dataesr/scientific-paragraphs-categorization huggingface dataset:
The two cases taken are p n = 2. 3 Pa, corresponding to a high recycling solution and p n = 8. 6 Pa where the divertor targets are partially detached. The latter condition is the most likely point at which future ITER operators will seek to operate in order to maintain manageable target heat flux densities  [10]. As shown in Fig. 4 , the divertor solution has very little effect on the main chamber SOL profiles, a reflection of the efficiency of the ITER divertor. Differences are much larger at the targets (Fig. 5 ) as would be expected. Once the plasma background is established, DIVIMP, a 2D kinetic Monte-Carlo global impurity transport code  [30]  is used to calculate Be transport. Impurity transport calculations can not be carried out in SOLPS, as the SOLPS plasma solution does not extend to the wall, and extrapolation is needed to compute the fluxes to the wall based on the density and temperatures at the last grid point.
Operating temperatures under burning plasma conditions can be high enough  [31]  to evaporate and sublimate Be and have a strong influence on the level of fuel deposition and retention. To estimate the target temperatures, the 1D heat transport code RACLETTE  [32]  is used (Fig. 8 ), in which a quasi-2D correction is included to account for the monoblocks  [33]. For the FW, a uniform temperature of 373 K is assumed. Increasing the temperature on the first wall would lead to a reduction in the retention rate, which would be more noticeable if there were significant co-deposits on the FW. In the future it will be important to investigate more realistic FW temperature profiles. The final step in the analysis chain is the application of WallDYN [7,34], a material surface evolution code, to calculate the impurity flux balance for each wall element to determine the local erosion/deposition.
Objective: To identify cross-border international registries for rare endocrine conditions that are led from Europe and to understand the extent of engagement with these registries within a network of reference centres (RCs) for rare endocrine conditions. Methods: Database search of international registries and a survey of RCs in the European Reference Network for rare endocrine conditions (Endo-ERN) with an overall response rate of 82%. Results: Of the 42 conditions with orphacodes currently covered within Endo-ERN, international registries exist for 32 (76%). Of 27 registries identified in the Orphanet and RD-Connect databases, Endo-ERN RCs were aware of 11 (41%). Of 21 registries identified by the RC, RD-Connect and Orphanet did not have a record of 10 (48%). Of the 29 glucose RCs, the awareness and participation rate in an international registry was highest for rare diabetes at 75 and 56% respectively.
Comparison of Endo-ERN, Orphanet and RD-Connect For endocrine conditions within the main thematic groups of Endo-ERN, 33 registries were identified through the above survey of Endo-ERN and the search of the Orphanet and RD-Connect databases (Table  2 ). Endo-ERN members identified 21 registries, Orphanet identified 20 and RD-Connect identified 7. Of the 27 registries identified via Orphanet or RD-Connect, Endo-ERN members reported 11 (41%) (Fig. 3 ). Amongst the international registries identified, several existed for discrete groups of conditions including Adrenal, Genetic Disorders of Glucose and Insulin Homeostasis and Growth and Obesity Syndromes.
Databases such as Orphanet and RD-Connect have been developed to facilitate greater awareness of registries for rare conditions and may also pave the way towards improving the quality of registries. However, it is important to note that current databases of rare disease registries such as Orphanet and RD-Connect did not have a record of several international registries that are currently led from Europe. This was noted particularly for rare disease registries led by industry. Given that rare disease registries have to enter their details on these databases, the discordance suggests a mutual lack of awareness between these databases and the rare disease registries of the respective platforms. It is possible that registries led by industry have been designed specifically for post-marketing surveillance of specific drugs for a fixed period with limited applicability to the wider community and long-term sustainability may not be a priority, and this may explain their lack of visibility on Orphanet and RD-Connect.
However, one of the most challenging aspects of developing an international detailed disease registry is the requirement of a common dataset based on a standardised data entry  (10). It is highly likely that these separate registries will have several fields that will be common to all. Thus, the existence of a group of local and national registries should be viewed positively as these registries can pave the way towards the development of a common dataset for an international registry. Another hurdle in the development of multinational registries has long been the existence of country-specific ethical regulations for the development and use of such registries. The recently issued General Data Protection Regulation (GDPR) by the EU provides a welcome opportunity to overcome these problems  (14).
