dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The  UMS SAFIRE  is acknowledged for supporting the ATR-42 aircraft deployment and for providing the aircraft meteorological data. The  POLARCAT-France  and CLIMSLIP projects were funded by  ANR ,  CNES ,  CNRS/INSU  and  IPEV. The FLEXPART team ( A. Stohl ,  P. Seibert ,  A. Frank ,  G. Wotawa ,  C. Forster ,  S. Eckhardt ,  J. Burkhart ,  H. Sodemann ) is acknowledged for providing the  FLEXPART code. NASA, CNES , the  ICARE  and the LARC data centre are gratefully acknowledge for supplying the CALIPSO data. Edited by:  G. Vaughan.
Double balloon endoscopy Since 2001, DBE has been introduced into clinical practice as a modification of the push method and as a method enabling endoscopic visualization of the entire small-bowel with a success rate of 40%-80%  [13, 42, 43]  (Figure  5C  and 8B ). One balloon is attached to the tip of the endoscope and another is located at the distal end of an overtube. The balloon facilitates the insertion of the endoscope, which can be advanced much further into the small intestine than with push enteroscopy. A main advantage of DBE is that diagnostic and therapeutic interventions can be combined in a single procedure, although to date, there is limited data to support such an approach  [13]. Before the introduction of DBE, the removal of polyps was possible only by intraoperative endoscopy, and in the case of proximal small bowel polyps, surgical resection or push enteroscopy was performed.
The weight of each fraction was recorded and used to calculate the percent of the total sediment sample that fell within each size category. We grouped the three smallest sieves into a "fines" category and, data from the three largest sieves were combined to make a "sand" category (Wentworth 1922). Samples for organic content were burned for four hours at 550° C in a muffle oven, placed in a desiccator and immediately weighed upon removal from the desiccator.
To create a blotted dry vs. oven dried conversion for S. alterniflora, we collected 47 leaves, and treated them exactly in the same manner as described above (rinse, blot, weigh). Each leaf was then dried at 60° C for 48 hours and re-weighed. The resulting relationship (oven dried=0. 1817*blotted dry + 0. 2105, R 2 = 0. 8725) was used to convert the initial blotted dry values to equivalent oven dried values. We used these data to determine the k decomposition constant (rate of change in mass over time) from the slope of the regression for each replicate plot. of S. alterniflora in each plot  (Mews et al. 2006; Conover 2011).
To address these questions, we assembled forest-tree data sets from comprehensive inventories in Canada, France, the United States, and Switzerland. We used repeated census measurements to model changes in the density of tree populations as a function of density in the first census and topo-climatic variables. From these analyses, we obtained the intrinsic population growth rate (r) and the carrying capacity (K) of each population. We then estimated occurrence probability (P occ ) with species-specific SDMs for each of the study areas using the same topoclimatic variables. As a surrogate for competitive ability, we collected published information on all species' shade tolerance, assuming that high tolerance denotes strong competitive ability. Finally, we investigated the relationships between demographic parameters and occurrence probability, focusing either on all estimated data or only on the upper limit of these relationships (because stochasticity or disturbance might cause suboptimal performance in some populations), and analysed differences in relationships between shade-tolerance classes.
Materials and Methods We compiled forest-tree datasets from four different areas in North America and Europe: Québec, Western United States, France and Switzerland (Fig. S1 ). For each dataset and for each species independently, we estimated the relationship between occurrence probability and demographic parameters (see Fig. 1  for a general workflow of the analysis). We used the same climatic variables in the SDMs to estimate occurrence probability and in the demographic models to estimate demographic parameters (Table  S1 ). We only retained plots not affected by harvesting between the censuses. Data for the distribution models consisted of measured presence or absence at each sample plot for each study area. In species with strongly size-structured populations (such as trees), the number of individuals is a poor descriptor of reproductive rates and the intensity of competition.
