dataesr/scientific-paragraphs-categorization huggingface dataset:
In the AOSD studies, GC reduction and/or discontinuation was reported in a majority of patients in eight of nine studies and in 33% in the remaining study. In a study by Laskari et al. [75]  that reported combined data from sJIA and AOSD patients, 12/22 (55%) were able to discontinue GCs and the median GC daily dose decreased from 22. 5 to 8. 75 mg/day. In none of the studies was a structured decision on tapering or stopping GC treatment applied, but instead relied on the physician's and the patient's decision. Hence the change in GC use might not correctly reflect the actual steroid-sparing potential. In a single-centre prospective cohort, anakinra was initiated before DMARDs, GCs or other bDMARD in 20 paediatric patients with Still's disease who failed to respond to NSAIDs  [64]. At year 1, 13/20 (65%) patients had achieved CID on anakinra treatment alone.
Disclosure statement: S. O. , L. O. K. , T. K. and K. F. -L. are fulltime employees of Sobi and holders of Sobi shares. S. J. V. has received consultancy fees from Sobi and Novartis and an unrestricted research grant from Sobi in a publicprivate collaborative grant from ZonMW on rational pharmacotherapy (20162021). Y. J. has received consultancy fees and congress fees from Sobi. P. Q. has received speakers fees from AbbVie, Novartis, Pfizer, Roche and Sobi; personal fees for consultancy from AbbVie, Lilly, Novartis, Novimmune, Sanofi and Sobi and invitation or financial support to attend congress from AbbVie, Bristol-Meyers Squibb, Novartis, Pfizer, Roche and Sobi. P. Q. takes part in two data safety monitoring boards for Sanofi and has participated as an investigator in clinical trials by AbbVie, Bristol-Meyers Squibb, Novartis, Pfizer, Roche and Sanofi.
As sources of reactive halogens, snowpacks in sea ice regions control the oxidative capacity of the Arctic atmosphere. However, measurements of snowpack halide concentrations remain sparse, particularly in the high Arctic, limiting our understanding of and ability to parameterize snowpack participation in tropospheric halogen chemistry. To address this gap, we measured concentrations of chloride, bromide, and sodium in snow samples collected during polar spring above remote multi-year sea ice (MYI) and first-year sea ice (FYI) north of Greenland and Alaska, as well as in the central Arctic, and compared these measurements to a larger dataset collected in the Alaskan coastal Arctic by  Krnavek et al. (2012). Regardless of sea ice region, these surface snow samples generally featured lower salinities, compared to coastal snow.
Snow sample collection Snow samples were collected from locations in both FYI and MYI regions across the Arctic Ocean accessed via aircraft (Figure  1 ) in April 2013,  May 2013 , February 2014 , and April 2014. Given the significant logistical challenges and cost of conducting fieldwork in the central Arctic, these samples were collected as part of the North Pole Environmental Observatory, Switchyard, and US Interagency Arctic Buoy programs, with snow sampling considered to be an add-on to the program goals, which determined the sampling locations. The dataset consists of 29 snow samples from the surface of the snowpack (top 1-3 cm), with 11 samples being collected over MYI, and 18 collected over thicker (1. 5-2 m) FYI. At five sites, vertical profiles consisting of three samples at differing snow depths were taken (Figure  S1 ).
(2012)  dataset. Examining the  Krnavek et al. (2012)  dataset shows ratios in excess of 1:200 occurred ≈15% of the time in MYI regions and less than 5% of the time in FYI regions. Given the prevalence of tropospheric BrO in satellite observations over sea ice regions in the Arctic spring (e. g. Wagner and Platt 1998; Theys et al. , 2011; Koo et al. , 2012) , our measurements suggest less enriched Br -/Cl -ratios than previously suggested by  Pratt et al. (2013)  regularly enable Br 2 production. However,  Pratt et al. (2013)  did not have available snow samples with acidic pH and enriched Br -/Cl -ratios to test Br 2 production. Prior studies suggest enhancements in the Br -/Cl -ratio are more common at low salinity, which is commonly observed in snow samples from coastal tundra and MYI regions  (Krnavek et al.
