dataesr/scientific-paragraphs-categorization huggingface dataset:
Some key features of the registries are shown in Table  3. There is a large heterogeneity in organizations running the registries: academic institutions (n=5), national MS societies (n=4), and ad hoc institutions (n=3); in one case (Russia), the registry is kept by the government. More than half of the registries (n=7) collected data for more than 10 years, whereas two registries just started in the year before the survey. The number of centres that took part in each registry ranged from five to approximately 150 (median 15. 5), and the number of patients from 270 to about 40,000 (median 8,300). In nine registries (69%), regular follow-up was intended, mainly annually or biannually (Table  3 ). Registration is mainly performed by neurologists or medical staff (n=11, 85%). Data are collected exclusively from patients in Croatia and Serbia, whereas in Italy and UK, both sectors contributed to data collection.
Longitudinal data were collected by 11 registries, where different datasets of one patient acquired in one or more centres at different time points can be linked. The data collected in the registries are shown in Table  4. Mostly, demographic and basic clinical data Multiple Sclerosis Journal 20 (11)   including data on current DMDs are gathered. Less frequent data were current disease activity (in particular the number of relapses within the last two years) and symptomatic treatment. Only six registries (Croatia, France, Italy, Norway, Sweden and United Kingdom) included patient-reported outcomes (Table  5 ). Informed consent from patients is needed in 11 registries; this is done mainly in written form (n=9). In two registries (Denmark and Spain/Catalonia), it is not mandatory to inform patients.
Results of the interviews The leaders from seven (54%) registries were interviewed via telephone conferences: Croatia, Denmark, Germany, Italy, Norway, Spain and United Kingdom. death have been registered since 1943) is facilitated by a unique registration number that is assigned to each Danish citizen. The main purpose of the registry is epidemiological investigations. Data collection is done by two neurologists who extract the relevant information from the medical records. A number of quality control mechanisms exist. As of 31 January 2012, 20,000 patients had been registered, of whom 12,500 were still alive (coverage of 90%). In 1996, the Danish MS Treatment Registry was established, and all patients treated with DMDs are obliged to be followed prospectively every six months. Documentation is done online by the neurological clinics.
As of January 2012, a total of 6,700 patients and 11,300 datasets had been recorded. 9] [10] [11]  Germany (JF): The German MS Registry is run by a company aligned to the German MS Society. Since 2001, data from over 30,000 patients have been collected (with over 80,000 datasets) from over 150 clinical centres (university hospitals, neurological clinics, rehabilitation units and neurologists) who are obliged to participate in order to be certified as an MS centre by the MS Society. The main purposes are epidemiological studies, healthcare research, quality management of healthcare and clinical research. Documentation is done locally by neurologists, with transfer of pseudonymized data every three months. A number of quality checks are available. [14]  Italy (MT): The registry is kept by the network of 40 MS centres and coordinated by the University of Bari and the Consortium Mario Negri Sud, Chieti.
This underrepresentation of patient-reported outcomes is also found in clinical trials, which are heavily weighted towards physician-based instruments. However, it is increasingly acknowledged that these standard clinical endpoints do not fully reflect the patient's experience of the disease, and particularly employment status, social and family relationships, bladder/bowel dysfunction, fatigue, visual disturbances, cognitive dysfunction and affective disorders may have an enormous impact on patients with MS. 22  Patient-reported outcomes are thus advocated to be routinely used in clinical trials for assessing HRQoL. 22, 23 In this regard, the UK MS registry is unique in that it combines clinical data that are recorded from neurologists or medical assistants (physician-oriented) and HRQoL data that are collected directly from the patients via an online portal (patientreported).
Acknowledgements The  EUReMS  project is a three-year project initiated and led by the  European Multiple Sclerosis Platform (EMSP)  in partnership with the following organizations:  Association of Multiple Sclerosis Societies of Croatia (AMSSC), Croatia ;  Deutsche Multiple Sklerose Gesellschaft, Bundesverband e. V. , Germany ;  Università degli Studi di Sassari, Italy; Universitetet i Bergen, UiB, Norway ;  Fundació Institut de Recerca Hospital Universitari Vall d'Hebron, Spain; Polskie Towarzystwo Stwardnienia Rozsianego, Poland; Societatea de Scleroza Multipla din Romania, Romania; UK MS Society, United Kingdom; Universitätsmedizin der Georg-August Universität Göttingen, Germany ;  Karolinska Institute Stockholm, Sweden ;  Neurologisches Rehabilitationszentrum Quellenhof in Bad Wildbad, Germany.
