dataesr/scientific-paragraphs-categorization huggingface dataset:
A covariate was kept in the model if it improved the fit, reduced interpatient variability, and decreased the Bayesian information criterion (BIC). The addition of covariates was stopped when no more decrease of BIC was obtained. The statistical significance of covariate was individually evaluated during the stepwise deletion using the likelihood ratio test (LRT). The covariate was kept in the model if the LRT was significant (P < 0. 05) when it was removed from the full model. In the final model, the 95% confidence interval of each parameter was estimated using bootstrapping (n = 1000)  [29, 30]  performed by R package Rsmlx  [31].
Monte Carlo Simulation Assessment for Different Dose Regimens Monte Carlo simulations were performed by Simulx (mlxR: R package version 4. 0. 6; Inria, Paris, France) based on the final PK model to generate 1000 PK profiles of mitotane for each candidate regimens. Simulated trough concentrations were obtained for each condition. One standard dose regimen (3 g/day) and three high-dose regimens were investigated (6, 9, 12 g/day). Moreover, we used a loading dose of 15 g/day for one month followed by a maintenance dose of 5 g/day. As immediately using high dose regimen (>6 g /day) increases the probability of adverse drug reactions, we also simulated a progressive loading dose regimen.
Study population The 43 PD patients evaluated in the current research were initially recruited to study the association between RBD and enteric PASH  [3]  and to perform a comprehensive evaluation of autonomic dysfunction in PD  [6]. This study was carried out in accordance with the Declaration of Helsinki, conducted with the approval of the local Ethical Committee (Comité de protection des personnes Ouest VI, France) and registered on ClinicalTrials. gov (identifier NCT01748409). Each participant gave written informed consent.
Immunohistochemistry Each whole-mount preparation of submucosa obtained from a single biopsy was permeabilized for 3 hours in PBS/NaN3 containing 1% (v/v) Triton X-100 and 10% (v/v) horse serum and then incubated with antibodies against phosphorylated alpha-synuclein (1:5000, WAKO, Osaka, Japan) and PGP9. 5 (1:10,000; Ultraclone Limited, UK). Suitable secondary antibodies conjugated to Alexa Fluor 488 and 594 were used (Invitrogen, Cergy-Pontoise, France). Specimens were viewed under a Zeiss Axiovert 200M microscope fluorescence microscope. Each fragment of submucosa was entirely scanned using the MosaiX module of Axovision software (Zeiss, Göttingen, Germany). The generated image was used as a map to analyze the whole biopsy for the presence of PASH (supplementary figure  1A ). A biopsy was considered positive when containing at least one structure immunoreactive for both phosphorylated alpha-synuclein and PGP9.
Statistics Continuous variables were expressed as the mean ± standard deviation and categorical data as numbers and percentage. Continuous variables were compared between groups (patients with and without PASH) using the two-sample Student test or the non-parametric Wilcoxon test (when normality assumption could not be assumed); and the Chi-2 test or Fisher test were used for categorical variables (SAS software ® version 9. 4). For all statistical tests p<0. 05 was deemed significant.
Details on these requirements can be found in Section 6. The PF candidates are clustered into jets using the FASTJET software package  [39]. Charged hadrons that are not associated with the PV in the event are excluded from the jet clustering procedure via charged hadron subtraction (CHS)  [36]. All jets are required to have |η| < 2. 4. Jets are clustered using the anti-k T jet clustering algorithm  [38]  with a distance parameter of 0. 4 (AK4 jets). If a lepton is found with ∆R < 0. 4 of an AK4 jet, its four-momentum is subtracted from that of the jet. The single-lepton and fully hadronic analyses also use anti-k T clustered jets with a distance parameter of 0. 8 (AK8 jets). These larger-radius jets are used to tag the hadronic decay of top quarks. A high-mass resonance decay creates daughter particles with significant Lorentz boost.
In the muon channel, p miss T and H T are required to be greater than 50 and 150 GeV, respectively, where H T ≡ p miss T + p T. In the electron channel, it is required that p miss T > 120 GeV. The electron channel has a higher p miss T requirement because of the larger QCD multijet background. As a result of this requirement, an additional selection on H T would not increase performance. In order to suppress the contamination from events originating from W+jets events, a boosted decision tree  [76]  (W+jets BDT) was trained using the TMVA software package  [77]  on the jet-related variables listed below, in order of importance. 1. ∆R min ( , j), i. e. , the separation between the lepton and its closest jet. 2. The CSV score of the subleading and leading AK4 jets. 3. The number of jets. 4. p T,rel ( , j), i.
