dataesr/scientific-paragraphs-categorization huggingface dataset:
Dosage schedules The dosage schedules are as follows: 1. 100 μg salbutamol → +300 μg = 400 μg salbutamol 2. 100 μg salbutamol → +500 μg = 600 μg salbutamol 3. 200 μg salbutamol → +600 μg = 800 μg salbutamol 4. 200 μg salbutamol → +200 μg = 400 μg salbutamol Thus these four dosage schedules will contribute the following data points to the dose-response curve covering the range 100-800 μg (Table  1 ). A schedule of assessments is given in Table  2. Rint measurement will be performed at baseline and 15 min after each of the two doses of salbutamol. The second dose of salbutamol will be given 30 min after the first dose. If the participant needs to take a bronchodilator within 12 h of the study visit, then the research appointment should be rescheduled. Rint measurements will be made as specified in the consensus guidelines  [6].
Study of the relationship between urinary salbutamol levels and Rint measurements The concentration of salbutamol in the urine at 30 min following a dose of salbutamol represents the gold standard of lung bioavailability. This has been established in adults. We will try to collect as many urine samples as possible, and we will aim to study the dose that is available to the lungs, measured as the 30 min urine salbutamol, against bronchodilator response. Data analysis will be performed as discussed above for inhaled lung dose versus bronchodilator response. As the production of urine on request is rather uncertain in children of this age, this is intended as a distinct secondary analysis. The main project, including the principal outcomes, will not be affected in any manner if the collection of these samples is incomplete.
Data collection methods The majority of data collected for the study will be recorded directly into the paper CRF. Source data entered directly into the CRF will include: (a) Clinical history with a focus on wheeze and related atopic diseases (b)Recent medication in relation to wheeze (c) Study procedures: salbutamol dosing schedule, Rint measurements, saliva and urine sampling The following information must be recorded in the participant's health records in addition to the CRF: A copy of the original CRF should be kept in the participant's health records. The CRF will be faxed or emailed to the Brighton and Sussex Clinical Trials Unit, Brighton, as per the study-specific CRF completion guidelines held in the site file. The original CRF will be stored in the site file. Blank and completed copies of the paper CRF will be stored securely in the site file.
Results: We identified 9 quality certificates and criteria catalogs for health information websites with 304 single criteria items. Through this, we aggregated 163 various quality criteria, each assigned to one of the following categories: thematic, technical, service, content, and legal. Finally, a consensus about 13 quality criteria for websites offering medical information on rare diseases was determined. Of these categories, 4 (data protection concept, imprint, creation and updating date, and possibility to contact the website provider) were identified as being the most important for publishing medical information about rare diseases.
Methods study design The study is a multicenter, randomized, open, parallel-group trial conducted at 33 centers in four European countries (France, Germany, Italy, and Spain) with a target enrollment of 153 patients assessable for the primary end point analysis in each group (ClinicalTrials. gov identifier: NCT01241526). The trial was approved by the local or national ethics committees and competent authority per country for each investigational center where applicable. The protocol was also submitted to data protection agencies in France: CCTIRS (Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé) and CNIL (Commission Nationale de l'Informatique et des Libertés). The study was conducted according to the current International Council for Harmonisation (ICH) Good Clinical Practice (GCP) guidelines, the Declaration of Helsinki (2008) and the European Directive 2001/20/ EC.
A worsening or alarm status triggers standardized interventions: patients perform self-measurements of pulse oximetry, spirometry, and body temperature, and receive a confirmatory/ supportive phone call from the CM to put into practice their personalized written action plan (predetermined by the CM and the hospital physician). Only alarm status is transmitted to the hospital physician for same-day medical assessment and management. Patients allocated to the comparison group, ie, usual care, receive standard COPD management, as routinely implemented at each investigational center, which varies by center and by country. Routine educational practice data (personnel, topics, duration, etc) are collected for each site at the beginning and end of patient inclusion. In addition, all patients on oxygen therapy are monitored with a NOWOX ® , which is a wearable device that records time of oxygen use and respiration rate.
