dataesr/scientific-paragraphs-categorization huggingface dataset:
Regarding the HDL proteome watch database (http://homepages. uc. edu/~{}davidswm/HDLproteome. html), we recovered 69 of the 95 (73%) proteins defined as appearing in three separate studies from three independent laboratories. We also found 155 of the 228 proteins cited in this database (70%). Among 522 proteins, 326 were at least found in 60% of the samples (id. five of the eight controls and six of the nine hemodialysis (HD) patients). Twenty-two and 20 proteins were only found in control and HD samples, respectively. Among the 522 proteins, 151 were found in every sample. (A). The hemodialysis/control patient (HD/CTL) protein ratio was calculated as the protein abundance in HD patients divided by protein abundance in control patients. Among 326 proteins, nine were significantly downregulated while 10 were upregulated (see Tables  2  and 3  for details).
Discussion We found that the HDL proteome from HD patients contained certainly more than 300 proteins (Figure  S1 ), among which 19 were up-or downregulated when compared to control subjects. Using a high-resolution mass spectrometer, we were able to identify 10 times more proteins associated with HDL than any previous work in the field had identified  [8, 14]. This finding is consistent with a study conducted in chronic heart failure patients, where the authors reported 494 proteins associated with HDL in this pathological context  [15]. Simultaneously, our findings were consistent with the HDL proteome watch database (see Table  S1 , http://homepages. uc. edu/~{}davidswm/HDLproteome. html). This highlights not only the complexity of the HDL structure but possibly other lipoproteins with diverse roles in pathophysiological processes, such as those observed in CKD.
In hemodialysis patients, many beneficial properties of HDL, such as cholesterol efflux, anti-oxidant, and vasodilatory effects, have been found to be impaired  [5] [6] [7] [8]. Gene ontology enrichment analyses of our dataset indicated that dysregulated HDL protein composition in HD patients likely underlies the altered properties of HDL observed in this population. Upregulated proteins were involved in extracellular matrix expansion and blood coagulation, platelet activation, oxidative stress, activation of immune system, and negative regulation of apoptosis. Taken together, the dysregulation of these proteins could point to poor vascular homeostasis, which would then contribute to endothelial dysfunction and premature aging of blood vessels. In a previous study also using label-free quantification, Mangé et al.
Mass Spectrometry Briefly, samples were reduced, alkylated, and digested with trypsin at 37 • C overnight. They were desalted on C18 spin columns, dried, and analyzed in triplicate using an Ultimate 3000 nano-RSLC (Thermo Fisher Scientific, Illkirch, France) coupled online to a Q-Exactive HF hybrid Quadrupole-Orbitrap Mass Spectrometer (Q-Orbitrap, Thermo Fisher Scientific). Briefly, peptides were separated on a C18 nano-column using an acetonitrile linear gradient and analyzed in a Top 20 Higher Collision Dissociation (HCD) data-dependent mass spectrometer. Data were processed by database searching using Sequest HT (Thermo Fisher Scientific) with Proteome Discoverer 2. 2 software (Thermo Fisher Scientific) against a human Swissprot database and quantified with a label-free quantification approach.
The present study assesses the impacts of two grid resolutions and the descriptors of soil texture and land cover on flash-flood modelling at local and basin scales. The ISBA-TOP coupled system, which is dedicated to Mediterranean flash-flood simulations, is used with two grid-cell sizes (300 and 1000 m), two soil texture datasets, and two land use databases to model 12 past flash-flood events in southeastern France. The skill of the hydrological simulations is assessed using conventional data (discharge measurements from operational networks) and proxy data such as post-event surveys and high-water marks. The results show significant differences between the experiments in terms of both the simulated river discharge and the spatial runoff, whether at the catchment scale or at the local scale.
drologic Ensemble Prediction Experiment;  Schaake et al. , 2007). The sensitivity of the hydrological models to the initial soil moisture (e. g. Silvestro and Rebora, 2014)  and to hydrological parameters (e. g. Liu et al. , 2012; Edouard et al. , 2018)  has also been extensively studied in the past. Based on such a sensitivity study,  Edouard et al. (2018)  designed an ensemble prediction system for flash-flood forecasting. In addition, hydrological modelling uncertainties arise from the soil and land descriptions. Elevation, land use, and soil texture datasets are available at various spatial resolutions and from various data providers. Many hydrological models are calibrated, and the value of the calibrated parameters may depend on such terrain descriptors. Several studies have investigated the impact of soil and land data and their resolution using various digital elevation model (DEM), land cover  (Kamali et al.
