dataesr/scientific-paragraphs-categorization huggingface dataset:
When the number of KTs performed was missing for 1 or 2 years, the national or regional renal registry was asked to provide the missing data if possible. In case we were unable to use one data source (ERA Registry or GODT), we used both databases. For 19 countries data were obtained from the ERA Registry database, for 18 countries the GODT database was used, and for 3 countries both the ERA Registry and the GODT database were used (Supplementary data, Fig. S1 ).
General population data For countries providing individual patient data to the ERA Registry and countries for which the data was obtained via the GODT database, the midyear general population counts were used from Eurostat  [16]. For countries that had provided aggregated data to the ERA Registry, and for Austria, Bosnia and Herzegovina, and the UK, midyear general population counts from their national bureaus of statistics were used. For the Russian Federation we used the general population data in 2014 from the United Nations Population Fund  [17].
Statistical analyses Time trends in KT rates were analyzed using Joinpoint regression  [18]. The average annual percentage change (APC) with 95% confidence interval (95% CI) was computed using Poisson regression provided by the Joinpoint regression program  [18]. Details of this method have been previously described  [19]. Joinpoint identifies points in time (e. g. years) at which the trend of, in this case KT rates, changes statistically significantly  [20, 21]. For the analyses the year of KT was added to the model as the independent variable and the KT rate (total, DD or LD) as the dependent variable. Corresponding to the availability of nine data points (i. e. years in our study period) a maximum of one joinpoint (two trends) was used  [20]. In addition, analyses were performed using zero joinpoints to obtain a single trend for the entire study period.
ACKNOWLED GEMENT S We would like to thank the patients and the staff of the dialysis and transplant units for contributing the data via their national and regional renal registries to the  ERA Registry  and the  GODT. Furthermore , we gratefully acknowledge the following registries and persons for their contribution of the data: Albanian Renal Registry ( M. Rroji  and  E. Likaj );  Austrian Dialysis and Transplant Registry (OEDTR)  (F. Engler,  R. Kramar ,  G. Mayer , and the  Austrian Society of Nephrology) ;  Belarus Renal Registry  ( K. S. Komissarov  and  K. S. Kamisarau );  Dutch-speaking Belgian Society of Nephrology (NBVN)  ( M. Couttenye  and  F. Schroven );  French-speaking Belgian Society of Nephrology (GNFB)  (J. M. des Grottes and F. Collart); Renal Registry Bosnia and Herzegovina; Cyprus Renal Registry ( A.
The TESS light curve reveals that α Sex is a variable star with multiperiodic pulsations and periods of the order of several hours. The IUE archival observations of α Sex over 3 days in 1992 December are also analyzed. This article is organized as follows. Section 2 describes the TESS light curves of α Sex and their analysis, Section 3 the analysis of the IUE spectra, and Section 4 the abundance determinations for 19 chemical elements. In Section 5, we use the SPInS stellar parameter inference program  (Lebreton & Reese 2020 ) and the BaSTI-IAC grid of stellar models  (Hidalgo et al. 2018)  to derive the evolutionary status, mass, radius, and age of α Sex. We discuss the nature of α Sex and conclude in the final section.
1 We checked for possible sources of contamination, but could not verify any known and sufficiently bright background or nearby sources. Using the LIGHTKURVE software (Lightkurve collaboration et al. 2018) software in combination with Gaia astrometry (Gaia Collaboration 2020), there are a only a few very faint (Gaia G > 14 mag) sources located within the assigned target pixel aperture mask, hence their flux contribution is negligible. Given the large gaps between the sectors 8, 35, and 45 light curves, we opted to analyze them separately for signatures of pulsations to avoid issues arising from the complex spectral window pattern in a combined light curve. Each TESS sector light curve maximally spans approximately 24 days with varying duty cycles depending on the specific sector. This yields a resultant frequency resolution following the Rayleigh criterion of approximately 0.