There is also a huge variety in terms of data collection (e. g. hospital-based vs. population-based), coverage, involvement of national MS societies, documentation (paper forms vs. electronically, neurol- ogist vs. patients), quality control mechanisms and governance. such as HRQoL (MSIS-29, EQ-5d) and fatigue (FSS). anonymously on all inpatient and outpatient attend- Different quality check routines are implemented at ances within Wales during the last 20 years. Datasets the time of data collection as well as within the data- can be linked. Access to the web portal is open to all base. Regular follow-up is supported: one year for MS patients in the UK. Quality control mechanisms patients without DMDs, and six months for patients check the plausibility and consistency of both data- with DMDs.
As of January 2012, 5,100 patients have sets. At the end of the pilot phase, approximately been documented (approximately 60% coverage). 1,200 clinical datasets and 8,300 patient-reported datasets had been collected. Spain/Catalonia (JSG, SO): The main purpose of the Catalan MS registry is epidemiological investiga- tions, particularly incidence studies. Newly diag- nosed patients with MS according to McDonald criteria and those with possible MS/CIS have been included since 2009. 20 Prospective follow-up is only performed until the diagnosis of MS is confirmed. Ninety percent of centres in Catalonia take part (six academic centres and 15 neurological clinics). Documentation is web-based and done by neurolo- gists, and multiple quality control routines are per- formed. The registry is run by the University Hospital Barcelona, and owned by the Department of Health of Catalonia.
Data were collected by applying a structured open-ended interview protocol, whose questions were based on the Grossman's model  [12]  of professional knowledge for teaching. The questions sought to characterize the conceptions of cooperating teachers about the objectives of the practicum, and the ideal profile to practice the profession. The responses were recorded, transcribed verbatim, and brought into qualitative analysis software NVivo 10. The qualitative data analysis was supported by the Feiman-conceptual orientations in teacher education  [13]. The first phase of the analysis consisted of coding the interview. Text before hand attached to two main topics deduced from the conceptual framework: "Objectives of PETE" and "Role of the Teacher". The coding process included the active search of themes and patterns emergent from cooperating teachers' transcripts.
Serological surveys The main four municipalities located at the lakeside were selected for the poultry surveys as they represented various types of landscape including rice paddy fields, wetlands and dry vegetation. Two cross-sectional serological surveys were conducted; first in August 2008 and then in May 2009. A two-stage, random sample approach was designed from a farm census database already established for previous studies  (Andriamanivo et al. , 2012). Villages and farms were the first-and second-degree units respectively. Sample size was first set at 200 birds for 2008 to obtain an absolute accuracy of 5% under the hypothesis of 15% seroprevalence with a 95% confidence level and then was increased to 750 birds in 2009. Blood samples were taken from chickens, ducks and geese that were present on farms.
Commercial enzyme -linked immunosorbent assay (ELISA) kits (IDVET © screen test) were used to detect antibodies against AIV type A. All sera testing positive for antibodies against AIV were also checked for subtypes H5 and H7 with other commercial IDVET © kits. In addition, data on farm characteristics were collected through a questionnaire. The geographical coordinates of the farms visited were recorded during fieldwork using a Garmin global positioning system (GPS) instrument.
Landscape classification and extraction of environmental variables We hypothesised that land cover and presence of water bodies drive the spatial distribution of AIV in Madagascar as has been shown previously for HPAI H5N1 in Southeast Asia  (Gilbert and Pfeiffer, 2012). One Landsat 7 enhanced thematic mapper plus (ETM+) satellite image cantered on the lake and marshlands in the South was analysed. The scene was dated 22 March 2007 (Path/Row 158/072); it was cloud-free and had no apparent haze. A supervised classification of land cover was realised with IDRISIsoftware © (http://clarklabs. org/ products/idrisi. cfm) based on a maximum likelihood classifier and a three-channel composition (TM3, TM4, TM5). Fifty-one polygons were digitised manually and 351 GPS field observations were used to validate the classification.
