω k a 0 δ 1 Q μ (r, θ, φ) K Qμ kk (r ) + δ 1 Q κ (r, θ, φ) K Qκ kk (r )r 2 dr (A6) scenarios until they break down Q μ inversions. In this test, real noise records collected from the stations considered are added to the synthetic data after scaling their root-mean-square amplitudes by a factor defined as the percentage of the peak amplitude value in the full 3 hr synthetic record used as data.
, 2017). More surprisingly, only few methods have addressed such an issue from a research perspective. More authors have focused on forest delineation  (Eysn et al. , 2012) , that do not convey information about the tree species and their spatial distribution. The analysis of the lidar and multispectral data is performed at three levels in  (Tiede et al. , 2004) , following the hierarchy of the nomenclature of forest LC species database. The multi-scale analysis offers the advantage of alleviating the standard limitations of individual tree crown detection, and of retrieving labels related here to forest development stage. Nevertheless, the pipeline is highly heuristic and under-exploits lidar data. Besides, significant confusions between classes are reported. The automatic segmentation of forests in  (Diedershagen et al.