Specifically, synthetic spectra were computed assuming LTE using Hubeny & Lanz (1992) SYNSPEC49 code which computes lines for elements up to Z = 99. The synthetic spectra were further convolved with a rotation parabolic profile for v i sin e = 21 km s -1  (Abt et al. 2002)  and the appropriate FWHM of the instrumental profile of NARVAL. The projected equatorial velocity has been checked by modeling the Fe II lines in the range 4500-4550 Å, they all yield a v i sin e of about 20. 0 ± 1. 0 km s -1 , which agrees well with the value provided by  Royer et al. (2002). In order to derive the microturbulent velocity of α Sex, we simultaneously derived the iron abundance [Fe/H] for 50 unblended Fe II lines and a set of microturbulent velocities ranging from 0. 0 to 2. 0 km s -1. The adopted microturbulent velocity, ξ t = 1.
Estimations of Mass, Radius, and Evolutionary Stage To estimate the mass, radius, and age of the star, we used the SPInS stellar model optimization tool  (Lebreton & Reese 2020). SPInS uses a Bayesian approach to find the probability distribution function of stellar parameters from a set of constraints. At the heart of the code is a Markov chain Monte Carlo sampler coupled with interpolation within a precomputed stellar model grid. Here, we used the BaSTI-IAC grid of stellar models  (Hidalgo et al. 2018 ). This grid is for a solar-scaled heavy element distribution with the solar mixture taken from  Caffau et al. (2011)  complemented by  Lodders (2010) , which corresponds to (Z/X) e = 0. 0209. The grid considers convective core overshooting included as an instantaneous mixing between Schwarzschild's convective limit up to layers at a distance α ov = 0.
Acknowledgments This work was supported by  FCT/MCTES  through the research grants  UIDB/04434/2020 ,  UIDP/04434/2020  and  PTDC/FIS-AST/30389/2017. M. D. is supported by national funds through FCT in the form of a work contract. D. M. B. gratefully acknowledges a senior postdoctoral fellowship from the  Research Foundation Flanders (FWO)  with grant agreement no. 1286521N. The authors gratefully acknowledge  Dr. Frédéric Arenou  for enlightening discussion on Gaia parallaxes. This research has made use of the SIMBAD database, Figure 4. Kiel diagram showing the observed position of HD 87887 together with two isochrones computed for the ±1σ age values inferred by SPInS operated at  CDS, Strasbourg, France. The TESS data presented in this paper were obtained from the  Mikulski Archive for Space Telescopes (MAST)  at the  Space Telescope Science Institute (STScI) , which is operated by the  Association of Universities for Research in Astronomy, Inc.
Funding: This research was funded by the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  (Grant No  ANR-10-LABX-62-IBEID ) and l' Agence Nationale pour la Recherche  (AfuInf  ANR-16-CE92-0039 ). The 800-MHz NMR spectrometer of the  Institut Pasteur  was partially funded by the  Région Ile de France  (SESAME 2014 NMRCHR grant no  4014526 ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
REWOLUTION HF is a needs-driven, targeted educational initiative with the aim of optimizing implementation of personalized HF treatment and accelerating adoption of this important new approach into clinical practice. To inform the development of the REWOLUTION HF programme and maximize its impact, international surveys were carried out to assess both healthcare professionals' (HCPs) educational needs and patients' perspectives on the impact of HF and its management. patient representatives, and a survey methodology expert. The Global Heart Hub (GHH), an international coalition of patient associations, involved the patient representatives on co-creation of the surveys, discussion of the results and manuscript preparation and/or review. The questionnaires were developed during preparation meetings by three working group members (EAJ, PL, AJSC) with support from a qualitative research design expert (KC) and a medical education company (MedEd Global Solutions, Paris, France), discussed during a plenary working group meeting held in July 2021, revised and circulated by email in August 2021, and finalized in September 2021.
Online versions of each questionnaire were set up using QuestionPro, a software for online surveys (https://www. questionpro. com). Pilot testing was conducted to evaluate readability of the questionnaires and functionality of the online system. HCPs and patients participated in the surveys by clicking on the QuestionPro links. The average time taken to complete the online survey was 13. 7 min for HCPs and 15. 3 min for patients. No personal information was collected during the process. Responses were collected between August and December 2022. Links to the questionnaires were disseminated by REWOLUTION HF working group members (15 HF experts in 11 countries) by email via their professional networks, with support from the Optimize Heart Failure Care programme  19  national coordinators (72 coordinators in 42 countries).
