dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments. This work is based on observations taken at ESO/VLT in Paranal and we would like to thank the  ESO  staff for their assistance and support during the MUSE GTO campaigns. This work was supported by the  Swiss National Science Foundation. We thank  J. Woo  and  M. Maseda  for their comments on the draft. SC gratefully acknowledges support from  Swiss National Science Foundation  grant  PP00P2 163824. JR acknowledges support from the  ERC  starting grant 336736-CALENDS. JB acknowledges support by  Fundação para a Ciência e a Tecnologia (FCT)  through national funds ( UID/FIS/04434/2013 ) and Investigador FCT contract  IF/01654/2014/CP1215/CT0003. , and by  FEDER  through  COMPETE2020  ( POCI-01-0145-FEDER-007672 ). TC acknowledges support of the  ANR FOGHAR  ( ANR-13-BS05-0010-02 ), the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements davenir" French government program  managed by the  ANR.
Results Allelic diversity and genetic differentiation. We identified 17 DQA alleles (accession numbers MH346126-MH346142) and 26 DQB alleles (accession numbers MH346143-MH346168) in 142 hares (L. capensis) from Tunisia with a total length of 228 and 222 bp, respectively. Among them, only three DQA alleles (Lecp-DQA*04, Lecp-DQA*05, Lecp-DQA*06 corresponding to Leeu-DQA*02, Leeu-DQA*03, Leeu-DQA*09 in L. europaeus, respectively) were previously described in L. europaeus. No insertions, deletions, or stop codons were detected in either DQA or DQB sequences. The nucleotide sequence of each allele in both loci translated into a different amino-acid sequence (Figs  1  and 2 ). Allele frequencies and basic diversity parameters for the two MHC class II exon 2 loci in the different populations as well as the overall values are displayed in Table  1.
Forward primers for both gene segments were 6'FAM-labelled and reverse primers were NED-labelled. We selected individuals with diverse SSCP patterns, representing all identified alleles, to investigate sequence variation at both DQA and DQB loci, respectively. The genes were amplified using the same procedure as in Goüy de Bellocq et al. 42  and Smith et al. 43  , but using non-labelled primers. As homozygous states were detected by SSCP analysis for all alleles in both gene segments, we directly sequenced the PCR product of the corresponding samples. Among all currently analysed samples, only eight were not screened with SCCP. These eight samples were directly sequenced using the same primers and the program phase  64  was used to separate alleles in heterozygous samples. For comparison between MHC and neutral genetic variation, genotype data of 14 microsatellite loci of 134 individuals out of the specimens analysed in the current study were used from a previous study  38.
Data analysis. Population genetic parameters. Sequences were edited and aligned by eye using the BioEdit v. 7. 2. 5 program©, 1997-2013  65. Allele frequencies, mean number of alleles (A), observed (Ho) and expected (He) heterozygosity were calculated for each locus and for each regional population (NT, CT, ST) with GENETIX  66. Tests of deviation from Hardy-Weinberg equilibrium and linkage disequilibrium were calculated using GENEPOP 4. 0  67  and GENETIX  66  , respectively. The allelic numbers for the cape hare sequences were assigned according to the guidelines of Klein et al. 68  and begin at Lecp-DQA*04 and Lecp-DQB*01 for DQA and DQB, respectively. The FSTAT vers. 2. 9. 3. 2. program  69  was used to calculate locus-specific values of allelic richness (Rs) based on a rarefaction approach to account for different sample sizes.
Allelic richness calculation was based on minimum sample size of 29 and 18 individuals for MHC and microsatellite loci, respectively. Differentiation between regional populations was determined by calculation of standardized pairwise F ST (10 000 permutations) in GENETIX  66  and also Jost's D (Dest)  70  as a more robust measure than F ST using GenAlEx 6. 5  71. In addition, an analysis of molecular variance (AMOVA) from the MHC data was calculated for the three eco-regions (NT, CT, ST) using the Arlequin 3. 5 program  72. The same genetic parameters were also estimated for genotypes at 14 microsatellite loci obtained earlier for the same individuals  38  for NT, CT, ST. Furthermore, in order to identify the contribution of the different MHC alleles to the population structure, a principal components analysis (PCA) was performed in ADE-4  73  , using the genotype data at both MHCII loci.
