dataesr/scientific-paragraphs-categorization huggingface dataset:
The whole procedure was validated using a leave-one-out cross-validation. We computed a standardized squared prediction error, where the mean should be equal to 1. 0 and the median to 0. 455  [31]. We then computed estimates of the carbon stock of the soil at the unsampled sites using universal co-kriging to produce a raster map of C 2005 stock and C 2014 stock. We implemented this procedure using the R package gstat  [32]. The temporal changes of SOC stocks (kg ha -1 9 yrs -1 ) were estimated by calculating the difference between the raster maps of the 2005 and 2014 carbon stocks: C stock change = ∆C = Cstock 2014 -Cstock 2005 (3) Evolution of C stocks per year based on initial stocks (C dynamic, ‰) was calculated as: C dynamics = ∆C 9 × 1000 C stock 2005 (4) The C/N ratio was calculated with the raster maps for 2005 and 2004 C stocks and the nitrogen stocks as: C/N ratio = C stock/N stock (5) The variability was classified from the estimated data standard score (Z score)  [33] , calculated as: Z score = (∆C -0)/SD (6) as "0" is no change in carbon, with standard deviation (SD) calculated with variance (Var) and covariance (CoVar)  [29] : SD = Var(C 2005 ) + Var(C 2014 ) -2CoVar(C 2005 , C 2014 ) (7) The Z score critical value corresponds to a p-value significance level, as for Z score > 1.
The location of measurements was provided by a Trimble Ag114 GPS with a subscription to differential corrections (Thalès-Omnistar) with a positioning accuracy of 1 m minimum. All calculations and analyses were carried out with R (Studio Version 1. 0. 136). A two-way ANOVA statistical analysis followed by Tukey's honest significant difference (HSD) post-hoc test (package agricola) allowed us to evaluate differences in physicochemical analyses between agricultural treatments and by areas with SOC gains and loss. In order to identify explanatory factors for ∆C, we carried out a principal component analysis (PCA) (package FactoMineR). The PCA function of the FactoMineR package automatically transformed data to a standardized normal distribution. The number of observations was 24 and the number of biophysical variables used for PCA was 10  [38].
Data Analysis We analyzed the data using IBM SPSS Statistics version 24. We controlled for duplicate participation by checking IP addresses and response patterns on demographics and key variables. We used descriptive statistics to describe the sociodemographic characteristics of the sample, and undertook difference tests to compare subsamples of participants recruited through the AmsterdamPinkPanel and through convenience sampling. To investigate self-selection bias, we further assessed differences in the sociodemographic characteristics of the AmsterdamPinkPanel members who participated in FPIE with panel members who did not participate in the survey, based on intake data of the AmsterdamPinkPanel. We used descriptive statistics to describe the sample with respect to PrEP knowledge, use, interest and usage intentions, and assessed differences on these variables between AmsterdamPinkPanel participants and participants recruited through convenience sampling.
During each 15-min period, 6 samples of outgoing air, 1 sample of fresh air, zero gas and calibration gas were measured. The gas samples to be measured were selected by a computer that also stored and processed the data  (28). Physical activity was continuously measured by use of an ActiSleep+ (ActiGraph LLC) accelerometer worn on the hip and expressed as counts-per-minute (CPM). Subjects had fixed bedtimes between 23:30 and 07:30. In the daytime, they were not allowed to sleep or to perform exercise. Meals were offered at stated times (breakfast: 09:00, lunch: 13:00, dinner: 17:45), and subjects were instructed to finish these within 30 min.
Statistical analysis Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS, IBM Corp. ) and GraphPad Prism (GraphPad Prism version 7. 00 for Windows, GraphPad Software). Data are presented as mean (SD). Normality of the parameters was assessed using the Shapiro-Wilk test and outliers were detected with the use of box plots in SPSS. Sample size calculation showed that at least 16 participants per group were needed to show an interaction effect of dietary intervention and time on 24-h DEE in this parallel study. For the calculations we used an α of 0. 05, a β of 0. 10, and effect size of 0. 4, based on a respiration chamber study by Westerterp et al. (33). In the whole group, protein oxidation, BMI, and AEE were not normally distributed. In the HP group, protein oxidation, fat oxidation, BMI, FFM, body-fat percentage, TEE, and AEE were not normally distributed.