Structure and morphology The Rietveld renement of the X-ray diffraction patterns of LiZn 2 Fe 3 O 8 ferrites obtained for Zn500 and Zn1100 are shown in Fig. 2 (a). The Rietveld FULLPROF program was used for the structural renements. 17 The XRD spectra conrmed the formation of a single-phase cubic spinel structure with a space group (Fd 3m) without any extra peaks of a secondary phase (ZnO or Fe 2 O 3 ) and there is a good agreement between the calculated and observed proles. As the sintering temperature increases, the peak width decreases due to an increase in crystallite size. The obtained Rietveld renement parameters for the two samples were computed and are given in Table  1. More details in the Rietveld analyses have been reported in our previous work. 18 Fitting parameters were found to be comparable to those of similar spinel ferrite samples.
The specic surface area decreased from 72. 6 to 27. 6 m 2 g À1 with the increase in the particle size. However, the high surface area value of LiZn 2 Fe 3 O 8 nanoparticles suggests that Zn500 nanoparticles could be used as sensors for detecting gases. 24 he high-resolution TEM image of a single grain (singlecrystalline nature) is shown in Fig. 2(d). The TEM image depicts that the particles are non-uniform in size and spherical in nature with some agglomerations. The average particle size estimated from the TEM image was 22 nm (inset of Fig. 2(d) ). We also carried out SEM studies. Fig. 2 (e) shows the typical SEM image of sample Zn1100. The average grain size increased to the micrometer range for this sample. It is worth mentioning that the average particle size is in agreement with the crystallite size deduced from the XRD data for the sample Zn500, which indicates the single domain nature of the sample.
5 ZnFe 1. 5 O 4 ) and N(E F ) is the density of the localized states at the Fermi level E F. The obtained T 0 values from the best t to the data (Fig. 7(c )) are 2 Â 10 10 K and 1. 62 Â 10 6 K, for Zn500 and Zn1100, respectively. In ferrite materials, these values can be used to calculate the density of the localized states at the Fermi level. The obtained T 0 values suggest that the density of states at the Fermi level is lower in Zn500 samples (N(E F ) ¼ 1. 6 Â 10 17 eV cm À3 for Zn500 and 2 Â 10 21 eV cm À3 for Zn1100 sample). This result can be explained by the presence of only one type of ferrite particle in the Zn500 sample, while the annealed sample at 1100 C contained a mixture of substances. 70 Using the value of T 0 , at a particular temperature T, the activation energy W is given by 67 W ¼ 0.
Idrees's point. 68 According to Idrees, the relaxation phenomena involve only hopping energy, whereas the conduction process requires disorder and binding energies in addition to hopping energy. On the other hand, the mismatch of approximately 331 meV is not negligible, implying that conduction and relaxation processes within Zn500 are due to the different types of carriers. The Nyquist diagrams of Zn500 along with Zn1100 at selected temperatures are indicated in Fig. 10. They are typical for spinel. 79 The patterns of curves at a xed temperature are close. The data at a given temperature falls into a semicircle. That curve becomes smaller with increasing temperature. Since reporters normally simulate the loop using a capacitor and resistor connected to each other in parallel, we suggest that the current specimens are simultaneously capacitive and resistive.
The energy of the proton beam during the irradiation was kept at 1930 keV with a r. m. s. of 15 keV. In this configuration and with the geometry depicted in Fig. 1 , the flux and energy distribution of the neutron beam at the target position were estimated via a Monte Carlo simulation using the SimLiT-Geant4 code  [6, 23]. The result, shown in Fig. 2 , resembles a Maxwell-Boltzmann distribution at 40. 1 keV with an average neutron flux of 5. 47 × 10 9 cm -2 s -1 mA -1 with a slight reduction of the high energy component, as is common in experiments using the 7 Li(p,n) reaction. The irradiation lasted 4. 6 hours during which the neutron beam intensity was monitored with a commercial 235 U fission chamber (PFC16A, Centronic Ltd. , UK) located just downstream the activation vacuum chamber, and with gamma dosimeters and a LaBr 3 scintillation detector attached to the adjacent walls.