Acknowledgments The authors sincerely thank all the COMET personnel who participated in this study, in particular the VitalAire case managers from the four countries, the three members of the  Endpoint Validation Committee  ( Dr Bucknall ,  Respiratory Medicine ,  Glasgow Royal Infirmary, Scotland, United Kingdom ;  Prof Burgel ,  Pneumologie et Centre de Mucoviscidose de l'adulte, Hôpital Cochin, Paris, France ;  Prof Vogelmeier ,  Pulmonary Division, Marburg University Hospital, Germany ), and the COMET investigators:  Dr Aguar.
Abstract. A unique set of 1-year precipitation and stable water isotope measurements from the Japanese Antarctic station, Dome Fuji, has been used to study the impact of the synoptic situation and the precipitation origin on the isotopic composition of precipitation on the Antarctic Plateau. The Antarctic Mesoscale Prediction System (AMPS) archive data are used to analyse the synoptic situations that cause precipitation. These situations are investigated and divided into five categories. The most common weather situation during a precipitation event is an upper-level ridge that extends onto the Antarctic Plateau and causes strong northerly advection from the ocean. Most precipitation events are associated with an increase in temperature and wind speed, and a local maximum of δ 18 O. During the measurement period, 21 synoptically caused precipitation events caused 60 % of the total annual precipitation, whereas the remaining 40 % were predominantly attributed to diamond dust.
, 2012). However, very few studies have been performed on present-day Antarctic precipitation and the relationships between its isotopic composition and moisture source and atmospheric transport characteristics. For a better understanding of the proxy data from ice cores, it is necessary to analyse the processes determining the isotopic composition of the snow at the drilling locations for recent time periods for which both meteorological and stable isotope data are available. The origin of precipitation is determined by the synoptic weather situations producing snowfall. Snowfall associated with synoptic precipitation events is mostly associated with warm events, which may be recorded in water stable isotopes  (Noone et al. , 1999). While in the coastal areas precipitation is caused by frontal activity in the circumpolar trough, different precipitation mechanisms are at play on the high Antarctic Plateau.
, 2010a; Ekaykin et al. , 2004). The remaining precipitation results from diamond dust. The spatial distribution of the precipitation is highly influenced by the topography  (King and Turner, 1997; Schlosser et al. , 2008a). The present study aims at an improved understanding of the atmospheric influences on the isotopic composition of Antarctic precipitation. Of particular interest is the influence of the position of the moisture source region and the conditions in this area and along the moisture transport paths. To reach this goal, precipitation conditions at the East Antarctic deep ice core drilling site Dome Fuji were investigated using the unique data set of daily precipitation and stable isotope measurements by  Fujita and Abe (2006). The atmospheric flow conditions and synoptic situations causing precipitation events at Dome Fuji were analysed with the help of Antarctic Mesoscale Prediction System (AMPS) archive data.
Synoptic analyses In the past, several studies were conducted to investigate the synoptic situations causing high-precipitation events on the plateau of Dronning Maud Land (DML)  (Sinclair, 1981; Noone et al. , 1999; Birnbaum et al. , 2006; Schlosser et al. , 2010a; Hirasawa et al. , 2013). One of the first to study the intrusion of synoptic weather systems into the interior of Antarctica was  Sinclair (1981). He found that in December 1978 an intensifying ridge over DML and cyclogenesis in the Weddell Sea caused record-high temperatures in the interior of Antarctica. Noone et al. (1999)  used reanalysis data from ECMWF to analyse high-precipitation events in DML. They found that the most common weather situation causing highprecipitation events develops through amplified long waves with a cyclone in the Weddell Sea and an Atlantic block forcing enhanced northerly advection.
AWS data The Antarctic Meteorological Research Center (AMRC) and Automatic Weather Station (AWS) Program are sister projects of the University of Wisconsin-Madison funded under the United States Antarctic Program (USAP). These projects focus on data for Antarctic research support, providing real-time and archived weather observations and satellite measurements as well as supporting a network of automatic weather stations across Antarctica. The current AWS at Dome Fuji, which measures the standard meteorological variables of air temperature, pressure, wind speed, wind direction, and humidity, was set up by the AMRC in February 1997. Data can be obtained from the AMRC website (http://amrc. ssec. wisc. edu).