To check for the presence of recombination in the alignment of our sequences, we first used PERMUTE (included in the software package OMEGAMAP  78  ), which computes three different statistics (r 2 , D, and G4) based on the correlation between physical distance of sites and their linkage disequilibrium. The RDP, GENECONV, MAXCHI, CHIMAERA and 3SEQ implemented in the RDP3 package  79  were all employed to detect recombination breakpoint locations. In the latter analyses we used a significance level of 0. 05. We also calculated the population scaled recombination rate ρ and mutation rate θ by using LDhat recombination rate scan implemented in theRDP3 package  79. Finally, the GARD method 80 was employed to search for possible recombination partitions. Detecting positive selection. In order to infer evidence of positive selection, three different methods that compare the rates of synonymous and non-synonymous substitutions separately for every single codon were used.
We chose to use HYPHY  81  as implemented in the DATAMONKEY webserver (http://www. datamonkey. org/83; last accessed 30 th January 2017), OMEGAMAP  78  and CODEML (PAML 4 package  82  ). The first two software packages have the important advantage of taking recombination into account whereas CODEML does not account for that. The OmegaMap program is based on a Bayesian population genetics approximation to the coalescent theory and generates means and credible intervals for the selection parameter (dN/dS = ω) and recombination rate (ρ = 4Nr) for each codon (N and r represent the effective population size and the per codon rate of recombination, respectively). We used the same parameters as in Smith et al. 43. Two Markov chain Monte Carlo runs of 250 000 iterations (25 000 iteration burn-in) for DQA and 400 000 iterations (40 000 iteration burn-in) for DQB on population allele frequencies at each locus, were compared for convergence.
However, mean annual temperature (r = -0. 796) and annual precipitation (r = 0. 831) correlated too closely with latitude to add them in the model, and latitude has the clearest effect of these three variables on the response variable (see Supplementary Table  S5 ). Thus, hereafter latitude will be interpreted as surrogate for climate, acknowledging that other abiotic factors like soil or wetness also might be covered by this variable. Mean annual temperature, annual precipitation and altitude were obtained from WORLDCLIM data set for 2. 5 min intervals (Version 1. 4, http://www. worldclim. org/bioclim. htm) and were automatically extracted using DIVA-GIS ver. 7. 5. We also run a multinomial log-linear model with individual heterozygosity in DQA and DQB loci as response variable and the same geographic variables as independent variables, expecting a higher level in NT (in parallel to allelic richness).
The model syntaxes were as follows (using the package nnet): We used an information-theoretic approach and techniques of model selection and multi-model inference 36 (i. e. , model ranking and model averaging) to obtain the best model explaining the data. Based on the global models (see syntaxes above), we run all possible models including the respective null-models and calculated the probability of each possible model being the best model using the Akaike weight based on AICc values (i. e. , corrected for small sample sizes). Adding up the weight of all models in which a given variable is present gives the "Relative Variable Importance", RVI, Table  5 ). RVI is an estimate of the absolute probability for a variable of being in the best model explaining this particular dataset. For each variable we also calculated the so-called delta deviance, i.
The OPC-N2 is an optical particle counter designed by Alphasense (www. alphasense. com). It measures particle size distribution from 0. 38 to 17 µm within 16 channels every 6 seconds with a flow rate of 1. 2 L/min. Another particle counter, mini-WRAS, developed by GRIMM, has been also mounted in MAAMS. The instrument measures the particle size distribution in 40 channels, from 10 nm to 25 µm every minute with a flow rate of 1. 2 L/min. A meteorological probe mounted on the van is used to measure the temperature, the relative humidity and the atmospheric pressure. Finally, MAAMS is equipped with a GPS navigation system, to map the mobile measurements and a camera, used to record sky cloudiness. Data is transferred to LOA database using a 3G communication and is processed routinely.
Statistical Analyses All data analyses were performed on IBM SPSS Statistics 20. A significance level of 0. 05 was used throughout the analysis process. Descriptive statistics were computed from raw scores on each questionnaire for each participant. For any mean comparison, the variance equality was checked for performing parametric statistics. Regarding pedagogical efficacy, results were treated in two steps for catching the short-term effects of KidBreath (initial assessment vs. mid-point assessment) and its long-term effect (initial assessment vs. end-point assessment). For each step, KidBreath's pedagogical efficacy was evaluated using a twoway mixed ANOVA [Time x Disease questionnaire] with pre-post intervention effect on children's AKQ and DKQ scores. Complementary analyses were performed using an independent t-test with pre-post intervention effect for both disease questionnaires.
