dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  J. Kirsch ,  H. Schliemann , the  Regine von Ramin Laboratory (DRFZ) , and  A. B. Koehler  ( Max Planck Institute of Infection Biology ) for technical help,  K. Rajewsky  ( MDC, Berlin ) for VH12,  K. Hayakawa  ( Fox Chase Cancer Center, USA ) for VH11 and Vk14 antibodies,  H. Wardemann  ( DKFZ, Heidelberg ) for Ig expression plasmids,  S. Nutt  ( WEHI, Australia ) for Prdm1eGFP mice,  H. J. Fehling  ( Ulm ) for ROSA-STOP-tdRFP mice, and  D. Vignali ,  St. Jude Children's Research Hospital , and IGBMC for Lag3 À/À mice. S. F. lab is supported by  ERC   PREG-LAB 647696 ,  AXA Chair Translational Immunology ,  Agence Nationale de la Recherche  ( ANR-16-CE18-0007-01 ), Chair of  Excellence (Universite Śorbonne Paris Cite) , and  Infect-ERA  project  ABIR  ( 031A403 ),  Deutsche Forschungsgemeinschaft  ( TRR130 ,  FI 1238/1-2 ).
DATA AND SOFTWARE AVAILABILITY The Affymetrix gene array data have been deposited at NCBI GEO depository and are accessible with the accession number GSE103458, or using the link https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE103458 The RNaseq and DNA methylation data have been deposited at the European Nucleotide Archive (ENA), and will be accessible with the accession number PRJEB22138, or using the links. http://www. ebi. ac. uk/ena/data/view/PRJEB22138<https://deref-web-02. de/ mail/client/dAlzMDs2lmY/dereferrer/?redirectUrl=http%3A%2F%2Fwww. ebi. ac. uk%2Fena%2Fdata%2Fview%2FPRJEB22138> e9 Immunity 49, 120-133. e1-e9, July 17, 2018.
RNaseq analysis Adaptor removal and trimming of low quality ends (phred score = 20) of FastQ format reads was performed using Trim Galore! (version 0. 3. 3) (http://www. bioinformatics. babraham. ac. uk/projects/trim_galore/). Alignment to the mm10 reference genome was done using 2-step STAR alignment  (Dobin and Gingeras, 2015). PCR duplications were determined using MarkDuplicate (version 1. 115) from Picard tools (http://broadinstitute. github. io/picard/) and quality control was performed using RNA-seQC  (DeLuca et al. , 2012). Subsequently, featureCounts  (Liao et al. , 2014)  was used to count reads mapping to the genes from Gencode annotation (vM2) (https://www. encodeproject. org/files/gencode. vM2. annotation). Pairwise differential expression was analyzed using the R package edgeR  (Robinson et al.
Design and Procedures To examine seasonal changes in body composition across various playing positions amongst elite European professional soccer players, data was collected across a 46-week period, encompassing the complete schedule of the 2014-2015 competitive season. To correspond with the periodised training structure implemented, body composition analysis was © Editorial Committee of Journal of Human Kinetics performed across 4 phases of training, initiating in June (pre-season) and concluding in May (end of season) and was inclusive of 5 specific data collection time points as shown in Figure  1. Body compositional assessment was supervised and performed by the sport science and medical department of the club with testing performed by the same ISAK certified practitioner throughout the seasonal investigation period in accordance with the standardised techniques adopted by the International Society for the Advancement of Kinanthropometry (ISAK).
On the contrary, lean mass was significantly higher in T1 compared to T0 in all playing positions. Except for Journal of Human Kinetics -volume 61/2018 http://www. johk. pl Defenders, calf girth increased from T0 to T1 (p < 0. 01). Calf girth and lean mass were higher in T2, T3 and T4 compared to T0 and T1. Values in T4 were slightly lower than in T2 and T3. Midfielders had less fat mass than all the other playing positions. Additionally, midfielders were found to be leaner than forwards and defenders in all data collection time point sessions. Defenders showed higher values in calf girth and lean mass than midfielders and forwards. Sites-4, Sites-7, Sites-8 and fat mass decreased in all playing positions from T0 to T4, however, values at the end of the season were slightly higher than in T2 and T3.
