dataesr/scientific-paragraphs-categorization huggingface dataset:
A single author (MD) reviewed the relevant articles and abstracts. A description of the patients' clinical features; treatment and/or outcome was often lacking. The reported cases with missing clinical data about the analyzed parameter were not included in the statistical analysis. Descriptive statistics were used to determine the mean age and to summarize the distribution of CDB among the cohort of report cases in literature. Statistical analysis was performed using StatView software, version 5. 0. 0. 0 (SAS Institute Inc). Categorical variables were compared using the chi-square test or two-tailed Fisher's exact test where applicable. For all statistical comparisons, results were considered significant when the p value was <0. 05.
Atmospheric water vapour plays a key role in the Arctic radiation budget, hydrological cycle and hence climate, but its measurement with high accuracy remains an important challenge. Total column water vapour (TCWV) datasets derived from ground-based GNSS measurements are used to assess the quality of different existing satellite TCWV datasets, namely from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Atmospheric Infrared Sounder (AIRS) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIA-MACHY). The comparisons between GNSS and satellite data are carried out for three reference Arctic observation sites (Sodankylä, Ny-Ålesund and Thule) where long homogeneous GNSS time series of more than a decade (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) are available.
We select hourly GNSS data that are coincident with overpasses of the different satellites over the three sites and then average them into monthly means that are compared with monthly mean satellite products for different seasons. The agreement between GNSS and satellite time series is generally within 5 % at all sites for most conditions. The weakest correlations are found during summer. Among all the satellite data, AIRS shows the best agreement with GNSS time series, though AIRS TCWV is often slightly too high in drier atmospheres (i. e. high-latitude stations during autumn and winter). SCIAMACHY TCWV data are generally drier than GNSS measurements at all the stations during the summer. This study suggests that these biases are associated with cloud cover, especially at Ny-Ålesund and Thule.
As common reference, we use a recently reprocessed version of GPS TCWV data with hourly temporal sampling covering the period from 1996 to 2014. It enables the largest number of coincident overpasses of three independent selected satellites AIRS IR (from 2003 to 2014), MODIS NIR (from 2001 to 2014) and SCIAMACHY VIS (from 2003 to 2011) for intercomparisons. Three Arctic groundbased GNSS observation sites were chosen: Ny-Ålesund (78 • N, 12 • E), Thule (76 • N, 69 • W) and Sodankylä (67 • N, 26 • E). Satellite gridded data were matched with these stations within a maximum spatial distance of 50 km. Generally, satellites measurements are more accurate during clear sky conditions. In this work we use only cloudcleared products in order to assess their uncertainties in optimal conditions in the Arctic region.
In contrast, depending on wavelengths, multiple scattering inside the clouds may increase the observed radiance and lead to overestimation of the water vapour content above the cloud. These effects are usually corrected in the retrieval algorithms using different methods depending on the instrument. However, in the end, both under-and overestimation of the retrieved TCWV can be observed. Section 2 describes the datasets used and discussed the error sources specific to each technique. Section 3 presents results of TCWV comparisons (satellite retrievals compared to GNSS). Section 4 investigates the link between observed biases in the satellite data and cloudiness. Section 5 presents conclusions. 2 Description of the datasets 2. 1 GNSS Originally designed for real-time navigation and positioning, GNSS was rapidly seen as a cheap and accurate technique for measuring TCWV from the ground  (Bevis et al.
, 1985). TCWV is converted from the ZWD as TCWV = ZWD • K(T m ), (2) where K(T m ) is a delay to mass conversion factor and T m is the humidity-weighted mean temperature  (Bevis et al. , 1992). In this study, we used GNSS ZTD data from the Geodetic Observatory Pecny (Czech Republic) named "repro2 solution" and referred to as GO4  (Dousa et al. , 2017). This ZTD dataset was produced with a homogeneous and optimised processing of GPS observations. Outliers in the ZTD time series were detected and removed using the range check and outlier check method described in  Bock et al. (2014). ZHD and T m were computed from the ERA-Interim reanalysis pressure level data (37 vertical levels between 1000 and 1 hPa, 0. 75 • × 0. 75 • horizontal resolution, 6-hourly time resolution)  (Dee et al. , 2011). The data were first interpolated vertically to the height of the GNSS station and then interpolated horizontally (bilinear interpolation using the four grid points surrounding the station) to the location of the station.
