dataesr/scientific-paragraphs-categorization huggingface dataset:
The EnOI-Clim performed poorest, especially for dry regions, where the climatological spread was found to be too low. However, there should be potential to improve the EnOI-Clim results by, for example, looking more into sampling methods of the background error. The impact of the atmospheric forcing on the analysis results was evaluated by assimilating SMAP data using the EnKF and the EnOI with MERRA-2 (model derived) and NLDAS (observation corrected) atmospheric forcing as model input. The largest improvements from an eOL run in correlation and ubrsmd were seen when we used the MERRA-2 atmospheric forcing data. This dataset had no precipitation correction, yet the initial skill was high when comparing the eOL forced by MERRA-2 data to in situ stations. The EnKF and the EnOI assimilation of SMAP showed statistically significant improvements in the surface zone correlation at the 95% level, while root-zone improvements were only found for USCRN stations located in regions favourable for soil moisture DA.
Our results indicate that land DA of SMAP-L2 data can account for random errors in the atmospheric forcing dataset. We base this statement on the fact that our EnKF and EnOI analysis using MERRA-2 forcing; both had comparable skill to the eOL run using NLDAS atmospheric forcing data. This could be important for regions of the world where high-quality meteorological forcing and observation data are missing, such as over the high northern latitudes and Africa. Finally, we assimilated the ESA CCI, SMAP and SMOS SM products using the EnOI and MERRA-2 atmospheric forcing. Here, we wanted to evaluate the satellite impact on the land DA analysis. We computed surface and root-zone correlations between the three analysis products and the in situ SM data, over regions favourable for soil moisture DA.
We found that assimilation of the SMAP-L2 product had the highest correlation skill, 0. 69 for the surface and 0. 68 for the root-zone SM. The SMOS-L2 product had the second highest overall correlation skill, 0. 67 for the surface and 0. 66 for the root-zone SM. While the ESA CCI product had the lowest correlation skill, 0. 65 for the surface and 0. 66 for the root-zone. Land DA of SMAP data were found to perform better than assimilation of SMOS and ESA CCI data. The differences between the SMAP and SMOS analysis skill were minor, and our analysis results support other studies indicating that SMAP SM data perform slightly better than SMOS SM data when compared to in situ stations  [74]. In general, our work supports other studies showing that assimilation of surface SM observations from satellites could improve the surface zone SM in land surface models  [8, 12, 17, 23, 77].
Background Dupilumab (monoclonal antibody inhibiting IL-4/IL-13 signalling) is approved for use in adolescents aged ≥ 12 years with inadequately controlled moderate-to-severe atopic dermatitis (AD). Dupilumab significantly improved AD signs/symptoms in a 16-week, randomised, placebo-controlled phase III trial in adolescents (NCT03054428). Objectives To characterize the pharmacokinetics of dupilumab, and long-term safety and efficacy in adolescents. Methods This was a global, multicentre, phase IIa, open-label, ascending-dose, sequential cohort study with a phase III open-label extension (OLE) in adolescents with moderate-to-severe AD. In the phase IIa study, patients received one dupilumab dose (2 mg kg À1 or 4 mg kg À1 ) and 8 weeks of pharmacokinetic sampling. Thereafter, patients received the same dose weekly for 4 weeks, with 8-week safety follow-up.
Study design and patient selection The first study, a phase IIa, multicentre, open-label, ascending-dose, sequential cohort study (R668-AD-1412; NCT02407756), was conducted at multiple centres in Europe (Czech Republic, Hungary, Germany, Poland, the U. K. ) and Canada. The study consisted of a screening period of up to 35 days, a baseline visit and two treatment phases: in part A, patients received a single dose of dupilumab followed by an 8-week sampling period for systemic drug concentration; in part B, patients received four weekly doses followed by an 8-week safety follow-up period (Figs S1 and S2; see Supporting Information). Patients were required to discontinue systemic treatments for AD (oral corticosteroids and non-steroidal immunosuppressants) for at least 2 weeks prior to the baseline visit.