Because the interval between two censuses is not constant, we included the elapsed time between them to have an approximate measure of basal area increment per year. Overall, we had 31 species with more than 100 observations of basal area increment available for further analyses. Western US-The USDA Forest Service Inventory and Analysis (FIA) program maintains an extensive census of tree characteristics on public and private lands across the Unites States (http://www. fia. fs. fed. us/). Data for 16 selected conifer species in the western Unites States were extracted from this dataset. This subset was chosen based on sufficient presenceabsence data information with high detection probability and repeated censuses. Information on the number and condition of trees is collected on plots approximately 0.
4 ha in size. There was approximately one plot per 2,400 ha, although spatial intensity of sample plots varies across the US. The FIA uses a panel rotation sample scheme for resampling of plots, with plots in the western study region resampled once every 10 years. Population densities and population growth were calculated from basal areas as described above. Data for analyses were restricted to western North America's mid-latitude dry domain (after Bailey 1996) consisting of 1830 plots. France-The French National Forest Inventory (http://inventaire-forestier. ign. fr) comprises a network of temporary plots established on a sample of grid of approximately 1000 × 1000 m (plot randomly located in a square of 450m around the centre of the cell) covering the French national territory between 2005 and 2011.
The climate varies between Mediterranean, oceanic, and continental and is also strongly affected by elevation. For each measured tree, stem circumference, species, status (alive or died in the last 5 years), and radial growth over five years were recorded. The radial growth was determined from two short cores taken at breast height for 41 species. The basal area per species at the time of measurement was computed based on the circumference of each living tree. We then computed the basal area five years earlier using the following information to reconstruct the stand: the five years radial growth of living trees, the trees that died between censuses, and the trees that recruited between censuses. We selected plots without harvesting or planting between censuses leading to 22,592 plots. For more details on the data and the computation of basal area see the Supplementary materials.
The best of the two alternative models for each dataset was selected using the small sample size corrected Akaike's Information Criterion, AICc,  (Akaike 1974, Burnham and Anderson 2002). This best model was then used to calculate the net reproductive rate r of each plot (the predicted change in density for N t = 0) and carrying capacity K (the N t at which change in density is 0). In other words, r corresponds to the prediction of Equ. 3 or Equ. 4 for N t =0. Since we took the log ratio of N t+1 and N t , it is actually the exponential of the prediction. (Equ. 9) For datasets with a hierarchical sampling block design (to account for multiple plots in Western US and for multiple measurements in Québec and Switzerland), we fitted the regressions as linear mixed-effects models including a random effect of block  (Pinheiro and Bates 2000).
Species distribution models We used generalised linear models (GLM) with linear and quadratic effects of climatic variables to estimate occurrence probability for each species. Although GLMs are not necessarily the best performing models for SDMs  (Elith et al. 2006) , we chose them to avoid overfitting and to ensure comparability to the modelling procedure for demographic parameters. Because identification of a most parsimonious model was not the goal of the paper, we ran the GLMs on the full set of selected variables for each dataset (Table  S1  in supplementary material). Models were evaluated by internal validation on the training dataset using the area under the curve (AUC,  Swets 1988). In general, SDMs were relatively good at predicting current tree distribution in the four study areas.
This approach allowed us to have the same number of data points for each species and dataset. For both strategies, we tested for linear and sigmoid relationships between P occ and the demographic parameters (r, K and N) and retained the best model using AICc. This analysis was done for each species independently and the results were summarised by light-competition strategy. We recorded the form of the relationship selected (linear vs. sigmoid), the slope (decreasing vs. increasing) and the R 2 of the selected regression. All calculations were carried out with R. 3. 0. 1 using the stats and ggplot2 packages (Wickham 2009, R Development Core Team 2013).
While this situation is possible in nature (e. g. if a species is outside of its niche), it most likely arose because a poor fit of the model to the data (see Fig. S6  and Fig. S7 ). Another non-exclusive reason is the occurrence of natural disturbances. While these are unlikely to generate a negative growth rate or carrying capacity (it would require a majority of the plots being disturbed), it could nonetheless generate a significant amount of noise. This is particularly true for the boreal forest of Quebec, where a severe outbreak of the spruce budworm affected the fir and spruce stands during the 1980-1995 period. This massive and synchronized disturbance is a likely cause of the low fit to the model we find in this dataset. Logging might also interrupt the dynamics of the stand, but it should be minimal in this dataset since the plots are supposed to be protected.