The 6-hourly P sfc and T m data were then interpolated (with cubic splines) to the times of the GNSS ZTD data resulting in the final 1-hourly GNSS TCWV dataset. In order to overcome the satellite/GNSS timing error due to limited hours of MODIS, AIRS and SCIAMACHY measurements during a month over a fixed point at the surface, the satellites passing hours over the three Arctic GNSS stations were defined through the IXION software (http: //climserv. ipsl. polytechnique. fr/ixion/index. php, last access: 15 May 2018). For each satellite, only GNSS TCWV corresponding to the overpasses less than 1 h (Table  1 ) was used to calculate the corresponding monthly time series. Seasonal variations of the TCWV over all three sites for a common period of 11 years (2004-2014) exhibit a pronounced seasonal cycle (Fig.
The TCWV data used in this study are from version 6 of the MODIS instrument on board Terra platform, referenced as "Water vapour near infrared -clear column (bright land and ocean sunglint only): mean of daily mean"  (Gao and Kaufman, 2003; Hubanks et al. , 2008). The Aqua platform was not used because of many gaps in the measurement. We retrieved global monthly mean files, gridded at 1 • by 1 • , from the MOD08_M3. 006 data stream 1  freely available at ftp: //ladsweb. nascom. nasa. gov/allData/6/MOD08_M3 (last access: 15 May 2018). We extracted TCWV from 2001 to 2014 for Sodankylä and Ny-Ålesund and from 2004 to 2014 for Thule for the comparison with GPS. The pixel selection method is the following. MODIS data coordinates refer to the centre of each gridded pixel, so a single pixel is considered per station (to avoid interpolation and select the nearest pixel to GNSS/IGS stations) and defined as follows: (lat, long) (pixel) = (lat, long) (station) + (0.
, 2011)  This error source is estimated to be about 15 % for clear sky observations, and up to 100 % in large clouds amounts  (Van Malderen et al. , 2014). The sensitivity to the surface albedo may cause deviations of up to about 15 %, or 6 kg m - 2  in regions of high surface albedo  (Noël, 2007a; Noël et al. , 2004). A scatter of about 5 kg m -2 , caused by atmospheric variability, is usually observed during the intercomparison with other TCWV datasets  (Noël, 2007b). The three stations used in this study were part of the ground-based stations contributing to the SCIAMACHY validation effort  (Piters et al. , 2006)  during which water vapour profiles alone were validated over Thule and Sodankylä, while TCWV was additionally validated over Ny-Ålesund. TCWV data used in this paper are from  Noël et al.
, 2014). This dataset has dense orbital coverage at high latitude. Similarly to MODIS data, the 1 • by 1 • gridded AIRS pixels were screened. The AIRS considered TCWV pixel per station is the same as for MODIS and defined by Eq. (  3  During this study, we additionally use the AIRS effective CF  (Kahn et al. , 2014)  monthly 1 • by 1 • dataset from the same atmospheric product (AIRX3STM. 006) in order to investigate possible effects of cloud interference on the satellite's observed biases. The AIRS effective CF product is the multiplication of spatial cloud fraction and cloud emissivity. AIRS cloud fraction is defined as the ratio of the number of AIRS cloudy measurements (CF > 0. 01) to the total number of AIRS measurements per 1 • by 1 •. 3 Mean seasonal comparisons and discussion 3. 1 GNSS vs.
MODIS MODIS time series of monthly means TCWV are compared to monthly means of coincident overpassing (mentioned in Table  1 ) GNSS data over Sodankylä and Ny-Ålesund for the period 2001-2014 and over Thule for 2004-2014. This difference in the data range is linked to the GNSS data availability, as GNSS dataset has some missing values at Thule during 2001-2003. The results show an excellent overall agreement with a high coefficient of correlation R > 96 % for the monthly time series (Table  2 ). High correlation of the monthly time series is indeed expected since the seasonal cycle is very marked at all three sites (Fig. 2 ). The mean biases are +0. 4, +0. 6 and +1. 7 kg m -2 at Ny-Ålesund, Thule and Sodankylä, respectively (Table  2 ). The overall positive biases indicate that MODIS generally underestimates TCWV compared to GPS.