A QCD multijet enriched region is selected by requiring one of the two jets to be "anti-tagged," meaning it has a PUPPI soft drop mass in the t-tag mass window 105 < m SD < 210 GeV, but the N-subjettiness requirement is inverted to τ 32 > 0. 65. The opposite "probe" jet is used to determine the t mistag rate. This rate is parametrized as a function of probe jet momentum (p) and is measured for each of the three subjet b-tag categories (Fig. 7 ). This "anti-tag and probe" procedure is repeated for the tt simulation, indicating that there is a small (≈2%) contribution from SM tt events. The observed tt contamination is then subtracted from the anti-tag and probe data selection. After the t mistag rate has been measured in the QCD multijet CR, it is used to estimate the m tt QCD multijet distribution in the SR.
For uncertainty sources that apply to multiple channels, the corresponding nuisance parameter is fully correlated across these channels if the symbol appears in the same row. For normalization uncertainties, the size of the effect on the prior distribution is indicated. 4. Lepton reconstruction and triggers: Simulated events are corrected by scale factors to account for differences between data and simulation in the efficiencies in the identification criteria for muons and electrons. By applying the scale factors shifted up or down by their uncertainties, new templates are obtained that correspond to these uncertainties. These templates can be used as the nuisance parameters, which are correlated between channels as identical identification criteria are used. The scale factors are parametrized as functions of lepton p T and η to account for different detector response.
The mild deficits at low m tt in the two plots on the left in Fig. 10  do not significantly impact the limit, because this region is used to evaluate the tt and W+jets cross sections and is not sensitive to the resonance signal. The t tagging efficiency is measured simultaneously in signal and control regions during the maximum likelihood fit, as it is not possible to select a CR that might not be contaminated by the potential signal. The t tagging efficiency scale factor is modeled as a free nuisance parameter, with an unconstrained prior, in the binned likelihood fit. The t tagging efficiency scale factor measured by the fit is 1. 001 ± 0. 012. Data are found to be in agreement with expectations in each of the categories considered in this analysis. Limits on the product of the production cross section and branching fraction are calculated, σ(pp → X) B(X → tt), for heavy resonances decaying to a pair of top quarks.
A shape-based analysis is performed using both the signal and control regions from the three exclusive analysis channels. The THETA software package  [85]  simultaneously fits the m tt distributions from the single-lepton and fully hadronic channels and the S T distributions from the dilepton channel. For the limit calculation, a Bayesian likelihood-based method is used  [86, 87]  with each bin of the distributions combined statistically, along with the implementation of unique nuisance parameters that correspond to the systematic uncertainties described in Section 8. The signal normalization is allowed to vary with a distinct unconstrained nuisance parameter having a uniform prior, while the other nuisance parameters have log-normal prior distributions. Finally, to account for the limited number of simulated events, an additional statistical uncertainty is included for each process relying on simulated events through the "Barlow-Beeston lite" method  [88].
Multiple Landsat scenes were combined to calculate means of sediment dispersal and sea surface temperature under changing seasonal wind conditions in the nearshore zone of Herschel Island Qikiqtaruk in the western Canadian Arctic since 1982. We use observations in the Landsat red and thermal wavebands, as well as a recently published water turbidity algorithm to relate archive wind data to turbidity and sea surface temperature. We map the spatial patterns of turbidity and water temperature at high spatial resolution in order to resolve transport pathways of water and sediment at the water surface. Our results show that these pathways are clearly related to the prevailing wind conditions, being ESE and NW. During easterly wind conditions, both turbidity and water temperature are significantly higher in the nearshore area.