The seawater bromide to chloride mole ratio  (Millero et al. , 2008)  is plotted in red for reference. The black line indicates the approximate minimum ratio of bromide to chloride for efficient snowpack Br 2 production in prior studies  (Pratt et al. , 2013). To provide context for these data, snow sample concentrations in FYI and MYI regions north of Alaska previously presented by  Krnavek et al. (2012)      Cumulative distribution functions calculated from the  Krnavek et al. (2012)  dataset for Br -/Cl -mole ratios in snow collected over land (blue), FYI regions (red), and MYI regions (yellow). The dashed red line denotes the sea water ratio  (Millero et al. , 2008)  of these anions, and the black line denotes a ratio of 1:200, which has been observed as optimal for Br 2 production  (Pratt et al.
Acknowledgements Michael Weber ,  Natalie Cleveland , and  Eric Boone  ( University of Michigan ) are thanked for assistance with IC analysis. Melinda Webster  ( University of Washington ) is thanked for assistance shipping samples to  University of Michigan. Bill Simpson  and  Laura Krnavek  ( University of Alaska Fairbanks ) are thanked for the provision of additional snowpack composition data (Krnavek et al. , 2012). Funding information Financial and logistical support for sample collection was provided by the  National Science Foundation (NSF) 's  North Pole Environmental Observatory  ( ARC-0856330 ) and  Switchyard program  ( ARC-1022475 ). The work at  University of Michigan  was supported by the  National Aeronautics and Space Administration (NASA) Atmospheric Composition Program  ( NNX14AP44G ),  Michigan Space Grant Consortium , and the  University of Michigan Department of Chemistry.
Study Desgin For this population-based study, we used data from the Cancer Barometer conducted in France in 2010 and 2015. This national survey, using computer-assisted telephone interviews, has investigated perceptions and knowledge related to cancer, cancer prevention and cancer risk factors (alcohol, tobacco, and exposure to ultraviolet radiation) since 2005. The questions are consistent throughout the time period; however, further questions about nutritional factors (diet, physical activity, weight status, and breastfeeding) and cancer risks were added in 2010 and asked again in 2015. The survey was conducted in accordance with the Declaration of Helsinki. The complete methodology of the surveys is presented elsewhere  [41, 42].
Survey The 2010 and 2015 surveys started with sociodemographic questions and general information-related questions for the participants. The following information was collected: sex, age, city, education level, current or previous employment, and monthly family income in Euros per consumption unit (CU; i. e. , one CU is attributed for the first adult in the household, 0. 5 for other persons aged 14 or older, and 0. 3 for children under 14 years old). In the analysis, three income ranges were considered: €0-1100/CU, €1100-1800/CU, and >€1800/CU. These ranges are based on the minimum wage and national data on individual salaries, representing three social classes: low income, middle income, and high income. Regarding the links between nutritional factors and cancer risk, three main multiple-choice questions were presented: (1) "Do you think that diet has a 'very important', 'somewhat important', 'somewhat unimportant' or 'not at all important 'role in cancer development?".
Statistical Analysis All statistical analyses were conducted using SAS enterprise guide 7. 13. First, descriptive analyses were used to describe the participants' characteristics and perceptions. Proportions were described as 'very low' when under 30%, 'low' when between 30% and 40%, 'moderate' when between 40% and 60%, 'high' when between 60% and 70%, and 'very high' when higher than 70%. These cut-offs are based on a Gaussian curve, with a theoretical mean of 50, and standard deviation of 10. Chi-squared tests were performed to study differences in sociodemographic variables associated with participants' perceptions. Finally, logistic regressions were performed to evaluate the associations between sociodemographic variables and the perception of nutritional factors in cancer risk. A stepwise analysis was performed to define the explanatory variables of each regression model.
Data availability statement and protocol standards The study protocol and statistical plan is available at nejm. org/doi/suppl/10. 1056/NEJMoa1205158/suppl_ file/nejmoa1205158_protocol. pdf. Data will not be available on the web. Researchers can submit proposals for collaborative studies. The study has been approved by the Kiel and Paris University ethics committees. The trial is registered at Clin-icalTrials. gov number, NCT00354133.
Disclosure W. Schuepbach is a consultant for Medtronic, Boston Scientific, and Aleva; has served on advisory boards for Ipsen and Merz Pharma; received speaker's honoraria from Allergan and Boston Scientific; and has received unrestricted research grants from Actelion, Boston Scientific, and Medtronic. L. Tonder is employed by Medtronic Inc. A. Schnitzler has received lecture fees from Abbott/SJM, Boston Scientific, Medtronic, and AbbVie and has been serving as a consultant for Abbott/SJM; and is a government employee and receives through his institution funding for his research from the German Research Council, the German Ministry of Education and Research. P. Krack received grants and personal fees from Medtronic, Boston Scientific, and UCB, and grants from St Jude Medical France, Edmond J & Lily Safra Foundation, French Ministry of Health (PHRC),.