In addition, patients who did not complete part A per schedule, for example patients who received systemic corticosteroids or systemic nonsteroidal immunosuppressive agents as rescue treatment within 2 weeks of the scheduled start of the repeat dose, had a 2-week washout period of the rescue medication prior to starting part B of the study. The second study, an ongoing phase III OLE (R668-AD-1434 LIBERTY AD PED-OLE; NCT02612454) enrolling paediatric patients who participated in previous dupilumab AD trials [the present phase IIa study, and phase III (NCT03054428) and phase I (NCT03050151) studies], includes centres from Canada, the Czech Republic, Germany, Hungary, Poland, the U. K. and the U. S. A. The study consists of a screening period (day À28 to day À1), a treatment period that lasts until regulatory approval of the product for the age group of the patients in their geographical region, and a 12-week followup period (Fig.
Missing values during the first 4-week repeat-dose treatment period of part B up to the end-of-treatment visit were imputed by the last-observation-carried-forward method. After the end of treatment in part B, no missing data imputation was made. For the phase III open-label extension, an allobserved method was employed, regardless of whether rescue treatment was used or if data were collected after withdrawal from study treatment. No missing values were imputed. SAS version 9Á2 (SAS Institute Inc. , Cary, NC, U. S. A. ) was used for all analyses.
e. were not treatment na€ ıve), which may have influenced outcomes in both studies. As the number of patients included in the study was small and efficacy was not the primary objective, P-values vs. baseline were not reported. Finally, it should be noted that although the exposure with the 2 mg kg À1 weekly regimen was comparable to that in adolescents treated with the every-two-weeks regimen in the phase III study (Simpson et al. submitted for publication), the regimens used in the phase IIa and OLE studies were not the actual currently approved every-two-weeks regimen in the adolescent patient population. In summary, in adolescents with moderate-to-severe AD, dupilumab exhibited a PK profile similar to adults. Findings from the OLE support the long-term safety and efficacy of dupilumab in adolescents with moderate-to-severe AD, extending and reinforcing the findings from a randomized, double-blind, placebo-controlled phase III trial (NCT03054428) and recently published case series (Simpson et al.
Acknowledgments This research was sponsored by  Sanofi  and  Regeneron Pharmaceuticals, Inc. ClinicalTrials. govIdentifiers:  NCT02407756  and  NCT02612454. Editorial assistance was provided by  Mihai Surducan, PhD , and  Manuela Pigors, PhD , of  Excerpta Medica , funded by  Sanofi Genzyme and Regeneron Pharmaceuticals, Inc. The authors thank the patients and their families for their participation in this study; their colleagues for their support; the principal investigators (see Appendix S2); and  Shikha Bansal ,  Elizabeth Bucknam ,  Steve Chen ,  Jennifer Foy ,  Jacqueline Kuritzky ,  Deirdre Mulhearn ,  Nelson Rita ,  George Vlamis ,  Linda Williams  and  Rick Zhang  of  Regeneron Pharmaceuticals, Inc. , and  El-Bdaoui Haddad ,  Elizabeth Laws ,  Junxiang Luo  and  Heribert Staudinger  of  Sanofi Genzyme  for their contributions.
Diffusion Analysis Parametric maps of fractional anisotropy (FA) and mean diffusivity (MD) maps were generated from the DTI acquisition as described elsewhere  [21]. All regions of interest (ROIs) were mapped from the structural to the diffusion space using NiftyReg (http://sourceforge. net/projects/niftyreg/) applied using TractoR software (http://www. tractor-mri. org. uk/ diffusion-processing). In the registration process, we used trilinear interpolation, preferred in reconstruction algorithms  [22] , given the precision required in the space manipulation of such small ROIs to avoid shape and size distortion. The resultant ROIs were binarized and, then, multiplied by the FA and MD maps to calculate the median values of FA and MD in each ROI. To evaluate the effect of partial volume effects of the RSSI and "control" ROIs with CSF, the areas of intersection of these ROIs with a binary mask of CSF in the diffusion space, generated from a previous study  [21] , were removed and median values were also obtained from these "noCSF" resultant ROIs.
DCE MRI Analysis The area under the curve (AUC) of the signal enhancement curves E(t) from t = 0 (administration of contrast agent) to t = 24 min, which is a semi-quantitative measure of contrast uptake in tissue, was used as the estimate for BBB leakage. It was measured separately in CSF, normal-appearing white and deep gray matter, WMH, and RSSI, which were delineated semiautomatically on co-registered images by an image analyst (MVH) using the in-house developed software MCMxxxVI (https://sourceforge. net/projects/bric1936/). All segmented tissue masks were manually checked and edited as necessary. In order to correct for inter-subject variability of the vascular input, the AUC of the respective regions of interest was normalized to the AUC of the vascular signal enhancement curve in the superior sagittal sinus yielding the blood-normalized AUC, as previously reported  [23, 24].