However, the nearshore zone plays a crucial role in Arctic biogeochemical cycling and sometimes for local economy. Most of the sediment derived from erosion of coastal permafrost settles in the nearshore zone or gets directly transferred into the greenhouse gases  [5, 13]. Arctic shelves are the main locus of primary production  [14] , which is highly dependent on solar light penetration within the water column and thus sensitive to variations of water turbidity  [15]. Fresh water input to Arctic shelves is for instance necessary for several species of amphidromous fishes that are central to the subsistence economy of indigenous communities  [15, 16]. Yet, the exact patterns of sediment dispersal in the Arctic nearshore zone are not well-known and shore-specific processes such as longshore drift or barrier island formation are often not captured by existing datasets.
The extensive Landsat archive, which contains spatially consistent data since 1982, can help to detect multi-year surface changes. Its high spatial resolution is also an asset to resolve small-scale surface current processes in the nearshore zone. However, as Landsat sensors were originally designed for land surface applications, the retrieval of suspended sediment information has been subject to many challenges  [21]. In this study, we explore the potential of a new technique where multiple Landsat scenes are stacked to calculate "mean" images for a thirty-year period. These images resolve turbidity and water temperature dispersal patterns at high spatial resolution in the nearshore zone. We applied this technique in a study area around Herschel Island Qikiqtaruk, further on referred to as HIQ, in the western Canadian Arctic under seasonal changing wind conditions.
However, the sedimentary input from coastal erosion to the Beaufort Sea is just approximately 2% of the sedimentary input of the Mackenzie River (1. 8 Tg/a compared to 125 Tg/a)  [29, 33, 38]  Figure  2  Windrose diagram showing wind direction and speed frequency during the sea ice-free period. Data was acquired for 1995-2016 from the weather station at Simpson Point, HIQ. Data provided by  [39] , figure modified after  [38].
Landsat Images Acquisition and Processing In this work, the following Landsat data products are used: • Level 1T data in the red and near infrared (NIR) channels are processed through the ACOLITE  [18]  atmospheric correction scheme to provide the remote sensing reflectance (RRS) and the water turbidity. • Level 1T data in the thermal infrared are used to derive brightness temperature at the top-of-atmosphere. • In addition, we use the L2 surface reflectance (RS) product as a means to quality control the ACOLITE-derived remote sensing reflectance (RRS) values. All Landsat data products were downloaded from the United States Geological Survey (USGS)  [40]. The dataset includes images from Thematic Mapper (TM) from 1982 to 2011, Enhanced Thematic Mapper + (ETM+) from 1999 to 2016, and Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) from 2013 to 2016.
Images from ETM+ acquired after 2003 were mostly excluded, because of the failure of the scan line corrector (SLC) leading to data loss in the resulting imagery of ~23%  [41]. After 2003, only ETM+ image data from flight path 67 were used in this study, because the study area lies within the central part of the image, where the loss of data is minimal. Since the sea is covered by ice from October to May, only images recorded in June, July, August, and September without significant sea ice and cloud coverage were considered. The surface reflectance (dimensionless) data product was chosen for this study because it is a standardized atmospherically corrected product processed and provided by the USGS. Landsat TM and ETM+ sensors provide lower radiometric resolution data products compared to Landsat OLI  [18].
• In addition, we use the L2 surface reflectance (RS) product as a means to quality control the ACOLITE-derived remote sensing reflectance (RRS) values. All Landsat data products were downloaded from the United States Geological Survey (USGS)  [40]. The dataset includes images from Thematic Mapper (TM) from 1982 to 2011, Enhanced Thematic Mapper + (ETM+) from 1999 to 2016, and Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) from 2013 to 2016. Images from ETM+ acquired after 2003 were mostly excluded, because of the failure of the scan line corrector (SLC) leading to data loss in the resulting imagery of ~23%  [41]. After 2003, only ETM+ image data from flight path 67 were used in this study, because the study area lies within the central part of the image, where the loss of data is minimal.
Since the sea is covered by ice from October to May, only images recorded in June, July, August, and September without significant sea ice and cloud coverage were considered. The surface reflectance (dimensionless) data product was chosen for this study because it is a standardized atmospherically corrected product processed and provided by the USGS. Landsat TM and ETM+ sensors provide lower radiometric resolution data products compared to Landsat OLI  [18]. This results in low signal-to-noise ratios over clear water surfaces, which are darker than land surfaces (except for the blue part of the spectra) or turbid water surfaces. Landsat data products have widely been used for the qualitative retrieval of water column constituents in the past decades  [42]. Generally, the water reflectance in the red part of the spectra is well correlated to the light backscattering by suspended sediments in the water column for low to moderate sediment concentrations  [43].