The bootstrap estimates for this region showed that there was a very high uncertainty for these parameters. The elevated variance in the parameter b might result in a bias estimate of K because of Jensen's inequality. If only b is varying, then it should lead to a systematic underestimation of K, with the bias linearly proportion to the variance. Since a and b are covarying, the consequence of their joint variability on K is not straightforward to estimate  (Gravel et al. 2011). The solution to the problem would be evaluate the parameters using a Markov chain Monte Carlo and to compute the posterior distribution of K. Running this scheme for all datasets and all species is however far beyond the scope of the paper. The results from Quebec are the most likely affected by this problem because of the low quality of the fit.
Equ. 5)(Equ. 6) To estimate K, we made a prediction from Equ. 3 or Equ. 4 at N t =1 to extract the value of (a +b) and then subtract a to isolate b. (Equ. 7) (Equ. 8) Since some datasets had different time intervals between censuses, we standardized r by time-interval (Δt).
Acknowledgments The research leading to this paper had received funding from the  European Research Council  under the European Community's  Seven Framework Programme FP7 /2007-2013 Grant Agreement no. 281422  (TEEMBIO). This study arose from two workshops entitled 'Advancing concepts and models of species range dynamics: understanding and disentangling processes across scales'. Funding was provided by the  Danish Council for Independent Research -Natural Sciences  (grant no. 10-085056 ). C. M. acknowledges funding from  National Science Foundation (NSF)  grant  1046328  and  NSF  grant  1137366. K. M. acknowledges funding from  NSF  grant  DEB-0919230. DZ acknowledges funding by  DFG  grant  WI 3576/1-1. The work of FMS was supported by  German Research Foundation (DFG)  grant  SCHU 2259/5-1.
GK was supported by a  Marie Curie International Outgoing Fellowship  within the 7th European Community Framework Program (Demo-traits project, no. 299340 ). We are thankful for access to forest inventory data of France, Quebec,  Western US , and  Switzerland. Most  of the computations presented in this paper were performed using the CIMENT infrastructure (https://ciment. ujf-grenoble. fr), which is supported by the  Rhône-Alpes region  (GRANT CPER07_13 CIRA: http://www. ci-ra. org)and  France-Grille  (http:// www. france-grilles. fr). WT belongs to the LECA, part of  Labex OSUG@2020  ( ANR10 LABX56 ).
( Equ. 5)(Equ. 6) To estimate K, we made a prediction from Equ. 3 or Equ. 4 at N t =1 to extract the value of (a +b) and then subtract a to isolate b. (Equ. 7) (Equ. 8) Since some datasets had different time intervals between censuses, we standardized r by time-interval (Δt).
Adverse drug events (ADE) may lead to hospital admission, and in some cases admission to an ICU is mandatory. We conducted a systematic review dealing with the incidence of ADE requiring ICU admission in adult patients, the reference population being all ICU admissions. Medline, Embase and Web of Science databases were screened from January 1982 to July 2014, using appropriate key words. Only original articles in English reporting the incidence of ADE requiring ICU admission in adult patients among total ICU admissions were included. Article eligibility was assessed by two independent reviewers, a third being involved in cases of disagreement. All reported characteristics (type of ICU, characteristics of patients, incidence of ADE, severity and preventability, drugs involved, causality) in the selected articles were collected for the review.
Information sources On 4 July 2014 we conducted a combined search in the Embase database, in the Medline database via PubMed and in the Web of Science. Using relevant search terms related to ADE responsible for admissions to the ICU, we searched for all publications in English from 1 January 1982 to 4 July 2014. Complementary searches were made to identify potential additional articles: the reference lists of retrieved articles as well as their citing lists (the latter being issued from the Web of Science) were handsearched.
Risk of bias assessment To assess the risks of bias in individual studies, a specific list of items adapted to the scope of ADE responsible for ICU admission was designed by three authors (PAJ, PH and CP). The list was based on a combination of Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)  [7]  and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)  [8]  items. Two of us (PAJ and PH) independently assessed each individual study by granting for each item either 1 point when reporting of corresponding data was complete or 0 points when corresponding data were missing or reporting was incomplete (disagreements were solved by consensus discussion). Scores per study (proportion of items completely reported) were considered for assessing the risk of bias in individual studies.