Statistical Analysis Statistical analyses were performed using the Statistical Package of the Social Sciences (IBM SPSS  Statistics 23). The level of significance was set at p < 0. 05. The Kolmogorov-Smirnov test and visual inspection of histograms assessed normality of data distribution. Groups were compared by the chi-square test (for nominal data), Students t test (for continuous, normally distributed variables), or the Mann-Whitney U test (for non-normally distributed variables). Patients were dichotomized according to median split of the primary outcome variable cavitation index. Correlation analysis was performed using Spearman correlation. After logarithmic transformation of the target variable cavitation index, we also performed two linear regression analyses based on our pre-specified hypotheses and the results from univariable analyses.
Results We identified 60 stroke patients (mean age 65. 1 ± 10. 6 years, 65% male) with a single symptomatic RSSI who fulfilled the inclusion criteria. DCE MRI data were unavailable in one patient, therefore 60 patients contributed to cavitation and DTI, and 59 to BBB analysis. Further clinical and neuroimaging characteristics of the study cohort are shown in Table  1. At 1-year post-stroke, 44 patients (73%) showed some evidence of cavitation on FLAIR-weighted MRI scans. This number increased to 50 (83%) when additionally considering T2-weighted scans. On high-resolution isotropic T1 scans, 56 (93%) patients had some cavitation of the index RSSI (Fig. 1 ). The remaining four patients also had small T1 hypointensities, which became visible after lesion magnification (Fig. 2 ). These T1 lesions were included in the calculation of the Median cavitation index at 1-year follow-up was 7% and ranged from 1 to 36% (median RSSI lesion volume on FLAIR at baseline 1000 mm 3 , IQR 942 mm 3 ; median cavity volume at 1 year on T1 65 mm 3 , IQR 78 mm 3 ).
The cavitation index is similar to the "black hole ratio" used in multiple sclerosis to assess rates of tissue destruction and ongoing disease activity  [23, 34, 35]. Our study also benefitted from the prospective recruitment of consecutive stroke patients, a thorough clinical workup and uniform, pre-specified MRI protocols at given time points on the same scanner. We also have to consider several limitations. First, sample size was moderate, which potentially also limits the explanatory power of multivariable analyses. On the other hand, this is the largest study that has analyzed tissue outcome on RSSI in detail. Although MRI ratings have been performed without knowledge of clinical data and most MRI features were available from prior analyses, RSSI fate was rated by one single rater. However, inter-rater reliability measurements between two observers with different experiences and different methods revealed consistent results regarding cavitation volume-the main tissue outcome parameter of this study.
This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the hospital's ethics committee (University Hospital of Angers, France). All patients gave written informed consent. Clinical data including medical history, baseline characteristics and clinical presentation on admission were prospectively collected during the index hospitalization and at the 3 and 12-month follow-ups. 32 patients who presented LVR were matched with 32 no-LVR patients with regard to age, sex, left ventricular ejection fraction, infarct size, culprit artery and time to reperfusion.
Biomarker Analysis The quality of our paired dataset granted us to perform paired analysis. Unpaired analyses were run afterwards to corroborate the results. All statistical tests were generated by R Software, Version 3. 1. 1 (R Core Team, Vienna, Austria). The data are expressed as mean ± standard deviation or median with IQR for continuous variables and as frequency, with a percentage for categorical variables. We used the Shapiro-Wilk test to determine the parametric nature of the variables. The Mann-Whitney test was used for non-parametric variables and the Student's test for parametric variables. For matching patients, comparisons of non-parametric quantitative variables were performed using the Wilcoxon signed-rank test and parametric variables with the Student's t-test for matched data.
8 and 0. 05 was selected for the median AUROC and median p-value, respectively. Global performance of pPLS on our data was considered as satisfactory only if median AUROC ≥ 0. 8 and median p-value ≤ 0. 05. In this case, variable selection was conducted based on the variable importance for the projection (VIP) and loading parameters. VIP values summarize the importance of each variable for the PLS-DA model, whereas the loading values are indicators of the relationship between the y vector containing the class information (i. e. , glaucomatous or control) and variables in the X matrix (i. e. , metabolites). Variables with a VIP value greater than unity are important for group discrimination. Multivariate analysis was performed using the R package mixOmics (http://www. Rproject. org).