A. Patient data selection and 3D geometry segmentation To produce true-to-scale patient-specific replicas of bifurcations, we used 3DRA images from our retrospectively maintained database of patients presenting to clinic with unruptured IAs. Informed consent was obtained from all participants, and institutional ethical approval was in place for research use of patient radiological data. The study was approved by the local institutional review board (Comité de Protection des Personnes de Tours, France) in accordance with French legislation and European Union guidelines. The @neurIST computational tool chain was used for segmentation and extraction of the surface mesh from 3DRA images, following the literature. 10, 11 The software Blender ® was also employed for fur- ther mesh manipulation. To reduce vascular complexity in patientspecific replicas, wherever appropriate, small tertiary-branches were eliminated from the models due to their negligible effect on local hemodynamics.
In total, six patients with intracranial aneurysm were selected (1 ACOM, 4 MCA, and 1 ACA). Segments of the idealized bifurcating vasculature were also generated in silico, for further testing. Blood flow is a complex phenomenon, highly dependent on boundary conditions and the geometry of the vessel. The simplified geometry of the idealized bifurcations allows for better control of the ultrasound experimental setup and thus can be used as a benchmark test for ultrasound measurement techniques. The geometries of two idealized cases were constructed, one symmetric and one asymmetric, using the open-source software FreeCAD. 12 In both cases, the afferent vessel diameter was set to represent an average size of the vessels located near the Circle of Willis. Branching vessels were defined in accordance with Murray's law.
E. Computational fluid dynamics analyses In order to validate the fluid dynamics inside the replicas, it would be necessary to accurately measure velocity profiles in small  arteries of the brain of patients. Since this type of measurement is not currently feasible due to difficulties in accessing the arteries, this study focused on quantifying discrepancies between PD measurements and a CFD model. The 3D Navier-Stokes equations were solved by using the finite-control-volume software, ANSYS ® CFX. Steady state flow analysis was performed,  17  assuming Newtonian incompressible blood with density ρ = 1050 kg/m 3 and viscosity μ = 3. 5 mPa ⋅ s. Tetrahedral elements were used to discretize the core of the computational domain, with three layers of prismatic elements at the wall to ensure accurate computation of velocity gradients.
The focus is to evaluate the flow near the interface where measurement is more challenging and other velocity measurement techniques present limitations. As a consequence, it is expected that saturation and artifacts appear deeper into the flow where velocities are higher. 24 For each replica, the ultrasonography PD protocol consisted of 15 s acquisitions and one frame in B-mode at the same position. For all idealized planar replicas, PD flow images were acquired in the bifurcation plane across the entire replica. In contrast, PD flow images for patient-specific replicas were acquired every 1 mm across the model, controlled by the mechanical holder with metered translation. The resultant data were transferred to a personal computer for further analysis. A post-processing analysis sequence was performed.
For each idealized case, the sequence comprised the following steps: (i) compute a time-averaged PD image from the 15 s acquisition, (ii) use B-mode image to locate the walls of the vessels and adjust the spatial scale of the images, (iii) velocity profile extraction along the distal vessels from the time-averaged PD image, and (iv) extraction of velocity profiles from the CFD results at the same locations. Figure  3  shows the time-averaged image used to extract velocity profiles for one of the idealized geometry datasets. In the case of patient-specific replicas, the steps were as follows: (i) for each plane of measurement, compute a time-averaged PD image from the 15 s acquisition; (ii) build a 3D map of velocities within the blood vessel using PD measurements and plane locations; (iii) extract the geometry of the vessel from B-mode images or the PD velocity isosurface near the wall and align it with the surface of the vessel used in the CFD analysis; (iv) find a base transformation matrix between the reference system used in CFD and PD measurements; (v) locate the PD measurement planes in the CFD reference system and extract CFD velocity values; and (vi) extract velocity profiles from PD measurements and CFD results at the same locations in these planes.
III. RESULTS In total, six silicone models of different patients were produced in addition to two other models with idealized geometries. For each patient case, geometry accuracy was quantitatively assessed using the error index. CFD analysis was performed, and PD data were acquired following the experimental procedure described earlier for each idealized geometry and two representative patient cases, one MCA and one ACA, since these are common locations for intracranial aneurysms. The analysis of the results is divided in two: First, the accuracy of the models was assessed by comparing the vessels of the patients extracted from 3DRA images and the surfaces of the produced replicas. Second, ultrasound PD measurements were compared to CFD results.