Acknowledgements The authors acknowledge financial support by  University of Seville  via the  VI PPIT-US programme , the  Spanish   FPA2013-45083-P, FPA2014-53290-C2-2-P  and  FPA2016-77689-C2-1-R  projects, and the  EC FP7 projects NeutAndalus  (Grant No. 334315 ) and CHANDA (Grant No. 605203 ) and the  CERN n_TOF Collaboration. The SARAF-LiLiT experiment was supported by the  PAZY Foundation  (Israel). M. P. acknowledges support of the  Israel Science Foundation  (Grant No. 1387/15 ).
STATISTICAL ANALYSES All statistical analyses were conducted in R 3. 3. 2 (Team RC 2016) using linear mixed effect models fitted in lme4  (Bates et al. 2015) , and figures were made using ggplot2  (Wickham 2009). We previously showed that the probability of developing conjunctivitis following experimental inoculation did not differ between birds from exposed versus unexposed populations, but the former subsequently displayed less severe symptoms  (Bonneaud et al. 2018 ). To test the roles of resistance and tolerance in this host evolutionary response, we restricted our analyses only to individuals that became symptomatic (N = 83) and further removed 3 individuals that died during the course of the experiment due to incomplete data. We thus analyzed data from 80 symptomatic individuals (N = 34 birds from exposed populations each inoculated with a distinct isolate and 46 birds from unexposed populations inoculated with 1 of 45 isolates (one isolate from 2007 was inoculated into two birds from unexposed populations)).
ACKNOWLEDGMENTS This research was supported by a  Natural Environment Research Council  standard grant to C. B. and A. W. ( NE/M00256X ). We thank  A. Russell , the Associate Editor  K. Lythgoe , and two anonymous referees for helpful discussion and/or constructive comments on the manuscript. We thank  M. Staley  for growing and shipping the pathogen isolates,  M. Cook  for assisting with bird captures in Arizona,  A. Santos ,  W. R. Hood , and the undergraduates in the  Hood lab  for assisting with bird captures in Alabama, and  A. K. Ziegler  for assisting with the experiment in Arizona. The authors declare no conflicts of interest.
Experimental data The experimental data set used has been previously published  (Giraut et al. 2011)  and was derived from large Arabidopsis reciprocal backcross populations. On average, 1505 plants were genotyped for 380 SNPs in the male population and 1507 plants genotyped for 386 SNPs in the female population (380 in common). As the average distance between markers is small in this data set, 316 kb in males and 311 kb in females, the number of double COs (DCOs) in a single interval are expected to be negligible (the average distance between DSBs is 480 kb). It was therefore assumed during analysis that all recombination events were identifiable. Genotyping and recombination data sets are provided (Supplemental Material, data sets S1 and S2, respectively).
Beam-film-parameter optimization Beam-film simulations were performed and best-fit parameters determined using MADpatterns  (White et al. 2017a ) and custom perl scripts with an approach based on that described in  Zhang et al. (2014a). For each chromosome and each sex, at least three rounds of analysis were undertaken. In each round of each analysis, 30,000 bivalents were simulated for a range of parameter values. In the first round, to ensure the full parameter space was sampled, relatively broad value ranges of optimized parameters (Smax: 2-10 L: 0. 4-1. 7; T2Prob: 0. 002-0. 008; cL: 0. 3-1. 3, and cR: 0. 3-1. 3) were chosen based on values described in  Zhang et al. (2014a)  and comparison of ad hoc simulations with analysis of experimental data sets  (Basu-Roy et al. 2013). Parameters N, B, E, Bs/Be/Bd, A, and M were set at appropriate default values (see below).