The maximum wind speed allowed in MCIM is 10 m s -1. In 30 % of cases the wind speed in the estimated source regions was higher and had to be adjusted. The initial conditions for the closure equation were taken from ECMWF-Interim Reanalysis (ERA-Interim) at the estimated source regions of the precipitation events. The required variables are available from ECMWF (http://apps. ecmwf. int/datasets/ data/interim-full-daily/). The arrival conditions were taken from AMPS at the arrival height of the trajectories. Furthermore, simulations with the isotopic GCM ECHAM5-wiso  (Werner et al. , 2011)  were used. This model enhances the atmospheric GCM ECHAM5 by stable water isotope diagnostics. A similar approach was already performed with the predecessors ECHAM3  (Hoffmann et al. , 1998)  and ECHAM4  (Werner et al.
The simulation was per-formed for the period 1979 to 2013, with prescribed values of ocean surface conditions (SST, sea ice concentration), insolation, and atmospheric greenhouse gas concentrations. Monthly SST and sea ice concentration were derived from the ERA-Interim data set, and the dynamic-thermodynamic state of the atmosphere was nudged to ERA-Interim data as well. In this study, we will compare the Dome F precipitation isotopic measurements with two different types of model outputs. First, we will use atmospheric back-trajectories combined with MCIM, a theoretical distillation model, in which a single moisture source is assumed. Second, we will use the outputs of ECHAM5-wiso, a GCM equipped with water stable isotopes and nudged to atmospheric reanalyses. The advantage of the first approach is to make best use of regional atmospheric circulation data, albeit with simplifications for isotopic processes.
The warm air advection associated with the event-type precipitation discussed in this and some previous studies increases the condensation temperature considerably, thus decreasing the temperature difference between the moisture source area and the deposition site, which means less fractionation than for colder conditions. It is not clear to what extent the results from precipitation events in a 1-year period can be transferred to longer time scales. However, the physical mechanisms at play stay the same and it is important to understand how changes in the general atmospheric circulation (e. g. during glacialinterglacial transitions) change moisture sources and transport paths for Antarctic precipitation and thus the stable isotope ratios measured in the ice cores. This study therefore stresses the importance of Antarctic precipitation monitoring data sets for longer time periods.
Acknowledgements. This study was financed by the  Austrian Science Fund (FWF)  under grant  P24223  and by the  Amadée Programm of WTZ  ( FR 12/2014 ). AMPS is supported by the  US National Science Foundation, Office of Polar Programs. We appreciate the support of the  University of Wisconsin-Madison Automatic Weather Station Program  for the Dome F AWS data set, data display, and information,  NSF  grant numbers  ANT-0944018  and  ANT-1245663. The  European Centre for Medium-Range Weather Forecasts  kindly provided the archive data from  ERA-Interim. We thank  Lindsey Nicholson  for software support. We also thank our editor,  Carlo Barbante , and  Frédéric Parrenin  and an anonymous reviewer for their positive reviews and helpful comments. Edited by:  C. Barbante.
Data availability The ERA-Interim data are available at http://www. ecmwf. int/en/elibrary/8174-era-interim-archive-version-20. For access a registration is necessary. AMPS archive data are available at http://polarmet. osu. edu/AMPS/amps_mm5. html. The Automatic weather station data can be accessed at ftp://amrc. ssec. wisc. edu/pub/aws/antrdr/.
The southern tip of Madagascar is also characterized by a wide continental shelf extending 50 km from land and is referred to as the Madagascar Plateau  [49]. Sightings of humpback whales have been recorded further south than the southern tip of Madagascar around the seamount of Walters Shoals located 645 km south of Madagascar and at depths less than 20 m  [31, 50]. The strongest geostrophic currents (0. 2-1 m. s) are observed on the east coast of Madagascar, up to the south of Ile Sainte Marie and Antongil Bay region (electronic supplementary material, figure  S1 , reconstructed from the dataset of SWIO12 climatological model (see environmental parameters)). The South Equatorial Current (SEC), which flows westward at around 20°S, splits into two branches as it approaches the eastern Madagascar coast: one branch flows northward to the northern tip of Madagascar; and the other flows southward along Madagascar, referred to as the East Madagascar Current (EMC)  [51, 52]  (electronic supplementary material, figure  S1 ).