C. Trenkwalder  received grants from the  Michael J. Fox Foundation  and the  European Commission, Horizon 2020 , " Propag-ageing , " Mundipharma , and  UCB ; funding for consultancy from  Novartis, Benevolent, Grünenthal, Britannia , and Vifor; and speakers fee from  UCB, Grünenthal, AbbVie , and  Britannia. A. Gharabaghi  is funded by the  German Federal Ministry of Education and Research  ( BMBF 13GW0119B  and  BMBF 13GW0214B ) and the  Baden-Wuerttemberg Foundation  ( NEU005 ) and receives research support from  Medtronic ,  Boston Scientific , and  Abbott. T. Wächter received speaker honoraria from  Bial-Portela & Ca SA  and  UCB Pharma GmbH. D. Weiss  is supported by the  German Research Foundation  (DFG,  WE5375/1-3 ) and receives research support, speakers honoraria, and travel grants from  Medtronic ,  Boston Scientific , and  Abbott.
Disclosure W. Schuepbach is a consultant for Medtronic, Boston Scientific, and Aleva; has served on advisory boards for Ipsen and Merz Pharma; received speaker's honoraria from Allergan and Boston Scientific; and has received unrestricted research grants from Actelion, Boston Scientific, and Medtronic. L. Tonder is employed by Medtronic Inc. A. Schnitzler has received lecture fees from Abbott/SJM, Boston Scientific, Medtronic, and AbbVie and has been serving as a consultant for Abbott/SJM; and is a government employee and receives through his institution funding for his research from the German Research Council, the German Ministry of Education and Research. P. Krack received grants and personal fees from Medtronic, Boston Scientific, and UCB, and grants from St Jude Medical France, Edmond J & Lily Safra Foundation, French Ministry of Health (PHRC), factor for outcome when selecting patients with early motor complications for STN-DBS.
Acknowledgment Fredy Pene (Hospital Pitié-Salpêtrière, Paris, France; CRA): led and coordinated communication among sites, data review and site compliance check. Anne Bissery (Hospital Pitié-Salpêtrière, Paris, France; project manager): led and coordinated communication among sites, data review and site compliance check. Didier Bouton, PhD (Department of Clinical Research and Development, Paris, France; project manager): led and coordinated communi- cation among sites, general supervision of study flow. Mathieu Quintin (Department of Clinical Research and Development, Paris, France; project manager): led and coordinated communication among sites, general supervi- sion of study flow. Kerstin Balthasar (Coordinating Center for Clinical Trials, Philipps University Marburg; CRA): led and coordinated communication among sites, data review and site compliance check.
Statistical analysis All statistical tests were completedusing STATISTICA 10. 0 Software (Stat-Soft, Maisons-Alfort, France). Normality of distribution was confirmed using the Shapiro-Wilks W-test. determine the origin of significance when a significant main or interaction effects wereF-ratio was observed using Tukey's honest significance difference (HSD). Effect sizes were calculated as partial eta-squared (η p 2 ) for the ANOVA analysis and as Cohen's d for the paired sample ttests. Effect size (ES) was calculated to determine the magnitude of the change score and was interpreted using the following criteria: <0. 2 = trivial, 0. 2-0. 6 = small, 0. 6-1. 2 = moderate, 1. 2-2. 0 = large, and >2. 0 = very large. 34 Confidence intervals (CI 95%) for ES were also specified. Data are presented as mean ± SD and statistical significance was set at p<0.
Statistical analyses Differences between the different time steps of storage duration, as well as between the two treatment groups, were analysed using a repeated measures linear model, where time step and treatment were fixed effects, and individual and location (either holding tank for untreated individuals, or capture site for stressed individuals) were treated as nested random effects. Analyses were conducted using the nlme package  (Pinheiro, et al. , 2014)  in R (Team RDC, 2019). For the HemoCue validation, a linear regression was fit to [Hb] measurements made with the HemoCue and cyanmethaemoglobin methods. Slope and intercept vales were then compared to equivalent equations developed for teleost blood  (Clark et al. , 2008 , Andrewartha et al. , 2016). Significant differences were determined using the 95% confidence intervals (CIs) around the slope and intercept values.