In the next two rounds, progressively smaller step sizes between values were used to arrive at the final parameter values. Descriptions of each parameter are provided below. For each round of analysis, the CO distributions, coefficient of co-incidence (CoC) curves, and event distributions (distribution of number of COs per gamete) simulated for each chromosome were determined using MADpatterns  (White et al. 2017a) , and compared to those obtained for the relevant sex and chromosome from the experimental data set. For CO distributions and CoC curves, each chromosome was split into 13 equal-sized adjacent intervals for analysis. Importantly, the experimental data are gamete data, while the MADpatterns program simulates (and outputs) bivalent data (i. e. , all COs on a pair of homologous chromosomes).
Data availability statement Data set S1 contains male and female genotyping data originally reported in  Giraut et al. (2011). Data set S2 contains male and female recombination location data. Code used for parameter optimization is available at https://github. com/andrewhmlloyd/ BeamFilmBestFit. Supplemental material available at Figshare: https://doi. org/10. 25386/genetics. 7610921.
Acknowledgments We thank  Christine Mezard  for providing the experimental Arabidopsis recombination data set and for critical reading of the manuscript. A. L. was funded by a  Marie Curie fellowship  ( PIOF-GA-2013-628128 ) and the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie grant  agreement (grant number  663830 ). The Institut Jean-Pierre Bourgin benefits from the support of the  Saclay Plant Sciences Laboratory of Excellence  ( ANR-10-LABX-0040-SPS ). Literature Cited.
Ocean colour has been recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System  [12, 13]. In the context of climate research, where one searches for small climate-related signals embedded in data with a dynamic range of more than four orders of magnitude, sampled from a noisy environment, the data stream should have the requisite precision. The sampling should be sustained over several decades, given that the oceans are subject to variability at decadal and multi-decadal scales  [14] [15] [16]. However, satellites and the sensors they carry have finite lifespans, typically about ten years. It is therefore required that data from different missions be joined in a seamless manner to produce a continuous time series that is free of artificial trends arising from blending disparate datasets.
Data All new satellite products presented in this study refer to those generated within the OC-CCI project until 2018 and published as OC-CCI dataset version 1 (OC-CCI-v1) and subsequent versions (now in OC-CCI-v4). Details regarding the processing chain of each version are given in Table  2. The table also provides links to the products and to processing tools. Ancillary data on ozone and meteorological variables required for POLYMER processing came from the National Center for Environmental Prediction (NCEP) as implemented by NASA for ocean-colour processing. This ensured consistency in auxiliary data used in all sensors, since L2Gen uses the same set of auxiliary data in its processing chain.
In Situ Data In situ data were used for three parts of the work: (i) for comparison of algorithms as part of algorithm selection process; (ii) for validation of satellite products; and (iii) for uncertainty characterisation of the products. The database was composed of in situ measurements of remote-sensing reflectances at multiple wavelengths, concentration of chlorophyll-a, inherent optical properties, and the diffuse attenuation coefficient. Data from the different sources were merged into a single unified database with the aim of increasing the number of match-ups available for validation and their distribution in time and space. To merge all data, methodologies for homogenisation, quality control, and removal of duplicate data were implemented. Homogenisation focused on the conversion of all data into a standard format with respect to units and nomenclatures.
The in situ database associated with v2 of OC-CCI has been published  [22]  and that assembled for v4 has been submitted  [23]. Radiometric observations were acquired originally from multiple sources after they had been quality-controlled, time-averaged, and extrapolated to the surface and, therefore, required only conversion to standard format. Remote-sensing reflectances, if not directly available, were computed from irradiance reflectance or water-leaving radiances. To ensure the highest consistency between in situ and satellite data, only exact or fully-normalized remote-sensing reflectances, which account for the bidirectional nature of the light field  [28, 29] , were used. For inherent optical properties, absorption coefficients of phytoplankton and of coloured dissolved matter plus detritus, as well as those of backscattering by particles, were either assembled or computed from the available data.