ACKNOWLEDGMENTS We gratefully acknowledge funding from the  Newton Fund Programmes of the Royal Academy of Engineering  (Grant No. NRCP1415/2/20 ) and the funding from  PICT  Start-up  2015-0006 ,  PICT 2016-0116 , financed by  FONCYT-ANPCYT of Argentina. We also acknowledge  Stryker  for the financial support and  Jean-Yves Tartu  during the production of the replicas. The datasets generated during and/or analyzed during this study are available in the University of Sheffield Research Data Catalogue and Repository ORDA (Online Research Data), DOI: 10. 15131/shef. data. 9758636.
Sometimes, a bare stent will be placed through the covered stent to avoid stent migration. Finally, all devices will be removed after confirming with a contrast agent that bile flows well through the stent or stricture. To avoid bile leakage into the peritoneum, a 7-Fr naso-biliary catheter will sometimes be placed through the metallic stent for 48 h. In cases of failed EUS-AG, EUS-HGS will be performed for being recorded as technical failed cases in the EUS-AG group. To ensure data integrity, this group of patients will still be followed up as scheduled and recorded properly. Both EUS-HGS and EUS-AG are established procedures of biliary drainage; there are no anticipated problems that would be detrimental to our patients. We do not anticipate the need for formal stopping rules for the trial; nevertheless, severe adverse events will be recorded in a timely manner and will be supervised by the monitoring board.
Data collection The baseline assessment will be performed after randomisation. Basic information including sex, age, race, and diagnosis will be recorded retrospectively. Concerning feasibility and safety, complete blood counts, hepatic and renal function tests, and biochemical tests will be performed on the day before the operation. Ultrasonography or upper abdominal computed tomography (CT) will be performed before the operation to confirm IH biliary dilation caused by obstruction. Patients will undergo an examination of their bilirubin levels on days 1 and 3 postoperatively to evaluate the efficiency of the operations. Upper abdominal CT will be performed postoperatively to clarify the position of the stent. Technical success is defined as successful stent placement, and clinical remission is defined as a decrease in the serum bilirubin level to less than 75% of preprocedural values within 30 days after stent placement.
Adverse events at any time will be recorded and classified as post-procedure (up to 14 days) and late (any time after 14 days) and graded according to the American Society for Gastrointestinal Endoscopy lexicon's severity grading system  [16]. Stent patency is described by time to stent dysfunction needing re-intervention. The criteria for stent dysfunction are as follows: (1) cholangitis, (2) 50% increase in bilirubin from the lowest level post-index procedure, (3) 20% increase in bilirubin from the lowest level postindex procedure and evidence of obstruction on imaging, and (4) endoscopic or radiological re-intervention confirming stent blockage or migration. Additionally, hospitalisation data including the length of hospital stay and total hospitalisation expenses will be recorded. The schedule of enrolment, interventions, and assessments is shown in Fig.
Follow-up The final study follow-up is scheduled at 1 year postoperatively and includes evaluation of perioperative mortality and operative complications. Follow-up assessments of the bilirubin level (total bilirubin, conjugated bilirubin, and unconjugated bilirubin) and survival status are scheduled at 1 month, 3 months, 6 months, and 1 year after the operation or until death. If follow-up information is unable to be obtained, the patient will be considered lost to follow-up, and their data will be documented accordingly.
For the stack recorded at low zoom (Supplementary Fig. 6 ), the segmentation of the image was performed on the blue component using a constant threshold value of gray level of about 75 that was empirically determined. For the stack recorded at high zoom (Supplementary Fig. 7 ), the processing of the images containing macroscopic objects with shadows masking the small particles, were removed. The mean intensity of the blue image was compared to the median value (background) incremented by two times the average value of the standard deviation of the intensity of the stack. The positions of every particle were determined using the Analyze Particle module of FIJI. Based on these positions, crops were recorded for each particle and the segmentation was refined using a local threshold value that was a function of the mean value of the crop.