Instrumentation and data collection For kinematic analysis, landmarks were located by the same experimenter on the right acromion, greater trochanter, lateral epicondyle of the femur, lateral malleolus, and the fifth metatarsophalangeal joint, according to previous studies analyzing vertical jumping  [6, 24]. Squat jumps were filmed in the sagittal plane with a 2D camera operating at 100 Hz (Ueye, IDS UI-2220SE-M-GL; IDS Imaging Development System GmbH, Obersulm, Germany). The optical axis of the camera was perpendicular to the plane of motion, and the lens was located 4 m from the participant. In addition, ground reaction forces were measured with a force platform sampled at 1,000 Hz (AMTI, model OR6-7-2000, Watertown, Massachusetts, USA).
Data treatment The dynamic data were smoothed with a zero-lag fourth-order low-pass Butterworth filter with a cutoff frequency of 15 Hz  [30]  and downsampled to 100 Hz. The presence of any countermovement was visually controlled for each trial. The mean and standard deviation values of the vertical ground reaction force were determined in the first second during which the subject held the initial position. The beginning of the push-off corresponded to the instant after the first second when the vertical ground reaction force increased more than two standard deviation values above the body weight one value  [6]. The reflective markers defined a 2D kinematic model composed of four body segments (feet, lower legs, upper legs, and "head-arm-trunk") and four degrees of freedom, by gathering the left and right sides (Fig  1 ).
PDS 110 is a young disk-hosting star in the Orion OB1A association. Two dimming events of similar depth and duration were seen in 2008 (WASP) and 2011 (KELT), consistent with an object in a closed periodic orbit. In this paper we present data from a ground-based observing campaign designed to measure the star both photometrically and spectroscopically during the time of predicted eclipse in September 2017. Despite high-quality photometry, the predicted eclipse did not occur, although coherent structure is present suggesting variable amounts of stellar flux or dust obscuration. We also searched for RV oscillations caused by any hypothetical companion and can rule out close binaries to 0. 1 M s. A search of Sonneberg plate archive data also enabled us to extend the photometric baseline of this star back more than 50 years, and similarly does not re-detect any deep eclipses.
4m time to observe in Sloan g r i z. We were also assisted in these efforts by the observing campaign "Time-Domain Observations of Young Stellar Objects (TOYS)" (STA2017AB-002, PI: Aleks Scholz), which contributed 10 hr of time on the 1m LCOGT network. This includes telescopes at McDonald Observatory, Cerro Tololo, SAAO and Siding Spring Observatory. These have a 4k × 4k Sinistro camera and 24 filter options including Johnson/Cousins UBV RI and Sloan u g r i. We primarily used the 1m time to observe PDS 110 in Johnson/Cousins BV RI and Sloan u (where PDS 110 is faintest). In both 1m and 0. 4m time, we took observing blocks of 3 images in each filter around 3 times per day, with exposure times adjusted to achieve SNR ≈ 200. The data were accessed via the online observing portal, and the images and calibration files downloaded.
STELLA, Tenerife STELLA is composed of two 1. 2m robotic telescopes at the Izana Observatory on Tenerife, Spain  (Strassmeier et al. 2004 ) which focuses on long-term photometric and spectroscopic monitoring of stellar activity (e. g. Mallonn et al. 2018). The wide-field imager, WiFSIP, has a 22 × 22 arcminute FoV and took observations of PDS 110 four times per night in B, V and I filters  (Johnson)  with exposure times of 20, 12 and 10 seconds. We obtained data on 38 nights from August to October 2017. The data reduction and extraction of the differential photometry of the target followed the description in  Mallonn et al. (2018). We made use of SExtractor for aperture photometry and employed the same comparison stars for the three broad-band filters.
NGTS, Chile NGTS (Next-Generation Transit Survey) is composed of 12x20cm telescopes, each observing 8. 1 square degrees (2. 8 • × 2. 8 • ) of the sky with a wide-band filter (from 520 to 890nm) and a 2048x2048 deep-depleted CCD. Its primary goal is to achieve mmag-precision photometry in order to search for transiting exoplanets  (Wheatley et al. 2018 ). Between Julian dates 2457997 and 2458199, PDS 110 was included in one of the NGTS survey fields and continuously observed by a single camera while above 30 • elevation. A of total of 95 nights of data were collected, with a typical hourly RMS below 1%. The raw 10 second NGTS frames were processed using a custom reduction pipeline  (Chote 2018, in prep)  to extract aperture photometry using several nearby comparison stars. The data were binned to 1hr bins before being included with the other photometric data here.