The SeaSWIR dataset consists of 137 ASD (Analytical Spectral Devices, Inc. ) marine reflectances, 137 Total Suspended Matter (TSM) measurements and 97 turbidity measurements gathered at three turbid estuarine sites (Gironde, La Plata, Scheldt). The dataset is valuable because of the high quality measurements of the marine reflectance in the Short Wave InfraRed I region (SWIR-I: 1000-1200nm) and SWIR-II (1200-1300nm) and because of the wide range of TSM 15 concentrations from 48 mg L-1 up to 1400 mg L-1. The ASD measurements were gathered using a detailed measurement protocol and were subjected to a strict quality control. The SeaSWIR marine reflectance is characterized by low reflectance at short wavelengths (< 450 nm), peak reflectance values between 600 and 720 nm and significant contributions in the Near InfraRed (NIR) and SWIR-I parts of the spectrum.
15 Most of the measurements from the SeaSWIR dataset were collected from fixed pontoons along the side of the rivers/estuaries. These pontoons allow to capture a large variation of TSM concentration and T during the daily tidal cycle and allow to make high quality reflectance measurements. In particular, the verticality of the irradiance sensor or horizontality of the ASD reflectance plaque can be much better controlled than on a ship. The range of azimuth viewing angles on such pontoons can be limited because of shadow/reflection by the structure itself. 20.
Data collection At all test sites concomitant water reflectance, turbidity and TSM measurements were made (https://doi. org/10. 1594/PANGAEA. 886287). Table  2  provides an overview of the measurements performed at each 10 campaign and station. The reported number of measurements refers to the remaining number of measurements after quality control. The measurement methodology is described in detail below. the spectrum. The VNIR spectrometer has a spectral resolution of approximately 3 nm at around 700 nm. The spectral resolution in the SWIR varies between 10 nm and 12 nm. All measurements were performed using a fore optics lens limiting the field-of-view to 8 degrees. Each individual measurement that the ASD makes is an average of 40 scans. As the ASD has only one radiometer, three consecutive measurements have to be made to obtain the downwelling irradiance, the total upwelling radiance and downwelling sky radiance.
When Rw_stdev(750)> 0. 01 the measurement is discarded • After subtracting 1350 nm an inspection is performed of remaining reflection between 1500 and 1700 nm which could be an indication of problems with skylight reflection or skylight which is not completely white. All measurements in this wavelength range with Rw > 0. 005 are removed. 15 The SeaSWIR dataset contains all water reflectance measurements retained after quality control, as well as the downwelling irradiance (average of E d1 (0+), E d2 (0+), E d3 (0+) ), the downwelling sky radiance (average of L sky1 (λ), L sky2 (λ), L sky3 (λ) , L sky4 (λ) L sky5 (λ) L sky6 (λ) L sky7 (λ) L sky8 (λ) L sky9 (λ) ) the total upwelling radiance (average of L sky1 (λ) L sky2 (λ) L sky3 (λ) L sky4 (λ) L sky5 (λ) L sky6 (λ) L sky7 (λ) L sky8 (λ) L sky9 (λ) ) and all standard deviations.
The sampling interval of the TRIOS is approximately 3. 3 nm and the spectral width isabout 10 nm. Position is measured simultaneously by GPS. Data were 30 acquired with the MSDA-XE software and radiometrically calibrated using nominal calibration constants. Calibrated data for 0 d E + , 0 sea L + , and 0 sky L + are interpolated to 2. 5 nm intervals and exported to MATLAB or R for further processing. Data is recalibrated using calibration updates from annual laboratory or factory calibrations. Full details of the data processing, including scan selection and averaging and quality control are described in  [Ruddick et al, 2006] , including its Web Appendix 1, except that a fixed value of 0. 0256 is used for the air-sea interface reflection coefficient in sunny conditions, 35 instead of the wind speed formulation, because of the fetch-limited surface wave field, as explained previously for the ASD data in section 4.
ASD and TRIOS reflectance 30 The SeaSWIR reflectance dataset is generally characterized by low reflectance values at short wavelengths (< 450 nm), peak reflectance values between 600 and 720 nm and significant contributions in the NIR and SWIR-I parts of the spectrum -see Figure  5. However significant variations exist among the different test sites. To further explain these differences, the water reflectance spectra for all data campaigns are displayed in Figure  5  from 350 to 1300nm. Beyond 1300 nm the pure water absorption is extremely high (>140 m -1 ) and there is no reflectance emerging from the water. In  Figure 5, 5 characteristic 35  spectral regions are defined between 350 and 1300 nm: A) between 350 and 600 nm, B) between 600 and 720 nm, C) in the NIR between 720 and 1000 nm, and finally regions corresponding to D) SWIR-I (1000 -1200 nm) and E) SWIR-II.