GNSS vs. AIRS The AIRS TCWV monthly product shows excellent agreement with coincident GNSS measurements at all stations. The overall correlation with GNSS is larger than 98 %, and the mean bias is smaller than 1 kg m -2 in absolute value (Table 2). These biases are in the same range as reported in previous studies over cold regions, e. g. Thomas et al. (2011)  over Antarctica. However, our study uses a more recent and improved version of both AIRS and GNSS datasets. Again, the monthly mean biases show a distinct seasonal variation at all three sites (Fig. 5 ). AIRS is found to be biased wet compared to GNSS during the colder and drier periods and biased dry during the moister months over Ny-Ålesund and Thule (Fig. 5 ). This observed wet-dry seasonal variation of the bias is consistent with the previous validation efforts of  Rama Varma Raja et al.
Conclusions This paper found a general good agreement between satellite TCWV retrievals and coincident measurements from three GPS instruments in the Arctic region. MODIS and SCIA-MACHY show overall mean dry biases compared to GPS with some seasonal and latitudinal variation. We generally see better agreement (higher correlation, smaller bias and RMSE) between GNSS and AIRS TCWV time series than between GNSS and MODIS or SCIAMACHY. The seasonal (3-monthly) biases do not exceed 1 kg m -2 with AIRS, 2. 5 kg m -2 with SCIAMACHY (except at Sodankylä for both satellites during summer) and 3. 5 kg m -2 with MODIS. At Sodankylä, the agreement between GNSS and satellite retrievals is lower for all three satellite measurements. We do not suspect the GNSS data as they passed a selective qual-  ity control and outlier detection procedure.
This sensitivity is strong to both AIRS and MODIS cloud fractions at Thule as both cloud fractions are more correlated at this station, and at all stations during summer. GNSS-AIRS biases are stronger correlated to the AIRS CF than to MODIS CF, whereas GNSS-MODIS biases are stronger correlated to MODIS CF. The use of two cloud fractions clears out a possible influence of the diurnal differences on studying the cloud impact. This effect is decreasing with latitude, as different sensitivity to both cloud fractions is mostly noticed at Sodankylä, which is thought to be linked to the diurnal variability. We suggest that more robust information on clouds is included in the satellite data processing procedures in order to reduce the TCWV biases in the Arctic and then improve space-borne instrumental uncertainties.
Acknowledgements. This work was developed in the framework of the  VEGA  project and supported by the  French program LEFE/INSU. This work is a contribution to the  European COST Action   ES1206 GNSS4SWEC  (GNSS for Severe Weather and Climate monitoring; http://www. cost. eu/COST_Actions/essem/ES1206,last access:  15 May 2018 ) aiming at the development of the global GNSS network for atmospheric research and climate change monitoring. The authors would like to thank  Jan Dousa ,  GOP, Czech Republic , for providing the reprocessed GPS ZTD data, and the staff from the  Climserv data centre at IPSL  for providing the  ERA-Interim data. Edited by:  Roeland Van Malderen Reviewed  by: two anonymous referees.
Data availability. All TCWV data are publicly available with the exception of the SCIAMACHY data set, which is obtainable by contacting Stephan Noel (data director, cited in the SCIAMACHY data section). All other data (which are cited with DOI and references in the article) are also available via Giovanni (NASA) online application. TCWV data and cloud cover at the three stations for the studied period are included in the Supplement. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-11-2949-2018-supplement.
Step 2: Identification of Scientific Evidence To ensure that the statements were evidence based, the current literature was reviewed and critically appraised. A systematic literature review of the studies published between January 1980 and September 2015 was carried out using the MEDLINE database (Appendix 1, http://links. lww. com/IGC/ A468). The literature search was limited to publications in English. Priority was given to high-quality systematic reviews, meta-analyses, and randomized controlled trials, but lower levels of evidence were also evaluated. The search strategy excluded editorials, letters, and in vitro studies. The reference list of each identified article was reviewed for other potentially relevant articles. The bibliography was also to be supplemented by additional references provided by the international development group.