, 2017; Yen et al. , 2015; Sharifi and Kalin, 2010) , and soil datasets (e. g. the Soil Survey Geographic database and the State Soil Geographic database;  Kumar and Merwade, 2009; Chaplot, 2014; Cotter et al. , 2003). The influence of the model resolution, which is strongly linked to the soil and land data resolution, has also been investigated  (Vázquez et al. , 2002; Egüen et al. , 2012). Even if in general a higher resolution leads to more accurate simulations, there can be a critical level beyond which the model response is not necessarily improved  (Hengl, 2006; Egüen et al. , 2012). Land use and, in particular, preferential pathways can have a large impact on the catchment residence times and the time that the flood wave travels  (Blöschl, 2001; Blöschl et al. , 2007). Several studies have identified the most appropriate model structure in hydrological modelling while taking into account several landscape complexity levels (e.
If the soil properties are simplified in the hydrological model (catchments were described using only their major soil type), the resulting misestimation of the maximum storage capacity of the catchment leads to large errors in the flow simulation  (Anquetin et al. , 2010). The present study investigates the impacts of the spatial resolution and terrain descriptors on flash-flood modelling using the ISBA-TOP hydrological model  (Bouilloud et al. , 2010; Vincendon et al. , 2016) , which is dedicated to Mediterranean flash-flood simulations. Two grid resolutions (300 and 1000 m) and soil datasets are used with ISBA-TOP to simulate several past flash-flood events in southeastern France. Validating a flash-flood discharge and runoff simulation is extremely challenging. Indeed, the lack of surface runoff observations is a real impediment to evaluating a runoff simulation at the proper spatial scale.
, 2015 (Payrastre et al. , , 2016)). A very recent database gathered floodrelated damage data since 2011 in the south of France at a fine scale  (Saint-Martin et al. , 2018). Javelle et al. (2014)  demonstrated that these data provide valuable information for evaluating the simulated flood peak. New approaches are being explored to use flood-damage and runoff-impact data to evaluate the simulated runoff. For example,  Vincendon et al. (2016)  compared flooded area diagnoses with road-cut data, and  Lagadec et al. (2016)  used information from postevent surveys to evaluate a method to map the susceptibility to surface runoff from the impact of floods on a railway. To investigate and rank the impacts of the spatial resolution and the terrain descriptors on flash-flood modelling, two spatial scales (catchment and local scales) are studied using conventional data as well as proxy data to assess the ISBA-TOP simulations, i.
Soil texture The sensitivity to the soil texture (the proportion of clay, sand, and silt) in ISBA-TOP was assessed using two different datasets: the Harmonized World Soil Database (HWSD, version 1. 2;  Nachtergaele et al. , 2012)  and the Land use/cover area frame statistical survey (LUCAS) topsoil data  (Ballabio et al. , 2016). The HWSD has global coverage at a resolution of 30 arcsec (corresponding to approximately 1 km at the Equator). It combines soil information from several sources worldwide, including from the European Soil Database, various regional SOTER databases (SOTWIS Database), and the Soil Map of the World database (http://www. iiasa. ac. at/ Research/LUC/External-World-soil-database/HTML/, last Figure  2  shows the clay and sand contents over southeastern France from the HWSD and LUCAS, respectively.
The mean soil texture fractions per watershed are reported in Table  3. In the HWSD, the soil is mostly clay for the Vidourle and the southern part of the Hérault catchments (33 %-38 %), whereas sand is dominant for the Gardon and Lergue catchments (approximately 40 %). The spatial distribution of the soil texture is not so highly contrasting for LU-CAS. In this second dataset, there is less clay over the Vidourle and Hérault catchments. For zone B, the proportions of sand in the HWSD are little higher than those of clay (38 % versus 24 %, respectively). For the southwestern part of zone B, the proportion of clay is particularly low. LUCAS generally reports less sand than the HWSD for zone B. Soil texture has an impact on simulated runoff through soil hydrodynamic parameters, which are determined by CH78 pedotransfer functions  (Clapp and Hornberger, 1978)  in ISBA-TOP.