The value of I OH at X = 0 corresponds exactly to the experimentally measured current transferred by the products of water splitting. However, the value of I OH at X = 1 can be different as a number of the H + and OH - ions is generated within the depleted diffusion layer. The numerical solution of the formulated above boundary-value problem was found using the Comsol Multiphysics software (COMSOL Group, Stockholm, Sweden), version 5. 5. To find an analytical solution, some transformations of the above equations are initially performed to obtain the relations convenient for this purpose.
During each measurement, calibration was performed by one injection of 1 mL of air into the chamber and the ambient temperature was noted. The spirogram was recorded and stored on a computer with acquisition data card (PCI-DAS1000, Dipsi, Chatillon, France) using respiratory acquisition software (Elphy Software, CNRS-UNIC, Gif-sur-Yvette, France) for off-line analysis. This technique was validated according criteria previously reported  [17, 18]. The following parameters were measured: ambient temperature the chamber, temperature of the animal, tidal volume (V T, expressed in µL), inspiratory time (T I expressed in s), expiratory time (T E expressed in s) total respiratory time (T TOT = T I + T E, expressed in s), the respiratory frequency (f expressed in bat/min), and the minute ventilation (V E = V T × f expressed in µL).
Telemetry Applied to Plethysmography Central temperature of the rats was measured using infrared telemetry. Rats were anaesthetized with ketamine/xylazine at doses of 70/10 mg/kg respectively, administered intraperitoneally. The infrared telemetry probe (TA10TA-F20, Data Sciences International, Harvard, MA, USA) was inserted aseptically into the peritoneal cavity. The abdomen was closed with sutures and wound clips. The animals were allowed to recover for at least 7 days prior to experimentation  [2, 19, 20]   Animals were randomly divided into seven groups (5 animals in each group). The control group received 0. 5 mL sodium isotonic solution as solvent of diethyl-paraoxon followed by 0. 5 mL sodium isotonic solution as solvent of oximes. The oxime groups received the solvent of diethyl-paraoxon (0.
The dicentric chromosome (DC) assay Researchers have been applying the DC assay for dose assessment in case of external exposure for more than 50 years, and a huge amount of data is available. The dicentric chromosome, the main aberration used for biological dosimetry, is an abnormal chromosome produced by asymmetric exchange between the centromeric pieces of two broken chromosomes. The method is well described (IAEA 2011) and the procedures for dose assessment are standardized for both small radiation accidents (ISO 2014a) and large-scale radiation events  (ISO 2008). In general, blood samples are taken shortly after exposure, the lymphocytes are cultivated for two days and 500-1000 metaphase spreads scored. The spontaneous background frequency in healthy unexposed populations is very low (approximately 1 dicentric in 1000 cells) allowing detection of whole-body acute doses around 0.
Workers in the low-dose group were exposed only to external radiation and had a wholebody median absorbed dose of 22 mSv, assessed through the Rocky Flats health physics records as the dose equivalent at a depth of 1 cm in the tissue of the body. Workers in the highdose group were exposed to both internal and external radiation. Internal doses were calculated for individuals known to have internal depositions of plutonium or americium based on historical and modern bioassays (urine) and in vivo (lung) measurements as part of the medical surveillance program. Assessments of internal doses were performed using the software CINDYq (code for internal dosimetry), based on the biokinetic models and dosimetry methods recommended by the ICRP, choosing the dose equivalent to the red bone marrow as the metric for this study.
The mean fraction of emitted α-energy escaping the aggregates (F) decreases with increasing injected amount, and so do the dose rates, as shown in Table  2. Very few papers contained chromosome analysis, usually in relation to applied amount of Thorotrast. Fischer et al. (1966)  reported data on whole-body counting (activity of 212 Bi in the region of the upper abdomen, including liver and spleen) and chromosome analysis for 20 patients injected with Thorotrast during the years 1939 to 1947 and measured 19 to 27 years later. A significant correlation was found between the number of chromosome breaks and a value calculated by multiplying the activity of 212 Bi measured in the upper abdomen region of the patients by the years since injection. This quantity was considered by the authors to be an indicator of the cumulated dose.