After 24 hours, all acquired data will be sent to the second investigator who will be blinded to the patient's randomisation arm, and who will have no access to the patient's records from the first 24 hours. In addition, the surgical wound will be evaluated at each participating centre by independent surgeons, also blinded to the randomisation arm. We assume that, because different masks will be used for oxygen administration in each of the study groups in the postoperative period, patient unblinding may occur. However, we anticipate that only a few patients will be able to discern their randomisation arm. Nevertheless, we will register and analyse the number of patients who become aware of their randomisation arm. The blinding of all general procedures and ventilatory management unrelated to FIO 2 have been previously described as part of the individualised perioperative group in the iPROVE study protocol.
39 Appropriate antibiotic prophylaxis will be administered,  40  glycaemia will be controlled and pharmacological treatment will be adopted to avoid hyperglycaemia,  41  and pharmacological prevention of postoperative nausea and vomiting (PONV) will be implemented. 42 Finally, when nasogastric tube insertion is required, the tube will be withdrawn prior to extubation when possible. All these data will be collected and analysed. 43 nitoring Intraoperative monitoring will include an ECG, pulse oximetry, capnography, glycaemia, bladder or oesophageal temperature measurement, anaesthetic (bispectral analysis; BIS) and neuromuscular blockade (train of four; TOF) depth analysis, invasive blood pressure measurements, and advanced haemodynamics monitoring with minimally invasive techniques (optional depending on the standard clinical practice and availability of equipment at each hospital).
Secondary outcomes The secondary outcomes are the composites of all the systemic complications experienced by the subjects in the first 7 postoperative days (points 1-17 below) or in the first 30 postsurgical days (points 1-17 below). The primary and secondary data outcomes will be recorded 3 hours after PACU/ICU admission and at 1, 2, 7 and 30 days after surgery, with a 180-and 365-day follow-up for mortality. Plasma samples will be taken preoperatively Open Access and 2 days after surgery. If the patient is not extubated in the operating room, the first four data time points will be taken from the time of extubation. secondary outcomes in the first 7 and 30 postoperative days The the secondary outcomes in the first 7 and 30 postoperative days are as follows: 1. Anastomosis dehiscence: suture-line failure with leakage of the intraluminal contents that may cause peritonitis, fistula from the wound or drain, or appearance of an abdominal fluid collection (diagnosed with imaging) that causes fever, septicaemia or shock  45  2.
Intraoperative parameters (recorded at three different time points: post-induction, 60 min post-induction and pre-extubation) will be: arterial blood gases, SpO 2 , FIO 2 , respiratory variables (VT, PEEP, P aw , P plat , driving pressure (P plat -PEEP), C rs , respiratory system resistance (R aw ), and haemodynamics (PAM, CI), diuresis, glycaemia and temperature. Other relevant data will also be recorded and include: the types of anaesthetic drugs used, type and volume of fluids given, blood loss and transfusion requirements, need for vasoactive drugs, diuresis, nasogastric tube insertion, duration of surgery, mechanical ventilation time, number of recruitment manoeuvres performed, the need for rescue therapy, and the number of patients in each group who become unblinded to their randomisation arm.
data analysis Normally distributed variables will be expressed as their mean and SD and non-normally distributed variables will be expressed as their medians and interquartile ranges; categorical variables will be expressed as the sample size number plus percentage (n, %). In test groups of continuous normally distributed variables, the Student t-test will be used; the Mann-Whitney U test will be used for continuous non-normally distributed data. Categorical variables will be compared with the χ 2 test or Fisher's exact test, or when appropriate, as the relative risk. Statistical analysis will be conducted on an intention-to-treat (ITT) basis. Unadjusted χ 2 or Fisher exact tests will be used for primary-outcome analyses. Secondary outcomes will be assessed as the count (total occurrence within the observation window) and binary variables (any occurrence, yes/no).
data management Personal information will be treated according to the Spanish Personal Data Protection Law (Ley Orgánica 15/1999 de Protección de Datos de Carácter Personal). To optimise the quality of the data, use of an electronic data collection notebook (DCN) will be implemented to automatically cross-check the entries and data-range values. Once all the data are entered into the database, the acceptable error rate for adequate statistical analysis of the database will be less than 0. 025% for the primary outcome and less than or equal to 0. 6% for the secondary outcomes. Monitoring visits will be performed according to the recruitment rate.