A categorical variable, corresponding to the predominant type of landscape in the neighbourhood (1 km radius) of farms, was then constructed from vegetation layers. This radius was delimited according to field observation indicating that it was the maximal perimeter for bird ranging around farmhouses. Two alternative buffer radius (500-m and 2-km) were also tested in a sensitivity analysis. The percentage of buffer areas covered by each class of vegetation was calculated using ArcGIS, version 9. 3 software (ESRI; Redlands, USA) and the landscape type dominating each farm buffer was identified based on this percentage. The Euclidian distance between each farm and the nearest body of water was calculated from the water polyline layer derived from raster image.
Statistical analysis Statistical analyses were carried out using the R 2. 14. 0 software (The R Foundation for Statistical Computing, 2012). Bird-level AIV seroprevalence and corresponding confidence intervals were estimated for the different poultry species and sampling date using the epiR package. The influence of the risk factors on flock-level prevalence was studied using generalised linear models with a logit link function. Clustering of poultry flocks within farms was accounted for by adding a random effect to the model. Univariate screening revealed that categorization of flock size into a binary variable (1 to 15 birds vs. more than 15 birds) and logarithmic transformation of distance to the nearest body of water provided a better fit. A full starting model was first constructed using flock-level prevalence as the output and six potential explanatory variables: year, flock species, flock size, number of different flocks within the farm, dominant landscape type and distance to the nearest water body.
Variation in the number of flocks present in each farm was accounted for by adding a weight term to the model. Two-way interactions were added to the multivariate model and their effect was statistically evaluated. The multivariate analysis was carried out using a stepwise, backward elimination process with the final model selected according to the Akaike information criterion (AIC)  (Dohoo et al. , 2003). Goodness-of-fit of the model was assessed by using the test described by  Hosmer and Lemeshow (2000). Epidemiological datasets often display spatial autocorrelation and if this pattern remains in the residuals of a statistical model based on such data, parameter estimates and standard errors may be biased  (Dormann et al. , 2007). However, if the spatial dependence in the response variable is completely explained by the spatial pattern of the explanatory variables, no spatial autocorrelation should be present in the residuals and regression should produce correct effect estimates and confidence intervals  (Dohoo et al.
, 2003). Considering this, we analysed the spatial correlogram of the Moran's I statistic by computing values at various spatial lags using the R spdep package  (Trevennec et al. , 2011)  to identify any not accountedfor spatial correlation in the residuals of the logistic model. The Moran's permutation test was also performed for each km from 0 to 30, the latter value corresponding approximately to half of the maximum distance between pairs of flocks  (Bivand et al. , 2008). A prediction map for the risk of avian influenza was produced from landscape rasters covering surroundings of Lake Alaotra. The following equation, based on the model's coefficient, was used to derive a predicted probability (p) of AIV infection for small chicken flocks in 2008 (the reference categories): with α being the intercept, β 1 , β 2 and β 3 the estimated coefficients, Dwater the log-transformed distance from the farmhouse to the closest water body, Wetland and Rice the presence of predominant wetlands or rice fields in a 1-km neighbourhood around the poultry farms, respectively.
70% and 98. 72%, respectively, according to the manufacturer. On the other hand, this may be more problematic for palmipeds given the lower test performances for these species (89. 02% and 88. 79%, respectively, according to the manufacturer). False positive and false negative results may thus be present in our dataset. Second, landscape was characterised based on a remote sensing image dating from March 2007, while the serological surveys were carried out in August 2008 and May 2009. This time gap may have resulted in misclassification of environmental variables if the land cover changed over time, a possibility that cannot be excluded, as the expanse of the water surface varies greatly in Lake Alaotra depending on climatic conditions and the dynamism of the marsh and rice cultivation zones  (Bakoariniaina et al.