Acknowledgements The authors are indebted to  D Benoist  and  C Weill  at the  Bibliothèque Interuniversitaire de Santé  for their helpful assistance in bibliographic searches. We have no funding to report. Author details 1  Institut Pierre Louis d'Epidémiologie et de Santé Publique, Sorbonne Universités, UPMC Univ Paris 06 ,  UMR_S 1136 , F-75013 Paris, France. 2  Institut Pierre Louis d'Epidémiologie et de Santé Publique, INSERM ,  UMR_S 1136 , F-75013 Paris, France. 3  AP-HP, Hôpital Saint-Antoine ,  Service de Pharmacie , F-75012 Paris, France. 4  Univ Paris-Sud ,  Faculté de Pharmacie , F-92296 Chatenay-Malabry, France. 5  AP-HP, Hôpital Saint-Antoine, Service de Réanimation, F-75012 Paris, France. 6AP-HP, Hôpital Saint-Antoine, Unité de Santé Publique, F-75012 Paris, France.
Context. GJ 436b is one of the few transiting warm Neptunes for which a detailed characterisation of the atmosphere is possible, whereas its non-negligible orbital eccentricity calls for further investigation. Independent analyses of several individual datasets obtained with Spitzer have led to contradicting results attributed to the different techniques used to treat the instrumental effects. Aims. We aim at investigating these previous controversial results and developing our knowledge of the system based on the full Spitzer photometry dataset combined with new Doppler measurements obtained with the HARPS spectrograph. We also want to search for additional planets. Methods. We optimise aperture photometry techniques and the photometric deconvolution algorithm DECPHOT to improve the data reduction of the Spitzer photometry spanning wavelengths from 3-24 µm.
g. ,  Charbonneau et al. 2005; Deming et al. 2005). Occultation spectrophotometry also yields strong constraints on the planet's orbit (e. g. ,  Campo et al. 2011). The bulk of the first measurements concerning exoplanetary atmospheres were gathered by the Spitzer Space Telescope  (Werner et al. 2004). Spitzer was equipped with an 85-cm diameter telescope and three instruments to provide imaging and spectroscopic capabilities from 3. 6 to 160 µm. These instruments are the Infrared Array Camera (IRAC,  Fazio et al. 2004 ), the Infrared Spectrograph (IRS,  Houck et al. 2004) , and the Multiband Imaging Photometer (MIPS,  Rieke et al. 2004). Spitzer data are unique thanks to their large infrared wavelength range. It was fully operational from 2003 to 2009. It has been in the so-called Warm mission since its cryogen was exhausted in May 2009, so that only two channels in the near-infrared (3.
6 and 4. 5 µm) are still operating. While ground-based facilities are now able to measure the thermal emission of some highly irradiated planets in the near-infrared (e. g. ,  Gillon et al. 2009; Croll et al. 2011; Bean et al. 2013) , we have to wait for future spacebased facilities like the James Webb Space Telescope  (Gardner et al. 2006)  to complete these data at longer wavelengths. Among the transiting extrasolar planets, GJ 436b is one of the few low-mass planets orbiting a star which is small and nearby enough to allow an advanced characterisation. It is a ∼22 Earth mass planet discovered by radial velocity in orbit around a nearby (∼10 pc) M2. 5-type dwarf  (Butler et al. 2004 ). Its transit was originally detected by  Gillon et al. (2007c)  so that GJ 436b became the first confirmed warm Neptune.
Shortly after,  Beaulieu et al. (2011, hereafter B11)  analysed Spitzer observations of GJ 436b transits obtained at 3. 6, 4. 5, and 8 µm. They measured a different planetary radius at 4. 5 µm than at 3. 6 and 8 µm, what made them conclude a high abundance of methane. In addition, B11 re-reduced the same occultation data as S10 at 3. 6 and 4. 5 µm and obtained significantly different results. S10 and B11 thus strongly differ in their interpretation, in particular on the IRAC 3. 6 and 4. 5 µm data during occultation. Both teams mentioned the need of a new dataset at those wavelengths to check their own theories. Later  Knutson et al. (2011, hereafter K11)  invoked evidence of stellar variability to explain their (and B11's) planetary radius measurement discordance. They also improved the system parameter estimates, notably thanks to a larger Spitzer dataset.