Recent technological improvements in cetacean satellite telemetry have increased information on seasonal distribution ranges, movements into remote areas, as well as migration route and stock structure  [26, 27, [56] [57] [58]. It is also now possible to relate the individual whale's horizontal movements to their physical environment at appropriate spatial and temporal scales, and to better understand how environmental parameters influence whales' distribution. In this study, we used data from Argos satellite tags attached to humpback whales off Ile Sainte Marie (northeast Madagascar) and Anakao (southwest Madagascar) during three breeding seasons to investigate movement patterns and habitat utilization of humpback whales in relation to sex and reproductive status. We then investigated how key environmental variables along the whale tracks influenced humpback whale movements.
The sex of each tagged individual was inferred in the field based upon the presence of a calf (i. e. assuming that adults in cow-calf pairs were females), the role in the group of whales (see  [62] ), when possible, and except for one whale was subsequently confirmed through genetic analysis from biopsy samples  [63]. Whale locations were obtained through the Argos data collection system  [64]. Further details about tag anchoring, deployment and whale selection can be found in  [62]. In 2012, we used both an 8 m long carbon fibre pole, and modified pneumatic tag deliverer (ARTS) to deploy tags on whales, and in subsequent years, we only used the ARTS  [26, 27, 57]. Tags deployed in 2012 were duty-cycled to transmit 6 h on, 6 h off for the first three months after deployment, and then every other day until the end of transmission.
Data processing All locations classified invalid by Argos (Argos location class 'Z') and all locations on land were removed from the dataset. We retained Argos locations, classes 3, 2, 1, 0, A and B (see http://www. argossystem. org/manual/3-location/34_location_classes. htm), and assumed as invalid any location implying a travel rate greater than 12 km h -1 (e. g. [19] ) as indicated by R Package Trip (R Development Core Team 2006). The Bayesian Switching State Space Model (SSSM) was used to model estimated positions and associated errors based upon the raw Argos locations  [65]  to provide the best estimate of each whale's path. A 12 h time step was used to minimize the number of positions estimated when the tags were not transmitting. The model was fitted using WinBUGS v. 1. 4. Two Markov Chain Monte Carlo (MCMC) chains were run in parallel, each for a total of 50 000 simulations.
Data organization The whale position data were split into two categories: coastal and oceanic according to whale position relative to the bathymetric environment from which the whales evolved (figure  1 ). Whale movements were defined as coastal when located over the continental shelf (less than 200 m water depth, 78% of coastal positions) or around the shelf break (more than 200 m water depth, 22% of coastal positions). Whale movements were defined as oceanic when animals were definitively leaving the Madagascar continental shelf towards deep ocean waters (i. e. all whale locations that were over a depth more than 200 m and not associated with coastal movements). www. gebco. net/) was used to compute bathymetry depth, bathymetry slope and DIST. Sea floor slope (hereafter 'slope') was derived using the R package raster (function terrain;  [68] ).
1°bins removed some locations from the analysis. SST was extracted from a daily SST dataset produced and distributed by JPL (http://ourocean. jpl. nasa. gov/SST/, G1SST product) at a 1 km spatial resolution. For consistency with the surface chlorophyll-a analysis, we used a mean value within a 0. 25°bin around each whale location. For the open-ocean dataset, daily geostrophic currents were computed from the daily merged and gridded satellite altimetry product produced by Aviso (http://www. aviso. altimetry. fr/) with a spatial resolution of 0. 25°(latitude, longitude). Because altimetry-derived ocean velocity measurements are noisier closer to a coast, surface currents for the coastal dataset were not available from Aviso products. Instead, we used a climatological estimate of a geostrophic current from a regional ocean model configuration.