Sequence-exploring KidBreath Regarding the total time spent to play KidBreath, children fulfilled less learning activities than those in study 1. Indeed, significant differences in log time recorded were found [(m study1 = 3312" vs. m study2 = 1424"); (t (29) = 3. 267; p < 0. 05)]. Regarding KidBreath exploration strategies, the learning paths of children were analyzed using Circos  (Krzywinski et al. , 2009). This software allows, when informed by KidBreath's session logs, to visualize interface transitions (when users change activity within KidBreath) and to produce a graphical output summarizing the different behaviors observed in the form of curved red et blue lines (see Figure  10 ). One transition begins on the yellow part of an activity label and ends on the brown part of another activity label.
Limitations First, the sample of participants was small (n = 14 in study 1; n = 15 in study 2). Nevertheless, in similar studies of health serious games evaluations, none of them took place at home but rather within a hospital, following a very controlled procedure with a larger number of participants, but restricting the tool's uses and spontaneous patients' behaviors. We focused on the potential impact of the tool's use in a non-controlled environment, which is the most representative of ecological issues. Second, in a related way, due to real-life setting of studies, many external family constraints have been challenging (e. g. , lack of time with extra-curricular activities, holidays etc. ). To keep participants involved, it could be interesting to study the monitoring effect of learning progress by the adding in KidBreath of a module for children, their parents and their practitioner for self-and hetero-monitoring purposes.
Five, only ZPDES was tested as ITS added into KidBreath. Further studies could investigate the impact of other learning optimization algorithms and variants of ZPDES implemented in KidBreath via comparative studies, as Clement et al. 's did  (Miller, 1987; González-Brenes and Mostow, 2012). To conclude, these pilot studies supported the viability of our participatory design-based methodology to enhance serious game design for health education according to both the content and the personalization display (ITS), even if improvements have to be added (ZPDES operationalization, hyper-parameter implementation, new module learning monitoring. ). The expected success was not fully achieved for ZPDES value but some results were encouraging such as gains in time for learning and in the number of contents for achieving a given learning level.
ACKNOWLEDGMENTS We would like to thank  Catherine Argillier ,  CEO of ItWell Company , which permitted to implement KidBreath application. We would like to thank  Dr. Marc Sapène , president of CETBA association, which allowed its patients and physicians network to carry out the two user studies. Obviously, we would like to thank all the 31 patient families who participated of the two user studies. This work forms part of a thesis project which is sponsored by  ItWell Company, Inria Research Institute and French Ministry of Higher Education and Research.
It is therefore necessary to use high angular resolution tSZ observations over a large field of view (FOV) in combination with current X-ray and optical data to describe the ICM thermodynamic properties up to high redshift and study potential variations of the tSZ-mass scaling relation characteristics with redshift. The New IRAM KIDs Array 2 (NIKA2) is a 6. 5 arcmin field of view camera designed to observe the sky at 150 and 260 GHz, with an angular resolution of 17. 7 and 11. 2 arcsec respectively (see  Adam et al. 2018). The NIKA2 camera has successfully been installed and commissioned at the Institut de Radio Astronomie Millimetrique (IRAM) 30-m telescope. The NIKA2 characteristics enable us to map the ICM of high-redshift galaxy clusters with a quality similar to the one reached by current Xray observatories such as XMM-Newton (e.
Observation at the IRAM 30-meter telescope with NIKA2 This section is dedicated to the description of the NIKA2 observations of PSZ2 G144. 83+25. 11 made in April 2017 and to the raw data analysis performed to produce the tSZ surface brightness maps at 150 GHz and 260 GHz. We first recall some key elements of the thermal SZ effect for the reader's convenience.