ASAS-SN The All Sky Automated Survey for SuperNovae, or ASAS-SN  (Shappee et al. 2014; Kochanek et al. 2017 ) is a 20-unit network of wide-field telescopes designed to survey the entire sky in ugriz g magnitude down to magnitude 17 each night, with the primary goal of rapidly detecting supernovae. We accessed ASAS-SN data of PDS 110 data from the Sky Patrol search page 4.
FEG, Sao Paulo Observations were carried out with a 16-in Meade LX200 telescope and a Merlin EM247 camera, with V-band filter and exposure time of 5 seconds. Useful data were acquired between 2017 September 2nd and 29th, totaling 5397 images in 14 nights. Each one of the 660 × 498 pixels frames was calibrated by bias subtraction and flat field correction. The fluxes of the target and nearby stars were determined from each image through aperture photometry taking advantage of the routines provided by the IDL Astronomy Library. The magnitude was calculated using the comparison stars provided by AAVSO (usually 000-BMH-803), with an error of 0. 01 mag. To determine the time evolution of the magnitude, the data were averaged every 36 images (3 minutes cadence), avoiding any spurious variation due to instrumental or meteorological effects.
2. 1. 9 TJO, Montsec Astronomical Observatory PDS 110 was observed with the Joan Oró robotic 0. 8 m telescope (TJO) at the Montsec Astronomical Observatory in Catalonia. The TJO is equipped with Johnson/Cousins UB-VRI filters and an e2v 2k × 2k CCD with a FoV of 12. 3 × 12. 3 arcmin. Johnson B and I filters were used and several observing blocks per night with 5 exposures each were configured. The exposure times for each filter were adjusted in order to achieve SNR ≈ 300. The images were reduced using the ICAT reduction pipeline at the TJO  (Colome & Ribas 2006)  and differential photometry was extracted using As-troImageJ. The final TJO dataset contains 255 and 225 data points in the B and I filters, respectively, taken in 20 different nights between September 5th and October 9th.
SAAO The SAAO 1m was used on two nights to observe PDS 110 in 3 bands using a Sutherland high-speed optical camera  (Coppejans et al. 2013). However the small field of view (2. 85 × 2. 85 arcminutes) made reference stars difficult, and the reduction required the use of measurements submitted by other observatories for calibration. The high-cadence data (cadence from 0. 7 to 10s) allowed a search for short-period oscillations (P < 3d -1 ), however none were detected. The data were binned with a weighted mean to 7. 2-min bins before being included in the ensemble analysis. 2. 1. 12 UCL Observatory PDS 110 was observed on eleven separate nights between JD 2457996 and 2458165 from the University College London Observatory (UCLO), located in Mill Hill, London. A fully robotic 0. 35-m Schmidt Cassegrain was used with a SBIG STL-6303E CCD camera.
The minimisation function ( f min ) is defined in Equation 2. 2. f min = N a i=1 N b j=1 y a,i -(y b, j + ∆ m ) 2 σ 2 a,i + σ 2 b, j exp -(x a,i -x b, j ) 2 ) 2l 2 (1) Bootstrapping was performed to assess the increase in errors due to this method, which were added in quadrature to the flux of the adjusted points. This procedure was then performed iteratively on each dataset in each filter until the offsets converged, with the exception of our LCOGT data (and CAHA data in I-band), which we held as a fixed reference lightcurve. The result is a magnitude offset (∆m) and uncertainty for each filter, and for each telescope. NGTS data were not minimised in this way as it observed in a unique broadband filter. The computed offsets for each telescope, which have been converted to relative flux to match the lightcurves presented in the following figures, are shown in Table  A1.
During each visit, a total of four spectra were obtained, each with an exposure time of 300 seconds. The data were reduced using the ESO UVES pipeline version 5. 7. 0 through ESO Reflex. The pipeline reduced and merged spectra from each epoch were combined to increase the signal-to-noise ratio. As the wavelength range of the red arm of UVES contains strong telluric bands (including the O 2 bands), we first used the ESO Molecfit tool 8   (Smette et al. 2015)  to correct the spectra for telluric absorption. The observations were corrected for blaze-variations from epoch to epoch, by first dividing the spectra from each epoch by the spectrum of the first epoch, binning this ratio, interpolating it using a cubic spline, and finally dividing the spectra by the interpolated function. This was done for each of the arms separately.