GWAS and meta-analysis Summary files on a common set of SNPs were shared among the EVE Consortium investigators. Genotype imputation using HapMap reference panels and Markov Chain Haplotyping software (MaCH)  [58]  were performed in each sample as previously described  [25]. We pooled the imputed genotype data for each ethnic group (European American, Latino, African American/African Caribbean) (Figure  1 ). To adjust for potential population stratification, we used Eigenstrat  [59]  to create principal components for each ethnic group. Within each ethnic group, we tested for the association of SNPs with allergic rhinitis by constructing a test statistic that had a standard normal distribution under the null hypothesis of no association and captured the direction of the effect. Models were implemented in PLINK  [60]  and controlled for age, sex, and principal components.
Acknowledgements We thank all the participants who made this study possible. We appreciate contributions from  Brooke Schuemann ,  Barbara Klanderman, PhD ,  Jody S. Sylvia ,  Ute Geigenmuller, PhD ,  Roxanne Kelly ,  M. B. A. ,  Jose Rodriguez Santana ,  M. D ,  William Rodriguez Cintron, M. D. ,  Rocio Chapela, M. D. ,  Jean Ford ,  M. D. ,  Shannon Thyne, M. D. , and  Pedro C. Avila , M. D. This work was supported by grants from the  National Institutes of Allergy and Infectious Disease  ( K08AI093538  to S. B. ;  AI070503  to C. O. ;  AI079139  and  AI061774  to L. K. W. ; and  AI077439  to E. G. B. ), the  National Heart, Lung, and Blood Institute  ( HL101651  to C. O. and D. L. N. ;  HL087665  to D. L. N. ;  HL085197 ,  HL087665 ,  HL072414 , and  HL49596  to C. O. ;  HL064307  and  HL064313  to F.
Acknowledgements We are grateful to our colleagues  Susan Abmayr ,  Manfred Frasch ,  Christian Klämbt , and  Hanh Nguyen  for sharing fly stocks and antibodies. We thank  Jessica Kuckwa  for competent help with some crossings,  Ruth Hyland  for technical assistance, and  Katja Gessner  for excellent secretarial assistance. This work was funded by the  Deutsche Forschungsgemeinschaft (DFG)   Re 628/14-3  and  Re 628/15-2  to R. R. -P. , and  Ho-2559/3-3  to A. H. , and  OE311/4-2  to S. F. Ö and by the  DFG  graduate school 1216.
The pressure and temperature of a gas are directly related to its density. Gas density is, in turn, directly related to the refractivity of the gas. In the instrument the PML team plans to develop, the refractivity of ultra-pure helium gas will be determined by locking lasers to the resonances of a variablelength, multiple Fabry-Pérot (FP) cavity. An FP cavity is formed by careful alignment of two highly reflective mirrors at each end of an enclosure (figure  1 ). A laser is locked to an FP cavity by tuning its frequency such that an integer number of half-wavelengths fit between the mirrors. In Europe, vacuum metrology has gained considerable revaluation by funding from the European Metrology Research Programme (EMRP)  [46]. Since 2011 the vacuum sections of seven European National Metrology Institutes, supported by five industrial partners and three research grants, have worked together in the consortium EMRP IND 12 'vacuum metrology for production environments' to open up new measurement capabilities for vacuum and to help industry to characterize vacuum in industrial environments.
Thus, UHV-based surface analytical tools are used extensively for research, development and quality control of such biointerfaces, as powerful and reference tools. The importance of this field has been recognized within IUVSTA with the inauguration of the Biointerfaces division. Another point is that the theme of biointerfaces is transversal and multidisciplinary, thus there is a general consensus that access to historical studies and data is difficult and presents a significant barrier for new researchers in the area. The main issues appear to be that the earliest works are hard to find using modern databases due to patchy coverage; recent studies are published in a wide range of journals and are of a variable quality. Providing the link between surfaces, characterizations and UHV, a dedicated issue of the Surface Science journal published a series of reviews of great interest  [69].