Observing conditions, scanning strategy, calibration, and data reduction The NIKA2 tSZ observations of PSZ2 G144. 83+25. 11 were made in April 2017. This is the very first tSZ observation of a galaxy cluster conducted with the NIKA2 instrument. This section aims at describing the observation conditions, scanning strategy, calibration method, and map making procedure. The weather conditions at the time of the observations were quite poor with a mean opacity of 0. 30 at 150 GHz, 0. 48 at 260 GHz, and a rather unstable atmosphere. The cluster field was observed for an effective observing time of 11. 3 hours at a mean elevation of 41. 4 •. The pointing center was chosen to be (R. A. , Dec. ) J2000 = (06:47:50. 5, +70:14:53. 0), which corresponds to the position of the tSZ surface brightness peak of the MUSTANG observations of PSZ2 G144.
The removal of both atmospheric and electronic correlated noise has been done independently for the two bands by using an iterative procedure. This method is different from the one used in previous NIKA analyses and aims at recovering extended signal far from the tSZ peak without using a mask in the raw data analysis. At the end of each iteration, the signal detected with a significance higher than 3σ on the map is removed from the raw data before the common mode estimation in the next iteration. The latter is computed by using blocks of highly correlated detectors to minimize the amount of residual spatially correlated noise in the final map. As shown in Fig. 1  left panel, the recovered tSZ signal and cluster extension at the end of the first iteration are rather weak because the correlated noise template computed as the mean of the time ordered information (TOI) of all detectors includes the cluster signal.
Furthermore, this iterative procedure allows us to avoid ringing in the final tSZ map as the amount of cluster signal biasing the correlated noise template in the final iteration is negligible. Removing correlated noise from the raw data induces a filtering of the tSZ signal at scales that are larger than the NIKA2 field of view (6. 5 arcmin). The NIKA2 processing circular transfer function at 150 GHz has been computed using simulations as described in  Adam et al. (2015). As can be seen on the left panel of Fig. 3 , it is fairly flat and close to unity at scales smaller than the NIKA2 FOV and vanishes rapidly for larger scales. The residual correlated noise in the final maps has been characterized following the procedure described in  Adam et al. (2016). Null maps are computed by the semi-difference of the coadded maps from two equivalent subsamples of processed scans.
Acknowledgements. We would like to thank the  IRAM  staff for their support during the campaigns and the anonymous referee for helpful comments. The NIKA dilution cryostat has been designed and built at the  Institut Néel. In particular, we acknowledge the crucial contribution of the Cryogenics Group, and in particular  Gregory Garde ,  Henri Rodenas ,  Jean Paul Leggeri , and  Philippe Camus. This work has been partially funded by the  Foundation Nanoscience Grenoble , the  LabEx FOCUS   ANR-11-LABX-0013 , and the  ANR  under the contracts " MKIDS", "NIKA" , and  ANR-15-CE31-0017. This work has benefited from the support of the  European Research Council Advanced Grants ORISTARS  and  M2C  under the  European Unions Seventh Framework Programme  (Grant Agreement Nos. 291294  and  340519 ).
We acknowledge funding from the  ENIGMASS French LabEx (R. A. and F. R. ), the  CNES post-doctoral fellowship program (R. A. ) , the  CNES doctoral fellowship program  (A. R. ), and the  FOCUS   French LabEx doctoral fellowship program  (A. R. ). R. A. acknowledges support from the  Spanish Ministerio de Economía and Competitividad (MINECO)  through grant number  AYA2015-66211-C2-2.
Outputs from ORCHIDEE-MICT, when forced by two climate input datasets, are extensively evaluated against (i) temperature gradients between the atmosphere and deep soils, (ii) the hydrological components comprising the water balance of the largest highlatitude basins, and (iii) CO 2 flux and carbon stock observations. The model performance is good with respect to empirical data, despite a simulated excessive plant water stress and Published by Copernicus Publications on behalf of the European Geosciences Union. 122 M. Guimberteau et al. : ORCHIDEE-MICT, a LSM for the high latitudes a positive land surface temperature bias. In addition, acute model sensitivity to the choice of input forcing data suggests that the calibration of model parameters is strongly forcingdependent. Overall, we suggest that this new model design is at the forefront of current efforts to reliably estimate future perturbations to the high-latitude terrestrial environment.