Previous studies have demonstrated that RS atmospheric correction algorithms are successfully applied to highly turbid waters  [44] [45] [46] [47]. However, this has not been tested with Landsat imagery yet. The atmospheric correction of Landsat satellite data to generate the RS data product was performed by the USGS according to  [48]. To assess the performance of the RS (red) data product over water surfaces in contrast to the RRS (red) data product, Landsat scenes processed toward the different reflectance algorithms were compared. These scenes were recorded on September 12th, 2011 by Landsat TM and on August 8th, 2016 by Landsat OLI over the Canadian Beaufort Shelf. Both scenes were downloaded as Level 1T data product without atmospheric correction and as Level 2 data product from the USGS  [40].
RRS data products were computed from L1T products using the ACOLITE software (version 20170619. 0) [18]  and applying the exponential extrapolation algorithm with the two spectral bands in the short-wave infrared (SWIR) part of the spectra as recommended by the authors [20]. The ACOLITE software has been selected because it provides a well-established and documented algorithm, which has been validated and tested in various turbidity settings  [49, 50]. 1500 point values were extracted from both reflectance products for both Landsat scenes within a wide range of reflectance values using value to point in the spatial analyst toolbox in ArcMap 10. 4. 1. The plots for comparison were compiled in Python. The comparison reveals that RRS (red) and RS (red) are well correlated for Landsat TM (Figure  3a , R 2 = 0.
RRS data products were computed from L1T products using the ACOLITE software (version 20170619. 0) [18]  and applying the exponential extrapolation algorithm with the two spectral bands in the short-wave infrared (SWIR) part of the spectra as recommended by the authors [20]. The ACOLITE software has been selected because it provides a well-established and documented algorithm, which has been validated and tested in various turbidity settings  [49, 50]. 1500 point values were extracted from both reflectance products for both Landsat scenes within a wide range of reflectance values using value to point in the spatial analyst toolbox in ArcMap 10. 4. 1. The plots for comparison were compiled in Python. The comparison reveals that RRS (red) and RS (red) are well correlated for Landsat TM (Figure  3a , R 2 =0.
Since TIRS band 10 showed higher accuracy than band 11  [52] , the high gain bands were used for all three sensors to assure better comparability of the results. Thermal Infrared image data from all Landsat satellites are not provided with an atmospheric correction by the USGS, thus they are only available as L1T data product. According to  [53] , the conversion from digital numbers (DN) to spectral radiance (L λ ) and then to brightness temperature (BT, [K]) is given by L λ = L M × DN + L A (1) and BT[K] = K 2 × ln K 1 L λ + 1 -1 (2) with L M and L A [W/(m 2 *sr*µm)] being the radiance multiplier and radiance add, respectively, and K 1 [K], K 2 [W/(m 2 *sr*µm)] being thermal constants, which are all given by the Landsat Metafile (MTL). In order to retrieve a temperature in • C as unit, BT[ • C] = BT[K] -237.
Landsat Turbidity Retrieval Since no specifically tuned optical algorithm (i. e. , water reflectance vs. water turbidity relationship) for our study area exists, we considered a recently published globally applicable turbidity model designed by  [43]  and applied it to our dataset. Turbidity (formazin nephelometric unit, FNU) is a well-known and easily measurable proxy for suspended sediment concentration in sediment transport applications  [54]. It here refers to a measurement of water clarity, indicating how intensively light is scattered by suspended particles within the water column  [55]. The general, semi-analytical turbidity algorithm was calibrated for Landsat TM, ETM+, and OLI imagery using a wide range of field data from coastal and estuarine environments in Europe and South America, based on a switching algorithm of red and NIR RRS values  [43].