Acknowledgments This work was partially supported by  Slovenian Research Agency  (grant # P2-0082 ), the  Australian Research Council  and  CSIRO OCE Science Leadership Programme (Australia) ,  Ministry of Science, Education and Sports of Croatia  (grant # 035-0352851-2856 , # 098-0982886-2895  and # 098-0982886-2859 ),  Grant Agency of the Czech Republic  (grant # GACR 13-09853S ),  Ministry of Education, Youth and Sports of Czech Republic  (grant # CZ. 1. 05/2. 1. 00/03. 0111 ), Department of the Autonomous Province of Trento, New Széchenyi Plan  (grant # T ÁMOP-4. 2. 2. A-11/1/KONV-2012-0019  -the realization of this project is supported by the  European Union , co-financed by the  European Social Fund). Financial support from the  German Federal Ministry of Economics and Technology (BMWi)  through the  programme Transfer von Forschungs-und Entwicklungsergebnissen (FuE) durch Normung und Standardisierung (TNS) , and the  European Union  through the  European Metrology Research Programme (EMRP)  is highly appreciated.
Climatic data Climatic data were obtained from an automatic weather station situated on the experimental site. Over the period 2007-2013, mean temperature was 10. 6 °C, annual rainfall (P) and Penman potential evapotranspiration (PET) were 686 and 714 mm respectively and annual global radiation was 4154 MJ m -2. The water balance (P-PET) during the growing season displayed a rather large variability between years (Table  1 ). We considered March 1 as the beginning of the growing season because it corresponds approximately to the time when winter crops like triticale start growing again and PET begins running higher than 1 mm d -1. We considered November 1 as the end of the growing season because all annual and semiperennial crops have been harvested and perennials are close to total senescence.
Measurements We used water content reflectometers  (Campbell Scientific CS616)  to monitor the soil moisture profile continuously from July 2007 to November 2013. Probes were installed in May 2007 in six plots (one plot per crop), inserted horizontally into the soil at  15, 45, 75, 105, 135, 165 and 195  cm depth (three replicates at 15 cm depth and two replicates at the other depths). Temperature sensors  (Campbell Scientific 107)  were also placed at 15 and 195 cm depth. Data were recorded at an hourly time step using CR1000 Campbell Scientific data loggers. Probes placed at 15 cm depth were removed for soil tillage and reinserted as soon as possible. Soil cores taken down to 210 cm from the six plots in 2006 were analysed to determine soil characteristics which were very homogeneous in the six plots (Table  2 ).
Data processing and calculations Data from CS616 probes need proper correction and calibration in order to obtain accurate soil moisture measurements  (Rudiger et al. 2010). First of all, data were regularly collected in a database managed with PostgreSQL and eventual outliers were eliminated. Secondly, the soil temperature was simulated at each CS616 depth using a script developed with R software (R Core Team 2014). We used Fourier's law to simulate heat conduction transfer through the soil profile. Soil temperature at 15 and 195 cm depth were taken as boundary conditions and the initial temperature along the soil profile was determined by linear interpolation between the two depths. Depth and time increments as well as thermal diffusivity (alpha) were optimized using two other plots of the same experiment with supplementary soil temperature measurements at 75 and 135 cm depth.
The optimized value for alpha was 24 cm 2 h -1 and the root mean square error (RMSE) was 0. 2°C over a period of 434 days. Thirdly, period measurements from the CS616 probes were corrected for measured or simulated soil temperature, using the temperature correction equation provided by  Rudiger et al. (2010)  with the slope coefficient for silt loam. The fourth step consisted in deriving a relationship between corrected period measurements and volumetric soil water contents, obtained from gravimetric water contents and bulk density measurements. A covariance analysis was applied with R software for the two or three replicates of each plot and depth in order to choose (with a 95% confidence level) between an individual calibration with a specific linear regression equation for each replicate, a common calibration or an individual calibration of the intercept with a common slope.
The mean coefficient of determination was 0. 86 for the 90 probes (n=17) and the mean RMSE was 0. 016 cm 3 cm -3. Finally, all corrected period measurements were converted into volumetric and gravimetric water content and the two or three replicates were averaged. Missing data were filled in by linear interpolation and data were aggregated to obtain daily measurements. Standard deviation between replicates was, on average over the period 2007-2013, 0. 019 cm 3 cm -3 for the first layer (3 replicates) and 0. 007 cm 3 cm -3 for the other layers (2 replicates). The soil water content (SWC, in mm) was calculated in each 30 cm soil layer and summed up over the monitored soil profile (0-210 cm). For each crop, SWC calculated over the three replicated plots with gravimetric measurements were compared to SWC calculated in the single instrumented plot using CS616 probes.