Acknowledgements Open Access funding provided by Projekt DEAL. The leading authors ( A. Giussani ,  M. A. Lopez ,  A. Testa ) are extremely grateful to all members of  EURADOS WG7  and  WG10  who participated to this review work in any of its phases. The additional contribution of  D. Gregoratto ,  J. Marsh ,  J. Moquet , ( PHE, UK) ;  J. F. Barquinero  ( UAB, Spain );  C. Lindholm  ( STUK, Finland );  N. Maznyk  ( Institute for Medical Radiology, Ukraine );  O. Monteiro Gil  ( Universidade de Lisboa, Portugal );  J. Pajic  ( Serbian Institute of Occupational Health, Serbia );  K. Rothkamm  ( UKE, Germany );  I. Veronese  ( Università degli Studi di Milano, Italy ) is here specifically acknowledged.
Ground-based photometry Additional photometric data for TOI-1235 were taken on 31 December 2019 with one of the 1 m telescopes of the Las Cumbres Observatory Global Telescope (LCOGT;  Brown et al. 2013)  Network at the McDonald Observatory in Texas, USA. We used the TESS Transit Finder, which is a customized version of the Tapir software package  (Jensen 2013) , to schedule a full transit observation. We used the zs (short z ) band and an aperture radius of 7. 0 arcsec for the photometry extraction. A total of 358 photometric measurements were obtained with a cadence of 56 s and a median precision of 1. 1 mmag per point. The images were calibrated using the standard LCOGT Banzai pipeline  (McCully et al. 2018) , and the photometric data were extracted using the AstroImageJ software package  (Collins et al.
5 arcmin of the target ruled out the possibility that the TESS transit signal was produced by any of these stars being short-period eclipsing binary contaminants. Finally, we searched for public time-series data of wide-area photometric surveys and databases exactly as in Díez  Alonso et al. (2019). In particular, we retrieved light curves from the All-Sky Automated Survey for SuperNovae (ASAS-SN;  Shappee et al. 2014; Kochanek et al. 2017)  in the g and V bands, and the Northern Sky Variability Survey (NSVS;  Woźniak et al. 2004)  Notes. ( a) Number of collected data points. ( b) After discarding 20 g and 10 V dubious data points (with poor quality flags). ( c) Data set eventually not used. and the Catalina Sky Survey  (Drake et al. 2009 ) in white light. Table  2  summarizes the three public data sets, including the standard deviation of the magnitudes and mean of the magnitude errors.
The Catalina data set is much noisier, sparser, and shorter than the others, therefore we did not use it in our analysis. In addition, we did not find data on TOI-1235 in the public archives of other photometric surveys, such as MEarth  (Irwin et al. 2011) , SuperWASP  (Pollacco et al. 2006 , including unpublished data), ASAS  (Pojmański 1997), and HATNet (Bakos et al. 2004 ). Finally, TOI-1235 was not labeled as a variable star in the ATLAS survey  (Heinze et al. 2018).
Using this kinematic information with the BANYAN Σ tool  (Gagné et al. 2018) , we classified TOI-1235 as a field star in the Galactic thin disk not associated with any young stellar kinematic group. Finally, we determined key indicators of stellar activity. First, we measured the Mount Wilson S index, S MWO , with the Yabi data environment on the HARPS-N spectra  (Hunter et al. 2012; Borsa et al. 2015) , from which we derived log R HK using the formulae of Astudillo-Defru et al. (  2017 ) and V -K s = 3. 602±0. 0. 059 mag. Next, we measured pEW(Hα) on the CARMENES stacked spectrum following  Schöfer et al. (2019) , which was in agreement within 2σ to the pEW(Hα) = +0. 74±0. 11 Å measured by  Gaidos et al. (2014 ) in April 2009. This means that the activity level of the star (as determined by Hα) has not substantially changed for over a decade.
These three indicators make TOI-1235 one of the least active stars for its spectral type  (Wright et al. 2004; Astudillo-Defru et al. 2017; Boro Saikia et al. 2018 ). See Sect. 4. 3 for a search for periodic signals in other spectroscopic activity indicators. We also searched for soft X-ray and ultraviolet data of TOI-1235, but the star was not covered by any pointing (XMM-Newton, Chandra, or EUVE), or was too faint and far from axis to be detected (ROSAT and GALEX). As an inactive member of the thin disk without further clear evidence to support a very young or very old age, TOI-1235 is likely between 0. 6 Ga (older than the Hyades) and 10 Ga (younger than low-metallicity thickdisk stars). Table  3  summarizes the stellar properties of TOI-1235. We provide the average values, their uncertainties, and corresponding reference.