Land cover The land cover type (e. g. forest, grass, crop, rock, town, or sea) of each ISBA grid mesh is initialized with the ECOCLIMAP-II  (Masson et al. , 2003; Faroux et al. , 2013)  land ecosystem database at a resolution of 1 km. Over Europe and the Mediterranean basin, ECOCLIMAP includes In this paper, the urban grid points of ECOCLIMAP-SG are considered to be either a fraction of bare soil, bare rock, temperate broadleaf deciduous and swamp areas (called ECO-SG), or bare rock (called ECO-SG-TownToRock). In Table  3 , the mean fractions of the land use types from ECOCLIMAP and ECO-SG are given. In ECOCLIMAP, a high percentage of every watershed in zone A is covered by forests (40 %-56 %) and grass (28 %-34 %). In general, ECO-SG presents even more forests (40 %-75 %) and less urban or bare soils in this area.
For zone B, the land use    and (d)  between R 2 T 2 C 2 and R 2 T 2 C 3. Note that the range of the colour scale is not the same for the various panels; however, in red-yellow, the differences are always positive, and in green, they are always negative. (a) between R 1 T 1 C 1 and R 2 T 1 C 1 , (b) between R 2 T 1 C 1 and R 2 T 2 C 1 , (c) between R 2 T 2 C 1 and R 2 T 2 C 2 , has a higher degree of contrast between the watersheds. In ECOCLIMAP, the soil is primarily covered by forests in the largest catchments of Siagne and Loup (37 %-48 %), whereas urban areas and towns are dominant in the Cagne and Brague catchments (approximately 40 %). For this zone, ECO-SG presents fewer forested areas than ECOCLIMAP but more crops. The proportions of urban or bare soils are of the same order for both datasets.
Experiments Several experiments (see Table  4 ) were designed to assess the sensitivity of the simulated hydrological response to the horizontal resolution (R), the soil texture (T), and the land cover (C). ISBA was run with two different regular grids at resolutions of 300 and 1000 m to assess the impact of the horizontal resolution. These two resolutions were selected because the spatial resolution of meteorological forcing data used in this study is 1 km and because the new land ecosys- tem database ECOCLIMAP Second Generation is produced at a 300m resolution. The ISBA grid orography for each grid resolution was averaged from the SRTM 90 m digital elevation data (Fig. 1 ), which have a vertical accuracy of ±16 m at the 90 % confidence level  (Jarvis et al. , 2004). Note  ) are approximately 10 %, and the error margins for the others (E no.
) were estimated using post-event surveys. Only the damage was registered for E14 during this event; therefore, no estimate is available. For E18, the discharge values ranged between 20 and 28 m 3 s -1. -TownToRock R 1 T 1 C 1 × × × R 2 T 1 C 1 × × × R 2 T 2 C 1 × × × R 2 T 2 C 2 × × × R 2 T 2 C 3 × × × that the watersheds in TOPODYN are described by a digital terrain model with a horizontal resolution of 50 m regardless of the ISBA grid resolution. The soil texture (from the HWSD or LUCAS) and land cover (from ECOCLIMAP-II or ECOCLIMAP-SG) were interpolated onto the ISBA grid. ISBA-TOP was run using five combinations of the available soil datasets. The first experiment, called (R 1 T 1 C 1 ), corresponds to the conventional use of ISBA-TOP (e. g. Vincendon et al. , 2016)  at a resolution of 1000 m with the HWSD and ECOCLIMAP-II.
River discharges in zone A To compare the different experiments, several skill scores (described in Appendix B) were used. The Nash-Sutcliffe efficiency (NSE;  Nash and Sutcliffe, 1970)  was computed considering the catchments and sub-catchments together and separately for all the events in zone A (Fig. 3 ) to assess the overall simulated hydrograph. Streamflow measurements were provided by the French HYDRO data bank (http:// www. hydro. eaufrance. fr/, last access: 28 March 2019). The uncertainty in the discharge measurement in this data bank is approximately ±10 %. In Fig. 3 , the closer the points are to the bottom-right corner, the better the skill. The experiment R 2 T 2 C 1 has the best score, followed by R 2 T 1 C 1. The R 1 T 1 C 1 experiment generally performs worse than the others.