Patients diagnosed with HF were invited to take part in the survey on an individual basis, either by an HCP or by a patient representative. In China, the HCP survey link was also disseminated via MedLive, a platform that provides professional medical information services for doctors. Given the wide dissemination of the questionnaire in China, the number of responses for the Chinese version of the HCP survey was limited to a maximum of 250. Compilation and analysis of the fully anonymous raw data were performed by MedEd Global Solutions.
Acknowledgements Patricia Vlasman  and  Winfried Klausnitzer  contributed to this project as patient representatives through their participation in the creation of the surveys, discussion of results and manuscript review. Organization of working group meetings, collection of survey responses and data analysis were supported by  MedEd Global Solutions. Writing assistance was provided by  Jenny Bryan. Boehringer Ingelheim ,  Novartis , and consulting fees from  General Electric, Government of Canada & Alberta ,  Novo Nordisk ,  AstraZeneca ,  Merck ,  Servier ,  Pfizer ,  Novartis ,  St. Jude  and Bayer. M. K. has received honoraria for consulting/committee membership or lectures from  Servier ,  Bayer ,  Novartis ,  AstraZeneca ,  Torrent ,  Boehringer Ingelheim ,  Sanofi , and  Amgen. L. H.
Popular orbitography software such as the French Space Agency Géodésie par Intégrations Numériques Simultanées (GINS)  [15]  or NASA's Mission Analysis, Operations, and Navigation Toolkit Environment (MONTE)  [16]  currently describe the motion of interplanetary spacecraft via a classical Newtonian framework linearly corrected with effective forces, accounting for the effects of general relativity via the socalled n-body Einstein-Infeld-Hoffmann (EIH) equations of motion  [17]. Given the stringent accuracy requirements associated with fields such as astrometry, geodesy, celestial mechanics, and metrological sciences  [18] , spacecraft and interplanetary probe orbit propagation tools based on the so-called "Newton + correction" framework need to include subtle relativistic effects in both state and measurement models to reflect the rapid improvements in modern measurement technologies.
TDI analysis employs solar system barycentric coordinate time while each LISA spacecraft records data according to the individual spacecraft proper time  [30]. Hence, sophisticated four-dimensional general relativistic spacetime transformations are required for TDI data reduction to detect gravitational waves using space-based interferometry. Bearing this in mind, we believe that the classical "Newton + correction" paradigm is now reaching its limits in terms of complexity. In a recent article  [31] , we presented the first results of a prototype software, namely General Relativistic Accelerometer-based Propagation Environment (GRAPE), which describes the perturbed motion of Parker Solar Probe  [32] , Mercury Planetary Orbiter  [33] , and Molniya-like  [34]  interplanetary probes and spacecraft subject to a radiation-like non-gravitational four-force using the complete framework of general relativity.
GRAPE: A brief overview In this section, we discuss the mathematical framework implemented in GRAPE. First, we provide a high-level overview of the platform and discuss some aspects of the software that are unique to general relativity and not common with classical propagation environments. In Section 2. 1, we present the equations of motion and discuss the treatment of non-gravitational perturbations in a fully relativistic framework. In the framework of general relativity, the unperturbed motion of test particles is described using a geodesic equation parameterized by an arbitrary affine parameter s. For massive test particles such as interplanetary probes and near-Earth spacecraft, it is common to parameterize equations of motion using their associated proper time τ such that s ≡ τ. While proper time parameterization is convenient when working in a theoretical framework, it is inconvenient for practical purposes.
Spacecraft operators, tracking stations, and orbit determination as well as data reduction software reference the state, error covariance, and observables with respect to a derived external coordinate time scale t such as terrestrial, barycentric, or ephemeris times. Hence, GRAPE internally adopts a global coordinate time t = x 0 /c during numerical propagation, and the corresponding equations of motion are transformed from an affine to a coordinate time parameterization accordingly. The incorporation of non-gravitational forces in classical Newtonian propagation environments is a well-recognized problem and remains an active area of research in operational astrodynamics  [37]. As discussed at length in Ref. [31] , the treatment of non-gravitational perturbations in general relativity has not received the same attention in the literature and requires further elucidation.