The spectra for the Scheldt (data campaigns Scheldt2010 and Scheldt2012, Figure  5a ) and 5b) have overall high reflectance values. In region A the spectra have a similar slope related to light absorption of CDOM and non-algal particles. In region B, maximum reflectance values are found up to 0. 1 and the spectra show clear changes in shape and magnitude. The influence of light absorption by Chlorophyll-a, for example, is clearly noticeable around 665 nm in the Scheldt2012 dataset. In region C, there is more variability in the magnitude of the spectra more related to the concentration of particles while spectral 5 variability is determined by pure water absorption  [Ruddick et al, 2006]. In SWIR-I a significant signal can be observed with maxima around 0. 02 (at 1071 nm). In SWIR-II the maximum reflectance is 0.
Conclusions The SeaSWIR dataset presents the first hyperspectral measurements of the water reflectance in the SWIR. Measuring water 5 reflectance in the SWIR was made possible by using an ASD spectrometer with one radiometer and a measurement protocol where the instrument is turned facing the sky, the water and the spectralon panel. Using this measurement protocol and consecutive post-processing it was shown, by intercomparison with TRIOS measurements, that high quality water reflectance measurements can be made with the ASD. It is however suggested to strictly follow the same procedure as outlined in this paper and apply a strict quality control. Measurements which passed quality control were all performed under 10 stable atmospheric conditions, being blue sky or completely overcast. Variable light conditions prohibit a correct retrieval of the water reflectance as lighting conditions change while turning the instrument and making the three individual measurements of downwelling irradiance, the total upwelling radiance and downwelling sky radiance.
It should also be noted that most measurements were made from fixed pontoons, which simplifies the measurement significantly and improves the quality of the data. Still, even when measuring on a pontoon, the location on the pontoon should be chosen 15 wisely and should not be close to any large obstacles which could alter the measurements of the downwelling irradiance and downwelling sky radiance. The quality of this dataset opens up the possibility of including the ASD in future measurement campaigns on water, for the development of algorithms as well for calibration and validation of satellite and airborne observations. This is important for current (e. g. Sentinel-3 OLCI) and future satellite missions (e. g. Environmental Mapping and Analysis Program -EnMAP, 20 Hyperspectral Precursor and Application Mission -PRISMA and Sentinel-10) with more spectral bands in the SWIR-I and SWIR-II.
Next to the water reflectance measurements, the SeaSWIR dataset also contains simultaneous TSM and turbidity measurements (using a HACH portable turbidity meter). The high quality of these measurements was confirmed by the standard deviation of the triplicates and the duplicate measurements performed in the LOV and VITO laboratories. As all measurements were made in estuaries during the tidal cycle, the TSM range of the SeaSWIR dataset is very high ranging from 48 mg L-1 up to extreme values of 1400 mg L-1. Hence, the SeaSWIR dataset is not only unique in terms of its spectral content but also in terms of its concentration range.
1 Specifically, we use the CosmoBolognaLib V4. 1, which implements OpenMP parallel algorithms both to measure the 2PCF (Section 3) and to perform Markov chain Monte Carlo sampling (Section 4). The CosmoBolognaLib is entirely implemented in C++ to provide a high-performance back end for all the computationally expensive tasks, while also supporting their use in Python to exploit the higherlevel abstraction of this language. The software and its documentation are freely available at the GitHub repository: https://github. com/ federicomarulli/CosmoBolognaLib.
Cluster sample The catalogue analysed in this work is drawn from the second public release of the XXL survey. The survey covers two extragalactic sky regions of ∼ 50 deg 2  in total, down to a point-source sensitivity of ∼ 6 • 10 -15 erg s -1 cm -2 , in the [0. 5-2] keV band (90% completeness limit;  Chiappetti et al. in press, XXL Paper XXVII). The data processing pipeline and subsequent cluster detection (extended sources) are described in detail in XXL Paper II; we briefly summarise below the main steps. In order to quantitatively deal with the completeness versus purity issues in the X-ray cluster selection process, we defined two samples of extended sources in the [flux -apparent size] parameter space from the X-ray pipeline output. This allowed us to compute accurate cluster selection functions by means of extensive simulations.
5), N is the number of comoving separation bins at which the 2PCF (ξ) is estimated, and the superscripts d and m stand for data and model. The likelihood is estimated at the mean pair separations in each bin (see Section 3. 2). The 2PCF model in redshift space, ξ m (s), is computed as ξ m (s) = (b e f f σ 8 ) 2 + 2 3 f σ 8 • b e f f σ 8 + 1 5 ( f σ 8 ) 2 ξ DM (αr) σ 2 8 , (7) where ξ DM (r, z) is the real-space dark matter 2PCF, which we estimated by Fourier transforming the power spectrum, P DM (k, z), computed with the software CAMB  (Lewis & Bridle 2002). Since the present cluster clustering analysis focuses at sufficiently large scales, the dark matter power spectrum can be safely computed in linear theory, P DM (k, z) P lin DM (k, z), with marginal effects on our results. The model depends on two free quantities, f σ 8 and b e f f σ 8 (since ξ DM ∝ σ 2 8 ), and on the reference background cosmology used both to convert angles and redshifts into distances and to estimate the real-space dark matter 2PCF (see e.