Acknowledgements Shark illustrations for Figs 1-6 were done by  Erin Walsh. We thank the staff of the  Centre de Recherches Insulaires et Observatoire de l'Environnement (CRIOBE) in Moorea, French Polynesia  and the  Marine and Aquaculture Research Facilities Unit (MARFU) at James Cook University in Australia  for their assistance facilitating this work. We also thank  Kim Eustache ,  Isabel Ender  and  Björn Illing  for their help with data collection. Finally, we would like to thank two anonymous reviewers for their thoughtful feedback on an earlier version of this manuscript.
The output of the RF power combiner is further attenuated using a Minicircuit 30 dB RF attenuator to ensure that the received signal at the receiver device is within the receive power limits of the AT86RF215 radio transceiver (-5 dBm according to the data-sheet). To coordinate the actions between the different devices, a computer running Ubuntu 18. 04 LTS is used. The computer is connected to the OpenMote-B boards over USB, and executes a Python script that orchestrates an experiment. For each board, the script uploads the radio configuration to be used (i. e. , modulation type, transmit power and packet length, among others) and starts transmission/reception. The OpenMote-B boards execute a custom firmware that receives commands over UART (Universal Asynchronous Receiver Transmitter), execute the action commanded (i.
Radio Calibration The PDR is evaluated with respect to SIR between the transmit power of the data and the interferer signals. According to the AT86RF215 radio transceiver datasheet (specifically Figure  11-2 ), there is a 6 dB offset between the value of the transmit power configuration register (RFn_PAX. TXPWR) and the actual transmit power of the radio transceiver depending on the selected modulation (OQPSK-DSSS and SUN-OFDM). Such difference in transmit power between different modulations can have an impact on the obtained results. For instance, an uncontrolled higher transmit power of the data signal results in a better PDR result for that particular modulation. Similarly, an uncontrolled lower transmit power of the interferer signal also results in a better PDR for that particular modulation.
Overall, the two more similar years of 2016 and 2013 were selected 6 and 5 times out of 6. The year 2015-16 was very dry. The most remarkable information from the analog time series is that there is no other similar year in the 2007-2018 dataset. This is shown by the values of the Euclidean distance: In November and October, the score remains low (1. 39 and 1. 78), but it then increases progressively until the month of April. Despite those bad scores, the most similar year was always the year 2013, suggesting that 2015-16 would be at least as dry as 2013. The year 2017-18 shows all the signs of the onset of a drought but with a late season of rain, which canceled the drought. The closest year, 2009, was detected as early as December. Yet in February the ranking was shuffled suggesting that there could still be a change of trajectory.
The longest periods of drought are found in the driest years: 2008 and 2016. The ambiguous situations corresponding to wet soil and low vegetation or dry soil and high vegetation are particularly seen in the two months of January and February. These specific conditions could be helpful factors in identifying the beginning or the end of a drought period. A predictability analogue algorithm is proposed in order to identify statistically the year in the past closest to the conditions of a present year, based on the drought vector with VAI and MAI indices and a database of eleven years (2007-2018). The results illustrate the high potential of this approach for retrieving analogous years. The capacity for retrieving an analogous year processed from December (beginning of the agricultural season) through April increases with time.
Data and Methods Remote sensing data. Modis NDVI. We employ the temporal 16-day composite series of MODIS NDVI (MOD13Q1, Collection 6 available on http://earthexplorer. usgs. gov), with a spatial resolution of 250 m. This product is retrieved from atmosphere-corrected  39  , daily bidirectional surface reflectance observations, using a compositing technique based on product quality  40. We use the 16-day composites to ensure a high probability of a reduced cloud effect on the NDVI pixels retrieved. The entire period from 2001 to 2018 is considered in our analysis of drought. The 16-day data have been averaged to the monthly time step. ASCAT SWI. The ASCAT scatterometer radar is one of the 12 instruments carried by ESA's METOP-A satellite (launched in 2006), and operates in the C-band (5. 3 GHz), in vertical polarization.