The increase in the grid resolution appears to significantly improve the simulated peak time (see Fig. 4c ). This might be due to the more detailed description of the river network and of the average slope over the watershed which influences the flow velocity  (Dutta and Nakayama, 2009; Vázquez et al. , 2002). The timing of the simulated peak is even better with the other experiments (R 2 T 2 C 1 -R 2 T 2 C 3 ). R 2 T 2 C 2 and R 2 T 2 C 3 give very similar results for every score of Figs. 3  and 4. In general, the best measures of the goodness of fit are obtained with R 2 T 2 C 1. The differences in the soil texture databases, which impact the water storage capacity and the ease of water to move through saturated soil, resulted in the most visible and beneficial changes in the simulated discharges.
Urbanized catchments of zone B To confirm the results obtained for zone A, the impact of various grid resolutions and soil datasets on simulations of the discharge and runoff were assessed for the more urbanized catchments of zone B. The percentages of urban cover and impervious soils in the catchments of zone B range from 16 % to 54 % (Table  3 ). The impacts were assessed for the catastrophic October 2015 event, which affected these watersheds. Extreme downpours and flash floods wreaked havoc over the French Riviera during this event. More than 270 mm of rain fell in the most affected area (Fig. 6 ). For zone B, only the upward watersheds of Pégomas, Villeneuve-Loubet, and Biot are gauged. For the coastal area where the more severe precipitation occurred, the evaluation relies on estimated peak discharges from several post-event surveys conducted in the framework of the HyMeX project  (Payrastre et al.
, 2016)  and streamflow measurements from the French HYDRO database. The observed or estimated peak discharge for each watershed is displayed in Fig. 7  together with the peak discharge simulated by all five experiments. ISBA-TOP simulated flood peaks of the correct order of magnitude, except for the discharge point E17. The timing  of the peak was also well simulated, according to Fig. 8. As in zone A, for most of the outlets, increasing the grid resolution of ISBA leads to peak values closer to the observations. However, contrary to zone A, the discharge peak values simulated by R 2 T 2 C 2 and R 2 T 2 C 3 were significantly different for several watersheds in zone B. Therefore, for these watersheds, replacing the mixture of bare soil, bare rock, and temperate broadleaf deciduous and swamp areas with only rock over the urban land use type patches had an impact.
Conclusions The representation of the soil and land properties in hydrological models is crucial for flash-flood simulations in addition to other data concerning the rainfall and the initial state of the soil moisture. The impact of these terrain descriptors on predictions in terms of both the spatial and temporal distributions of the runoff has not been fully explored. In this study, different sources of soil texture and land use data were used to describe two areas (a rural area and an urbanized area) in southeastern France using the ISBA-TOP system run at two different resolutions (300 and 1000 m). The model performances, especially in terms of the runoff simulations, are difficult to assess. The results were analysed to rank the impacts of alternative physiographic maps for flash-flood modelling purposes at the catchment scale and at the local level.
Discharge measurements, as well as proxy data such as post-event surveys and high-water marks, were used depending on their availability. The main conclusions from this study are as follows. -Changing the resolution of ISBA-TOP leads to differences in terms of the simulated river discharge and the spatial runoff. Higher resolution (300 m) simulations give more accurate results. -The simulated discharge values are often more affected by differences in the soil texture databases than differences in the land use databases, especially in rural areas. -No significant difference in the peak time was found when comparing the different 300 m experiments. -Land cover and soil texture influence locally the processes in the catchments. Their spatial variability has an impact on the preferential flow paths, the flow velocities, and the water storage.
The complexity of the inter-actions between processes at the catchment scale does not allow us to clearly conclude on how land cover and soil texture induce differences in simulated flows. -Finally, in this study, the best results were obtained using SRTM data for orography, LUCAS data for soil texture, and ECOCLIMAP-II data for land cover at a resolution of 300 m (i. e. the R 2 T 2 C 1 experiment). These conclusions need to be considered with caution because the sample of events and catchments was limited, especially for the urbanized area. Moreover, it would be interesting to compare these results with those that can be obtained using other hydrological models dedicated to flashflood modelling. For example, the higher sensitivity to soil texture than to land cover might depend on how the vegetation is treated in the model.