Movement datasets From the 19 whales that displayed coastal movements, 11 were females (six females with calves and five females without calves; electronic supplementary material, figures S2a and S2b) and eight were males. Whale movements were analysed using linear mixed-effects models (LMMs; see below for details of analysis) considering the logit of behavioural mode values and whale active swimming speed values as response variables, and SST, bathymetry, DIST, slope and current speed as explanatory variables. Seven whales displayed oceanic movements including six females and one male. These oceanic movements were analysed using LMMs considering the logit of behavioural mode values, whale active swimming speed and whale deviation from the current values as response variables, and SST, bathymetry, slope, current speed and the surface CHL concentration as covariates.
Behavioural metrics Two metrics were used to characterize the whale movements: (i) the behavioural mode from SSSM outputs described earlier (see data processing), and (ii) the whale active swimming speed. The observed whale speed vector (T) was estimated by computing the distances, in the x and y directions, between the two consecutive locations and then dividing by the time elapsed. It was systematically computed as T(t) = [X(t + t) -X(t)] t , (2. 1) where [X(t), X(t + t)] are the two successive positions over a time interval, t. The observed whale speed vector (T) can also be written as the sum of the whale's active swimming vector (A) and the current vector (C) (e. g. [71] ) T(t) = A(t) + C(t). (2. 2) In other words, the mean active swimming speed (A) computed over the time interval [t, t + t] can be expressed as the difference between T(t) and the averaged current speed during the same time interval (namely [C(t) + C(t + t)]/2; e.
g. Galli et al. [72] ). To simplify the notation, A(t) will be denoted as A, T(t) as T and [C(t) + C(t + 1)]/2 as C; so equation (2. 2) is rewritten as A = T -C. (2. 3) The resulting tracks from the SSSM are made of positions estimated with a fixed sampling period t = 12 h. To extract environmental variables under the whale tracks, we first linearly interpolated locations every 6 h between the positions estimated every 12 h by the SSSM (using R package adehabitat, function redisltraj;  [73] ). Then the computed whale active swimming speeds were assigned to each interpolated location and environmental variables were extracted for these locations. The latter positions were used in the modelling analysis to link the active swimming speed metric and environmental variables.
Statistical analysis All statistical analyses were performed using R Program v. 3. 1. 2 (R Core Team 2014). We fitted a series of LMMs using the R software package nlme (function lme;  [74] ) following the steps described in Zuur et al. [75]  to examine the relationship between three response variables (behavioural mode, active swimming speed, angle between whale's heading and surface current) and the explanatory environmental variables. Behavioural state is the proportional likelihood of exhibiting localized movements, ranging between 1 (transiting) and 2 (localized). As a consequence, behavioural mode values were logit-transformed before the analysis  [76]. An autocorrelation term (corAR1) was added to account for the lack of temporal independence within telemetry data for each whale  [75].
Acknowledgements. Many individuals made this work possible. This study represents a collaborative effort between the  Institute of Neurosciences Paris Saclay (NeuroPSI, France) , the association Cetamada (Madagascar), the  Wildlife Conservation Society (WCS, USA)  and the  NOAA National Marine Mammal Lab (NMML, USA). Maria Faria  and  Henry Bellon  provided critical field logistic support and participated in the fieldwork; further logistic and administrative support was provided by  Sophia Rakotoharimalala ,  Anjara Salomavola ,  Norbert Andrianarivelo ,  Boris Andrianantenaina ,  Sylviane Raharivelo ,  Luccianie Raonison ,  Cesaire Ramilison ,  Rina Ralison ,  Devon Litherland ,  Victoria Cordi ,  Ambroise Brenier  and  Christopher Holmes. For  logistical support in the Anakao region, we thank the staff of IHSM in Toliara, in particular  Daniel Ramampiherika  and  Thierry Lavitra ,  Thierry Bournonville  and the staff of  Lalandaka Lodge ,  Michel Agou  and  Madam Diamondra  from  Le Prince Hotel , and the  Ministry of Fisheries   PACP  project.