Over land surfaces, various approaches have been proposed for estimating surface soil moisture. The Institute of Photogrammetry and Remote Sensing (IPF), Vienna University of Technology (TU-Wien) has offered a soil moisture product at a spatial resolution of 12. 5 km based on ASCAT data since 2007. The product is distributed as a daily dataset by the Copernicus Global Land Service (http://land. copernicus. eu/global/products/swi). The proposed approach is based on a change detection methodology, using normalized multi-incidence radar signals. The derived soil moisture index is between 0 and 1 (0 for dry soils, 1 for very wet surfaces). This product has been validated through analysis in several regions of the globe and by intercomparison with other satellite products. Based on the high repetitivity of these products, the Soil Water Index data (SWI) was derived from m s using the following equation (1).
SWI t m t e e for t i s i t t T i t t T i ( )/ ( )/ i i = ∑ ∑ ≤ -- -- t ( ) ( ) (1) where m s is the surface soil moisture estimate from the ASCAT scatterometer at time t i. The parameter T, called the characteristic time length, represents the time scale of soil moisture variations in units of time. A T of 20 days has shown the best fit to ground measurements. The daily data have been averaged to the monthly time step. ASCAT moisture products were validated over the central Tunisia, using continuous ground moisture measurements  15. Modis LST. In this study, we employ the monthly composite MOD11C3 series, a collection  41  having a spatial resolution of 0. 05°. This product is retrieved from atmosphere-corrected, daily bidirectional surface reflectance observations, using a compositing technique based on product quality.
The Global Administrative Areas Database (GADM, version 2. 8, Nov. 2015) was developed by R. Hijmans and colleagues at UC Berkeley (http://www. gadm. org). For our purpose, we focused on administrative levels, which are relatively homogeneous in size across the three countries of the Maghreb. Thus the levels that were chosen are the provinces for Morocco, the wilayas for Algeria and the governorates for Tunisia. The original data have been unified into a single administrative vector file and simplified for the purposes of display and analysis.
SWI where σSWI is the temporal standard deviation of SWI and SWI is the SWI average Finally the TAI is obtained from equation (4) as TAI LST LST (4) LST σ = - where σLST is the temporal standard deviation of LST and LST is the LST average. The VAI, MAI and TAI are dimensionless indices ranging theoretically from unlimited negative values (drier than normal) to unlimited positive values (wetter than normal). The indices are computed for the administrative areas. The most common way of synthesizing raster data to create vector polygons is to rasterize the vector dataset and tag each polygon. The average, sum or other calculations are then computed for the observed value of each polygon. Since the resolution of our input data ranges between 250 meters and 12 km, we preferred to compute a weighted vectorized synthesis.
Acknowledgements This study was funded by the  AMETHYST  project ( ANR-12-TMED-0006-01 ,  ANR TRANSMED program ) and the  CHAAMS  project ( ERANET3-062  CHAAMS). We thank  NASA  for providing the  TERRA-MODIS NDVI  and LST products, the  Copernicus Global Land  service for providing the ASCAT SWI products, the  ESA Climate Change Initiative  for providing the landcover map, the  Climate Hazard Group  from  UC Santa Barbara  for providing the CHIRPS dataset.
Acknowledgement This work has used the facilities and the  expertise of the (Electron Microscopy Platform) Cell Biology unit of the Imagif platform (Centre de Recherche de Gifhttp ://www. imagif. cnrs. fr). Authors thank  Paule Opolon  and  Olivia Bawa  from  Gustave Roussy  for histology and IHC experiments and  Zeeshan Ahmed  for TEM images.
Results LmjF. 36. 6540 is a potential hydrolase, whose sequence is conserved among Leishmania species The gene LmjF. 36. 6540 encodes a protein of 240 amino acids. Thanks to the software Phyre2 (www. sbg. bio. ic. ac. uk/phyre2), 90% of the sequence has been modeled with 100% confidence by the single highest scoring template: the dienelactone hydrolase (Protein Data Bank (PDB) ID 4zi5). The amino acids form six helices and eight strands of β-sheet (Fig. 1a, b ). The dienelactone hydrolase possesses characteristics of α/β-hydrolases: a catalytic triad consisting of a cysteine (Cys123), a histidine (His202) and an aspartic acid (Asp171)  18. These residues are conserved at positions C126, H201 and D174 in PDB 4zi5. In LmjF. 36. 6540, as shown in Fig. 1a  by a red star, these residues are also conserved: C130, H204 and D175, and are close to each other in the tertiary structure (Fig.