The nonlinear field equations of general relativity  [38, 39]  can be decomposed according to contributions from the Einstein tensor G µν and the energy momentum tensor T µν. The Einstein tensor is composed of the metric tensor and quantities derived from it, e. g. , the Ricci and Riemann tensors, which encode the geometry of spacetime so that all gravitational phenomena are captured entirely at the order of the spacetime metric tensor. Similarly, quantities derived from the energy-momentum tensor encode information related to non-gravitational phenomena. In the parlance of operational astrodynamics and interplanetary navigation, gravitational and nongravitational perturbations are obtained from the metric and energy-momentum tensors, respectively. Hence, the perturbed equations of motion employed by GRAPE are derived from the divergence of the energy-momentum tensor, which is linearly decomposed according to contributions from the energy-momentum of an isolated test particle and the corresponding stresses acting on the system that produce geodesic and perturbed motion, respectively  [40, 41].
3. 3 in Ref. [37]  for further details. In the present analysis, the non-gravitational perturbations entering Eq. (  23 ) are inferred directly from the Cassini SPICE kernel, and we assume that they are held constant throughout the experiment. Given that this is the first application of GRAPE to real radiometric tracking data, we persevere with this assumption and note that it is an approximation. We further note that the GRAPE estimation module is currently being developed, where specific time-dependent non-gravitational force models will be applied to parameter estimation problems for probes during interplanetary cruise. The explicit details and steps required to reproduce the present analysis are discussed below. Finally, we note that by construction, the routines utilized internally by the SPICE toolkit automatically take into account the Earth ephemeris, the location of Earth-based tracking stations, and the Earth's rotation.
The success of the Cassini gravitational wave experiments and solar system tests of general relativity was mediated by two major technical upgrades. First, Cassini was equipped with a novel multi-link telecommunication configuration with two ④ The dataset ID used here is CO-X-RSS-1-GWE1-V1. 0. It can be downloaded at NASA's Planetary Data System. ⑤ See https://naif. jpl. nasa. gov/pub/naif/toolkit docs/FORTRA N/spicelib/spkezr. html for further details. uplink carriers in the X and Ka bands as well as three downlink carriers in the X, Ka1 (coherent with the Xuplink), and Ka2 (coherent with the Ka-uplink) bands, considerably reducing the electromagnetic wave phase scintillation owing to solar plasma  [6, 52]. Second, the radioscience experiments performed by Cassini benefited from a dedicated advanced media calibration system located at the Goldstone Deep Space Communications Complex (DSS-25).
The details of the Cassini kernel and observational data used in the present analysis are presented in Footnotes ② and ④. For clarity, we plotted only the residuals for the first pass of Cassini during GWE1. However, we note that the difference in the residuals remains at the ∼0. 2 Hz level, with no noticeable secular variation during the first 1 × 10 5 observations. The pre-fit Doppler residuals are slightly biased owing to uncertainties in the initial state of the spacecraft obtained from SPICE. The bias is corrected in the estimation phase, e. g. , by employing a (sequential) batch least-squares or Kalman filter algorithm  [37]. The initial-state bias in Fig. 2  is standard and is comparable to that of other modern orbit prediction and determination software; see Fig. 1  in Ref. [1]  for example.
Typically, in orbit determination problems, the goal is to obtain Gaussian residuals with zero mean. In this regard, the results presented herein are slightly different. In its simplest form, orbit determination software is typically decomposed into two modules: prediction and estimation. The goal of the present study is not estimation via leastsquares or Kalman filter analysis. Instead, this study aims to present the first results of the GRAPE platform during the prediction phase. As noted above, the estimation module of GRAPE is currently under development and will be the topic of a future paper on general relativistic state and parameter estimation. Figure  3  compares the GRAPE and Cassini ephemerides in the time-dependent ( R, Ŝ, Ŵ ) spacecraft frame, as discussed in Step  (7). Using GRAPE, the reconstructed Cassini trajectory during cruise agrees with the SPICE ephemeris at the level of 10 -4 km.