Acknowledgements. XXL is an international project based around an XMM Very Large Programme surveying two 25 deg 2 extragalactic fields at a depth of ∼ 6 × 10 -15 erg cm -2 s -1 in the [0. 5-2] keV band for point-like sources. The XXL website is http://irfu. cea. fr/xxl. Multi-band information and spectroscopic follow-ups of the X-ray sources are obtained through a number of survey programmes, summarised at http://xxlmultiwave. pbworks. com/. We thank  L. Chiappetti  and  M. Roncarelli  for helpful comments. F. M. acknowledges support from the grant  MIUR PRIN 2015  "Cosmology and Fundamental Physics: illuminating the  Dark Universe with Euclid". The Saclay group acknowledges long-term support from the  Centre national d'études spatiales (CNES). S. E. acknowledges financial contribution from the contracts  NARO15 ASI-INAF I/037/12/0 ,  ASI 2015-046-R.
aero. obs-mip. fr). It is a nonhydrostatic anelastic model, able to simulate the motion of the atmosphere, at scales ranging from synoptic (hundreds of kilometers) to microscale (tens of meters). Meso-NH has already been successfully used to simulate storms and tropical cyclones  (Barthe et al. , 2016; Chane-Ming et al. , 2014; Jolivet et al. , 2013; Nuissier et al. , 2005; Pantillon et al. , 2013 Pantillon et al. , , 2015 Pantillon et al. , , 2016)). Meso-NH has a large set of physical parameterizations to represent radiation, turbulence, clouds, precipitation, convection, chemistry, aerosols, etc. For the present study, the MNH-V5-3-0 package was used, and a simple set of physical parameterizations was chosen. The microphysics scheme  (Pinty & Jabouille, 1998)  was a single-moment bulk mixed-phase scheme predicting the mixing ratio of five microphysical species: cloud water, rain, cloud ice, snow, and graupel.
25 (where h is the depth). The domain has 32 vertical levels, with enhanced resolution near the surface. The original vertical grid stretching formulation described in  Song & Haidvogel (1994)  is used. The model is initialized and forced at its boundaries using the 3 day ECCO2 reanalysis (Estimating the Circulation and Climate of the Ocean: Phase 2,  Menemenlis et al. , 2008)  at 00 UTC on 1 January 2014. Figure  4a  shows the sea surface temperature from the ECCO2 analysis used as an initial condition for all the simulations. The colored squares represent the sea surface temperature (SST) measured by drifting buoys from the Journal of Advances in Modeling Earth Systems 10. 1002/2017MS001177 WMO (World Meteorological Organization) database at the same time. Overall, the sea surface temperature observed by the drifting buoys is well reproduced by the analysis.
Coupling Strategy OASIS (Ocean Atmosphere Sea Ice Soil,  Craig et al. , 2017)  was used to couple the oceanic, waves, and atmospheric models. The current OASIS3-MCT version is interfaced with the Model Coupling Toolkit (MCT,  Jacob et al. , 2005; Larson et al. , 2005)  and with the Spherical Coordinate Remapping Interpolation Package (SCRIP,  Jones, 1999). OASIS3-MCT is fully parallelized, allowing very good computational performance. The variables that are exchanged between the various models are shown in Figure  3. In SurfEx, turbulent fluxes at the sea surface are computed using the sea surface temperature (SST; K) and the two components of the sea surface currents (m s 21 ) from CROCO. These parameters are used to compute the stability of the atmosphere and the relative wind. The Charnock parameter (a; dimensionless)  (Charnock, 1955)  from WW3 is also needed.
Description of the Fully Coupled OWA Simulation The aim of this section is to describe and evaluate the fully coupled OWA simulation with available observations and the literature. Best-track data are used for the evaluation of the TC track and intensity (section 4. 1), buoys, and altimeters for the evaluation of the sea state (section 4. 3), and drifting buoys for the evaluation of the sea surface temperature (section 4. 4).