, 1999) , which may impose powerful constraints on plant growth. Siberian and Canadian boreal forests have thus been shown to experience water stress, with ratios of surface sensible to latent heat flux of up to ∼ 2  (Jarvis et al. , 1997; Baldocchi et al. , 1997; Schulze et al. , 1999)  causing further heating of the near-surface atmosphere. These large seasonal shifts of the high-latitude water balance -how water input from precipitation is shared between changes in water storage in snow, ice and soil moisture, and balanced against losses from evapotranspiration, sublimation and river discharge -can now be better assessed and evaluated using state-of-the-art observation datasets. In addition, in terms of realistic process representation, land surface models (LSMs) focusing on high-latitude phenomena require the inclusion of the following non-exhaustive series of pivotal hydrological and biogeochemical interactions.
Since the comprehensive description of the ORCHIDEE model by  Krinner et al. (2005) , the model has gone through major modifications and improvements; we present here the major ones linked to highlatitude processes. ORCHIDEE-MICT is evaluated over the last 2 to 3 decades (depending on the variable) against empirically generated datasets. Against these, we are able to evaluate model performance regarding the distribution of permafrost and the effect of snow on soil thermics (mechanisms 1 and 2); the different components of the water cycle over a wide range of high-latitude basins (mechanism 3); plant primary productivity as constrained by high-latitude conditions (mechanisms 3 and 4); and replication of soil carbon stocks and feedback dynamics (mechanisms 5 and 6).
The second physical factor contributing to total runoff is free gravitational drainage at the bottom of the soil. The runoff routing module  (Polcher, 2003; Ngo-Duc et al. , 2005; Guimberteau et al. , 2012)  aggregates surface runoff and drainage produced at a 30 min time step to calculate daily flow between grid cells and discharge to the ocean. Grid cells are subdivided into basins in which water is transferred through a series of linear reservoirs along the drainage network, derived from a 0. 5 • resolution dataset  (Vörösmarty et al. , 2000; Oki et al. , 1999). In a given basin, a "slow" reservoir collects drainage water, while a "fast" reservoir collects surface runoff, each with different linear response timescales. Corresponding outflows are transferred to the stream reservoir of the downstream basin.
The process is fully detailed in  Guimberteau et al. (2012). The routing scheme includes a parameterization of floodplains (d  'Orgeval et al. , 2008; Guimberteau et al. , 2012) , the maximum extent of which is prescribed by the GLWD (Global Lakes and Wetlands Database) map  (Lehner and Döll, 2004). In grid cells with flooded areas, river discharge from upstream basins is diverted to a floodplain reservoir, which then feeds a delayed return flow back to the stream reservoir of the basin.
Fires The SPITFIRE (SPread and InTensity of FIRE) prognostic fire module, which has been previously integrated into and calibrated for a standard version of ORCHIDEE  (Thonicke et al. , 2010; Yue et al. , 2014) , was merged into ORCHIDEE-MICT. Ignitions were re-calibrated using the GFED4s dataset (http://www. globalfiredata. org/data. html) by using region-specific scaling factors (see Table  S3 ) and the exclusion of cropland fires to ensure that simulated mean annual burned area for 1997-2013 was equal to that of the GFED4s dataset. Note that this method only calibrated for mean annual regional burned area, and that simulated latitudinal distributions and grid cell spatial patterns of burned area and fire carbon emissions, and their interannual and seasonal variabilities, could still be compared with observationbased data.
Simulation setup Two separate runs using different climate forcing input data -CRUNCEP v7 (hereafter CRUNCEP) and GSWP3 -were performed with ORCHIDEE-MICT for the terrestrial Northern Hemisphere (> 30 • N) at 1 • spatial resolution. Both sets of runs encompass the 20th century and the beginning of the 21st century, and were preceded by separate spin-ups for each climate dataset, forced by fixed pre-industrial conditions of atmospheric CO 2 (286 ppm) and vegetation maps. The dynamic vegetation model is de-activated throughout both runs. In order to accumulate soil organic carbon in the model, which requires substantial computing time before reaching near-equilibrium in the presence of the slow mixing processes described in Sect. 4. 1, the spin-up procedure comprised three steps. (1) The full ORCHIDEE-MICT model was forced by looped climate fields over the period  for 100 years to reach equilibria for soil temperature, soil moisture, vegetation productivity, soil carbon inputs from dead plants, etc.