Monitoring plan The monitoring plan is based on the modified Haybittle-Peto boundaries for stopping trials after interim analyses in the second half of the inclusion period. 53 54 Analysis of the main endpoint will be presented to the data and safety management board with a blinded code for each allocation group. The interim analysis will be conducted when outcome data for 378 trial participants have been obtained. If the interim analysis shows a significant (p<0. 001) benefit or harm from the intervention, the data and safety management board will advise the steering committee to stop the trial. Should any serious adverse events (SAE) occur during the trial, a specific template has been designed for use in reporting it to the promotor, which will be done within 24 hours of the principal investigator becoming aware of the SAE.
A subclass of these observers considers unknown parameter estimation along with that of the state estimation (i. e. , adaptive observers). Adaptive T-S observers could be classified into two types: those that consider an additive unknown parameter in the form of unknown inputs and those that consider multiplicative unknown parameters. In  Ichalal et al. (2009b) , an adaptive observer in the unknown input estimation form is proposed for a system with unmeasured premise variable. The common observer structures are either a Proportional Integral (PI) or Proportional Multiple Integral (PMI) (for the parameter estimation part), depending upon the order The work was supported by FP7 project Energy in Time (EiT) under the grant no. 608981 of derivatives of the parameter(s) that doesn't vanish (see, for instance,  Lendek et al.
Simulation Results The simulation of this example was carried out on Matlab with the Yalmip modeling interface  (Löfberg (2004) ) and using the SeDuMi solver  (Sturm (1999) ). As could be seen, the conditions in (  21 ) are satisfied for this example only if P is constrained to be diagonal. By using Theorem 3. 5, the results show that this constraint is not required. The state estimation errors from this simulation are shown in the Fig. 1. As could be seen, the estimation results are fairly accurate. The parameter estimation tracking is shown in the Fig. 2 , which shows a good tracking even when the unknown parameter changes (ρ 1 = 1 is used for this simulation). The input used for the simulation is shown in the Fig. 3  and the weighting functions in Fig. 4  illustrate the sufficient excitation of both the submodels.
Acknowledgements Yuan was part-funded by  EPSRC/NERC  Grant  EP/1000917/1  and  Marcon  by  ANR-10-LABX-25-01. We are pleased to thank the referees, whose comments led to a much-improved paper. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
In parallel the duration of treatment has been dramatically reduced, going from 48 to 12 weeks for most patients 2, 3. Several studies demonstrated that the treatment duration could be even shorter in naïve or non-cirrhotic patients when combining two or three DAAs. For instance, in the ION-3 phase 3 study evaluating the combination of sofosbuvir and ledipasvir (SOF + LDV) in 647 HCV genotype 1 treatment naïve patients, 8 weeks of treatment was significantly non-inferior to 12 weeks of treatment, with SVR rates of 94% and 95%, respectively 4. However, it is unlikely that this combination will allow for shorter treatment duration as a relapse rate of 30%, albeit on a small sample size (N = 25), was found in patients treated for only 6 weeks 5. In the quest for shorter treatment duration, the SYNERGY trial (ClinicalTrials.
gov, number NCT01805882) added a third antiviral, either a non-nucleoside polymerase inhibitor (GS-9669) or a protease inhibitor (GS-9451), on top of SOF + LDV. Although the number of patients was limited (N = 40), both combinations showed SVR rates of 95% after only 6 weeks of treatment 6. This result is probably not limited to this combination and a SVR rate of 87% (N = 26/30) was also found with another SOF-containing triple therapy 7. Whether triple therapy can achieve similarly high SVR rates after only 4 weeks of treatment is still unclear but results reported in a small number of patients (N = 25) with these combinations led to much lower SVR rates (40%), suggesting that 6 weeks was probably a minimal duration for many patient populations 8.