The time axis is given in days since the initial epoch. models are implemented in GRAPE, the ∆R residual will be significantly reduced ⑥. It is important that the results presented in this manuscript are easily reproducible. As a result, and in anticipation of a future public release of the GRAPE software, we outline the specific steps required to reproduce Figs. 1 2 3 (1) Generate an ephemeris using GRAPE by numerically solving Eq. (  23 ) subject to the condition that f α ≡ 0. This step produces an ephemeris for the Cassini trajectory during the first gravitational wave experiment under the assumption that the motion of the probe is governed entirely by the n-body solar system metric tensor components given by Eq. (  22 ). (  2  Accordingly, we adopted h = 300 s. at each observation time using finite differences.
(5) Define the non-gravitational (NG) perturbations acting on the probe according to a NG ≈ a S -a G (24) The GRAPE (G) and SPICE (S) acceleration vectors are respectively defined by a G ≈ a M , a S ≈ a M + a NG (25) where we have introduced the acceleration owing to the n-body metric tensor components only as a M. Under the assumption that a NG ≈ a 0 during the short duration cruise phase (< 40 days), where a 0 denotes a constant vector, we employ Eq. (  10 ) at each integration time-step and produce a new GRAPE-generated kernel to be used with the SPICE software. The ephemerides associated with the new kernel now take into account the non-gravitational forces acting on the probe under the assumption that they remain constant throughout the integration procedure. Again, we reiterate that the adopted approach is an approximation.
Discussions and conclusions We presented the first practical results of a novel general relativistic orbitography platform, GRAPE. While the initial results are in good agreement with NASA's SPICE software, further work is required. It is of utmost importance that GRAPE can accurately provide light-time solutions for the calculation of radiometric observables in a fully relativistic framework. Rather than adopting the standard approach in the literature, where light-time is approximated by a series of linearly superimposed post-Newtonian corrections that gradually increase in complexity and are error-prone for higherorder corrections, we are currently implementing lighttime solutions in GRAPE by numerically solving the null geodesic equation given in Ref. [55]. The latter approach guarantees that all relevant relativistic contributions to the dynamics are appropriately accounted for at the order of the metric tensor and voids the possibility of neglecting potentially important terms  [20] [21] [22].
The motivation for designing a general relativistic orbit prediction and estimation tool is not to replace the current popular orbitography platforms based on the corrected Newtonian approach. Rather, it is to provide a fully consistent and highly accurate benchmark for current and future platforms. With radioscience data preprocessing software now publicly available (see for example Ref. [56] ), archival tracking data from a myriad of space missions can be easily reduced, thereby providing ample opportunity to revisit historical missions and verify future modifications to the tool. We expect the current manuscript to be the first in a series of papers describing new approaches to general relativistic orbit determination (see for example Ref. [47, Chapter 7] ) applicable to missions requiring high precision, e.
Two statistical software packages were used for the analyses: First, IRaMuTeQ  [40]  was used to produce the hierarchical clustering classifications (the Reinert method, explained). Second, Lexico 3  [41]  was used to calculate the lexical specificities 2 of the sub-corpora from n-grams. For the IRaMuTeQ analysis, the texts were annotated by date, and for the Lexico analysis, the texts were annotated by date and political party. The latter annotation facilitated the identification of the linguistic features of the speech segments produced by the parliamentary majority, and those of the opposition parties. All texts were extracted from the National Assembly website and include three texts discussed in the closed French National Assembly Examination Committee hearings (on 05/06/2019, 19/06/2019, and 14/01/2020) during which they discussed the Avia Bill before the debates in the public sessions: As soon as they are submitted, the Bills are printed and distributed to all the MPs.
The colored lines represent the best-fit logistic growth curves for each parasite isolate. Note that the x-axis is on a log10 scale. (b) Oocyst intensity 7 dpbm (number of oocysts in infected mosquitoes) as a function of gametocytemia. Each colored circle represents a P. falciparum oocyst-positive midgut. The colored lines represent the linear relationship for each parasite isolate, while the black line (± se) for all data regardless of isolate origin. Note that the xand y-axes are on a log10 scale. (c) Sporozoite prevalence (number of mosquitoes with heads/thoraxes detected positive to P. falciparum by qPCR at the time of death of the individual out of the total number of tested heads/thoraxes) as a function of gametocytemia. Each colored circle represents a tested head/thorax. The x-axis is on a log10 scale.