Note that, for calibrated models, this impact might be "corrected" during the calibration procedure. Therefore, for a different dataset, the calibrated model needs to be recalibrated. In any case, for the future development of flash-flood modelling and forecasting, the impact of soil datasets should be taken into account in the uncertainty quantification, even though this impact is less significant than those associated with the rainfall and initial soil moisture. The lack of information with regard to flash floods in ungauged catchments may constitute a real barrier to the evaluation of the simulated hydrologic responses. Fortunately, data such as impact data from post-event surveys, "connected" measurements, and georeferenced data from social networks might be useful for enlarging the capacity of the model output assessments, especially during extreme events.
2. 3. The Shuttle Radar Topographic Mission (SRTM) 90 m digital elevation data, originally produced by NASA, are available at the Consortium for Spatial Information of the Consultative Group for International Agricultural Research (CGIAR-CSI) geoportal (http://srtm. csi. cgiar. org/, last access: 28 March 2019). The Harmonized World Soil Database (HWSD, version 1. 2) was provided by the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA). Precipitation rain gauges and initial conditions (soil water and soil temperature) data are provided by Météo-France and are available in the HyMeX database (https://doi. org/10. 17616/R3M34X). Streamflow measurements are provided by the French HYDRO data bank (http: //www. hydro.
Acknowledgements. This research was performed in the framework of the  HyMeX programme (MISTRALS grants ) and is a contribution to the  French  project  PICS  ( ANR -17 -CE03 -0011 ). The authors would like to express their gratitude for access to useful data and discussions about ECOCLIMAP with  Stéphanie Faroux  (CNRM). Review statement. This paper was edited by  Giuseppe Tito Aronica  and reviewed by  Massimiliano Zappa  and  Renata Romanowicz.
1 18. 9 41 40. 2 % ECO-SG 1. 9 1. 5 6. 4 2. 6 15. 8 15. 6 32. 6 53. 6 273 landscape types, resulting from the merging of satellite data, i. e. the Corine Land Cover 2000 product over EU coun- tries at a resolution of 100 m, the Global Land Cover 2000 global database, and the SPOT-VEGETATION satellite data. In ISBA-TOP, the urban cover of ECOCLIMAP-II is consid- ered to be a rocky cover to simulate the impervious surfaces of towns. ECOCLIMAP Second Generation (ECOCLIMAP-SG; https:/.
Objective: Patient recruitment in clinical trials is challenging with failure to recruit to time and target sample size common. This may be caused by unanticipated problems or by overestimation of the recruitment rate. This study is a systematic review of statistical models to predict recruitment at the design stage of clinical trials. Study Design and Setting: The Online Resource for Recruitment research in Clinical triAls database was searched to identify articles published between 2008 and 2016. Articles published before 2008 were identified from a relevant systematic review. Google search was used to find potential methods in gray literature. Results: Thirteen eligible articles were identified of which, 11 focused on stochastic approaches, one on deterministic models, and one included both stochastic and deterministic methods.
Introduction Successfully recruiting the prespecified number of trial participants remains a difficult challenge negatively impacting all stakeholders  [1]. Twenty-six percent of trials funded by the Swiss National Science Foundation were prematurely discontinued because of slow recruitment  [2]. Despite investment targeting recruitment difficulties  [3, 4] , there has been no improvement over time and 45% of trials supported by two prestigious UK funding bodies, fail to meet their original recruitment targets  [5]. Recruitment of patients in trials is complex and participation rates that are lower than expected may be due to several factors  [6]. Common reasons for failure to recruit are delays with contracting, center initiation delays, inadequate planning, insufficient staff, and overoptimistic expectations.
Identification of potentially eligible studies The search strategy was split by articles published before or after July 2008. The systematic review of Barnard et al. [10]  was used to identify eligible articles published up to July 2008 (see Fig. 1 ). This review includes models for recruitment prediction at the design stage or during ongoing recruitment. Articles published after July 2008 to 2016 were identified by searching the ''Recruitment prediction'' domain in the Online Resource for Recruitment research in Clinical triAls (ORRCA)  [11] , a searchable database of research relevant to clinical trial recruitment. The development of ORRCA is described elsewhere  [12]. At the time of the ORRCA search (June 2018), it included publications up to 2016. References of eligible articles were also reviewed for additional articles including book chapters.