The algorithm was applied to the Landsat scenes listed in Table  1  using the ACOLITE software  [18, 20] , where turbidity (T) is calculated through: T(red or NIR) = A λ T ρ W (λ) (1 -ρ W (λ)/C λ T ) [FNU] (4) and T = (1 -w) × T(red) + w × T(NIR) (5) with ρ W (λ) being the water reflectance (ρ W = π × RRS) at wavelength λ, A T [FNU] and C T [dimensionless] being wavelength dependent calibration coefficients, w being a weighing coefficient, T (red) being the modelled turbidity using RRS (red), and T (NIR) being the turbidity modelled using RRS (NIR)  [43]. The RRS (red) formula was used for ρ w < 0. 05 and the RRS (NIR) formula was used for ρ w > 0. 07, with a linear blending (w) in the transition zone 0. 05 ≤ ρ w ≤ 0. 07 according to Equation  (5). The exact retrievals of A T and C T is described in  [43].
Transects in the Nearshore Zone In order to investigate the variations in Landsat derived water turbidity close to the coast, a transect of alongshore sampling points was generated starting at Simpson Point and moving clockwise around HIQ (Figure  4 ). This line consists of points with a spacing of 250 m, resulting in 235 points in total. The distance of each point to the coastline is 75 m, meaning there are two pixels between the USGS-cf mask coastline and the sampling point to avoid mixing pixels of land and water. The value to point function in the spatial analyst toolbox in ArcMap 10. 4. 1 was used to extract RS (red) and turbidity values at each point of the transect. The extracted points were plotted against each other using Python. In order to facilitate the interpretation of the BT, RS (red) and turbidity images, five transects were designed perpendicular to the coast of HIQ.
They cover different coastal orientations and range from 0 m to 2000 m distance to the coastline, with 0 m referring to the first water pixel. Each transect was made of 68 points with a spacing of 30 m to cover every Landsat pixel. The pixel values at each of these points were extracted using the extract multi values to point function in the spatial analyst toolbox in ArcMap 10. 4. 1. The attribute tables were then exported as. txt and processed using Python.
Wind Data Wind data were collected for the weather stations HIQ and Komakuk Beach from the climate archive of Environment Canada  [39]. Data from Komakuk Beach were used before the HIQ station was set up in 1994 and whenever the weather recording at the HIQ station failed. To receive consistent data over the whole investigation period, only measurements from 00:00, 06:00, 12:00, and 18:00 were extracted from all datasets, even though hourly data from both stations is available since 1994  [36]. Wind speed and direction observations were acquired for the date of each Landsat scene and the two previous days in order to extract representative dispersal patterns, in total 12 measurements In order to facilitate the interpretation of the BT, RS (red) and turbidity images, five transects were designed perpendicular to the coast of HIQ.
Wind Data Wind data were collected for the weather stations HIQ and Komakuk Beach from the climate archive of Environment Canada  [39]. Data from Komakuk Beach were used before the HIQ station was set up in 1994 and whenever the weather recording at the HIQ station failed. To receive consistent data over the whole investigation period, only measurements from 00:00, 06:00, 12:00, and 18:00 were extracted from all datasets, even though hourly data from both stations is available since 1994  [36]. Wind speed and direction observations were acquired for the date of each Landsat scene and the two previous days in order to extract representative dispersal patterns, in total 12 measurements per scene. When six out of the last eight six-hourly measurements (date of the scene plus the previous day) or nine out of twelve had comparable wind directions, the conditions were assumed to be steady and the scene was used for this study.
The wind data was used to reconstruct the general wind direction patterns over several days so that an acquisition of wind data each six hours was sufficient and the only way to provide consistent and comparable data over the whole observation period. Manually controlled random samples in the hourly dataset showed no significant anomalies compared to the six hours interval used per day. In total, 18 scenes showed continuous ESE wind conditions (TM: 13, ETM+: 4, OLI: 1), 8 continuous NW wind conditions (TM: 4, ETM+: 3, OLI: 1), and 9 showed changing wind conditions (TM: 8, ETM+:1), which refers to unsteady wind conditions during the 12 observations.
and comparable data over the whole observation period. Manually controlled random samples in the hourly dataset showed no significant anomalies compared to the six hours interval used per day. In total, 18 scenes showed continuous ESE wind conditions (TM: 13, ETM+: 4, OLI: 1), 8 continuous NW wind conditions (TM: 4, ETM+: 3, OLI: 1), and 9 showed changing wind conditions (TM: 8, ETM+:1), which refers to unsteady wind conditions during the 12 observations.