It increases by approximately 2 m in 22 h both in the observations and in the model. During this first stage, the model is in very good agreement with observations. From 22 UTC on 1 January to 06 UTC on 2 January, Bejisa is approaching La R eunion, and H s increases significantly (1. 5-2 m in 8 h). In the simulation, the maximum of H s is reached at 06 UTC on 2 January for the NRL_RN2 (5. 8 m) and NRL_RN4 Journal of Advances in Modeling Earth Systems 10. 1002/2017MS001177 (5. 5 m) buoys, and 2 h later for the CANDHIS_97403 buoy (5. 1 m). Note that the CANDHIS_97403 and NRL_RN2 buoys broke during the cyclone and did not measure the wave peak. For the remaining NRL_RN4 buoy, the model differs from the buoy data concerning the time and value of the peak of H s. It is 5 h in advance in the simulation (06 UTC on 2 January) compared to the observations (11 UTC on 2 January).
The maximum H s for the CANDHIS_97403 buoy is lower than for the other two buoys although Bejisa passed closer to this buoy. In fact, it was relatively protected by Pointe des Galets (red dot in Figure  6 ), and the plateau west of the buoy increased the friction, and consequently the wave breaking, leading to lower values of H s. To assess the simulated H s from the OWA simulation, a comparison is also made with the data from altimeters (in Figure  7  and Table  2 ). The merged and calibrated altimeter data set of Queffeulou and Croiz e-Fillon (  2017 ) is used in this study. Data from the Cryosat, Jason-2, and Altika Saral missions are available for the relevant period and region of interest and are presented in Figure  7b. Only the altimeter data in the offshore areas (further than 50 km from the coast) were taken into account, as comparisons with coastal buoy measurements indicated biases and errors in the altimeter measurements  (Shanas et al.
Sea Surface Temperature Sea surface cooling under a tropical cyclone is one of the air-sea coupling processes involved in tropical cyclone evolution. To evaluate the simulated sea surface cooling, the final SST of the OWA simulation is presented in Figure  8a. A colder SST region (26-278C) clearly appears along the track of Bejisa, while the SST in the surrounding waters is higher than 27. 58C north of 228S. The simulated SST is in good agreement with the data from the drifting buoys (WMO database) in the external part of the cold wake. For each buoy, less than 0. 258C difference is reported between the observation and the simulation. The difference in the SST between the end (18 UTC on 2 January) and the beginning (00 UTC on 1 January) of the simulation is shown in Figure  8b. Because of the asymmetric wind stress forcing, the SST cooling is highly asymmetric and is maximum on the left quadrants.
1002/2017MS001177 homogeneous over the TC. This is explained by the low resolution of the wave model used in the MARC database (0. 58). These H s differences imply strong differences in the instantaneous net sea salt fluxes, which are higher by a factor of 1. 5 in the OWA_MARC simulation (Figure  12e ) than in the OWA simulation (Figure  12a ). The OWA_FORC simulation highlights the problem of using a wave field from a model where the storm has a different position. Since the OWA_FORC simulation uses the H s from WW3 forced by the ECMWF operational high-resolution analysis (black contours in Figure  12f ), the significant wave height is consistent with the wind field of the ECMWF analysis and not with that of the Meso-NH simulation. In the ECMWF analysis, the center of the cyclone is located $50-60 km north-west of its actual position in the OWA simulation.
Acknowledgments This work was carried out in the framework of the  DiMe  project, which benefits from government support managed by the  Agence Nationale de la Recherche  under the  Investissement d'Avenir program  with the reference  ANR-10-IEED-0006-14. Additional financial support was also provided by the  Universit e de la R eunion through the federation Observatoire des Milieux Naturels et des Changements Globaux (OMNCG) of the Observatoire des Sciences de l'Univers de la R eunion (OSU-R). Thanks to the reviewers for their suggestions and comments that helped to improve the manuscript. We are grateful to the society NortekMed and the  DDE (Direction D epartementale de l'Equipement)  for providing buoy data. Philippe Caroff  and  S ebastien Langlade  ( M et eo-France/RSMC La R eunion ) are acknowledged for their detailed report about Bejisa cyclogenesis and evolution.
PoS(NEUTEL2017)072 CUORE is a ton-scale experiment approaching the data taking phase in Gran Sasso National Laboratory. Its primary goal is to search for the neutrinoless double-beta decay in 130 Te using 988 crystals of tellurim dioxide. The crystals are operated as bolometers at about 10 mK taking advantage of one of the largest dilution cryostat ever built. Concluded in March 2016, the cryostat commissioning consisted in a sequence of cool down runs each one integrating new parts of the apparatus. The last run was performed with the fully configured cryostat and the thermal load at 4 K reached the impressive mass of about 14 tons. During that run the base temperature of 6. 3 mK was reached and maintained for more than 70 days. An array of 8 crystals, called mini-tower, was used to check bolometers operation, readout electronics and DAQ.