It is not the first time that different teams have obtained conflicting results from the same Spitzer dataset. One can mention the detection  (Tinetti et al. 2007; Beaulieu et al. 2008)  or nondetection  (Désert et al. 2009 ) of water vapour in HD 189733b. In this context, we have decided to perform this new independent analysis of archived Spitzer data. The main concept of this project is not only to perform global Bayesian analysis of the extensive Spitzer datasets available for GJ 436b and to compare them with the results previously obtained by different teams for subsets of the available data, but also to complement them with our new HARPS RV measurements to derive stronger constraints on the planetary properties. When combined with the stellar properties, RV measurements provide orbital parameters and the minimal planetary mass.
Our motivation is also to better understand the limitations of space IR observations for the study of exoplanet atmospheres. Such an effort is especially important in the context of the future launch of JWST  (Seager et al. 2009) , in order to optimise the use of its capacities for the studies of other worlds. In Sect. 2 we present all the observations obtained in the Spitzer programs targeting GJ 436b and the way we performed their data reduction. The new HARPS RVs are presented in the same section. Section 3 summarises the analysis of the Spitzer data and the RV measurements. Section 4 discusses the possible evidence of companions, while Sect. 5 presents an atmospheric model based on our emission and transmission spectra. We discuss our results in Sect. 6, before concluding in Sect. 7.
6, 4. 5, and 8. 0 µm IRAC bands (ID : 50051, B11, K11). Furthermore, the program 60003 attempted to observe occultations of GJ 436b at 3. 6 and 4. 5 µm. Wrong ephemerides were used, and the occultation phase was not observed. Lately, S. Ballard (ID : 541, Ballard et al. 2010a ) attempted to identify a third body in the system during an 18hour observation at 4. 5 µm. Finally, J. Harrington et al. obtained two more observations at 3. 6 and 4. 5 µm with Warm Spitzer during two occultations of GJ 436b to try to confirm the conclusions given by S10 and S12. In this section we present the photometric data with the different tested data reduction strategies on each instrument and our updated HARPS RVs dataset. For all Spitzer instruments, we use the images calibrated by the standard Spitzer pipeline and delivered to the community as Basic Calibrated Data (BCD).
An exposure time of 0. 08 s was used at 3. 6 µm, 0. 08 and 0. 32 s at 4. 5 µm, and 0. 32 s for the two other channels. For more details on these IRAC observations we refer the reader to Table  1 ,  Gillon et al. (2007a) ,  Deming et al. (2007) ,  Demory et al. (2007) ,  Ballard et al. (2010a) , S10, K11, and S12. We reduce all the IRAC data with our EXOPHOT PyRAF 4  pipeline to get raw light curves. We give a general overview of its routines below. For every subarray image, a 2D elliptical Gaussian profile fit is performed to determine the centre of the GJ 436 PSF. Aperture photometry is then accomplished with the IRAF/DAOPHOT 5  software  (Stetson 1987). Other centring approaches are tested (e. g. , centroid fit and different double 1D Gaussian adjustments 6  ) but with lower performance (Fig.
For every block of 64 subarray images, we reject the discrepant values for the measurements of the x-and y-position, and the stellar and background flux using a 3-σ median clipping. We generally discard up to 2 measurements from the 64 subarray images. Then the remaining measurements are averaged. We take the photometric error on the mean of the photometric error Fig. 2. Evolution of the photometric precision for raw data with the aperture radius according to different centring techniques in a complete dataset (e. g. , AOR : 42614016). The 2D Gaussian adjustment (green asterisks) provides the best photometric precision for raw data in comparison to a centroid fit (magenta crosses), a double 1D Gaussian fit from the previously computed x-or y-FWHM (cyan boxes and red diamonds respectively), and a 1D Gaussian fit with a fixed FWHM (blue stars), in particular for small aperture radii.