We recognise, however, that this choice invites the risk of increasing the apparent differences between satellite observations and in situ observations. This being the case, the validation exercises undertaken in this study are best interpreted as testing how well the satellite data represent the surface chlorophyll values. Quality checks on the radiometric and pigment data included visualisation of data through time series and maps and the removal of observations with unrealistic values, missing time or geographic coordinates and fields, poor quality (as indicated by flags in original databases), inappropriate method of observation, and suspicious observations (e. g. , spuriously high or low). Duplicate data present in multiple databases were identified and removed on the basis of the metadata (e.
g. , experiment or cruise name) and temporal-spatial thresholds. The metadata of each in situ measurement (source, cruise or experiment, and principal investigator) were propagated throughout the processing into the merged database. The final merged database represents the output of an attempt to gather, homogenise, and merge several data sources that have emerged from the long-term efforts of the ocean-colour and biological oceanographic communities to provide scientists with high-quality in situ observations. As an example, geographic distributions of chlorophyll concentrations in the in situ database associated with the four versions of OC-CCI products are shown in Figure  1. For further details regarding the sources of the original data and the process of quality checking and elimination of duplicates, please refer to Valente et al.
Match-Up Database The in situ data were matched with corresponding satellite data for use in algorithm selection, for validation of the products, and for generation of uncertainties. The match-ups were identified by the nearest latitude and longitude, the central pixel being co-located with each of the in situ data. The surrounding pixels (3 × 3 box, with the in situ datum in the centre) were selected for further analysis. Only those pixels that had a valid central pixel and which satisfied various product-specific checks (e. g. , chlorophyll within the 0. 01 to 100 mg m -3 range and water depth greater than 50 m according to bathymetry) were selected for further analysis. As the OC-CCI product is a daily merged product of MODIS-Aqua, MERIS, SeaWiFS, and VIIRS, with overpass between ∼10:00 and ∼13:30 local solar time, the acceptable time window for match-ups was initially set to 09:00-15:00, but subsequent tests showed minimal significant effect from accepting in situ samples at any time of the same day; the time window was therefore set to same day.
Homogeneity criteria (e. g. , CV < 0. 15; at least five valid pixels in the 3 × 3 box) were used to test the impact of non-homogeneity in the vicinity of the match-up data on the uncertainty estimates. However, finally, all data with valid central pixels were used for uncertainty characterisation in the interest of maximising the number of observations and geographical and seasonal coverage of the match-up database. Statistical information on the 3 × 3 box, including mean, median, and coefficient of variation, was retained in the match-up database for further downstream analysis.
Quantitative Criteria For the quantitative comparison, match-up datasets were constructed first. To test the atmospheric correction processors, a match-up database of remote-sensing reflectances (R rs ) derived from satellite and in situ observations was created. To test the in-water algorithms, an in situ database with inherent optical properties, diffuse attenuation coefficient, and chlorophyll concentration was matched with corresponding R rs from satellite data. The geographic distribution and trophic conditions corresponding to observations in any match-up database are unlikely to be representative of the entire global ocean over a full seasonal cycle. Therefore, in both the atmospheric-correction and in-water inter-comparisons, the method of bootstrapping  [36, 37]  was used to explore the effect of the in situ datasets on the results.
This involved randomly resampling, with replacement, the in situ data to create several thousand of new datasets, each of the same size as the original dataset. The quantitative statistical tests and the scoring method, developed for atmospheric-correction and in-water comparisons, were run for each new dataset to explore the impact of choice of dataset and to account for uncertainties in the methodology adopted for scoring. For both the atmospheric-correction and in-water inter-comparisons, the performance of the candidate processors and algorithms was evaluated using a suite of statistical tests commonly used in comparisons between modelled and in situ data  [38, 39]. For comparisons of algorithms, these statistical tests were employed to develop an objective methodology designed to rank the quantitative performance of the candidate algorithms and processors.
At the same time, in the interest of minimising gaps in data, too many pixels should not be misclassified as being unusable. The cloud screening for MERIS provides three levels of clouds: opaque, semi-transparent, and clear sky. We optimised the distinction between the three classes by analysing the increase in the error on match-ups. All data generated at Level 2 are processed automatically to Level 3, and only good pixels can be taken forward at this stage to meet the accuracy requirements for climate-change studies. Pixels considered good are those not flagged as clouds (including semi-transparent clouds for MERIS) or as sea ice or floating vegetation and where the quality flags provided by the respective atmospheric correction algorithms permitted usage of the retrieved marine reflectance.