dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Qualitative variables were expressed as counts (percentages), and quantitative continuous data were expressed as mean and range or as median and interquartile range  [IQR]. Quantitative nongaussian data were compared using the Kruskal-Wallis test or the Mann-Whitney test, where appropriate. Qualitative data were compared between groups using Fisher's exact test. The association between quantitative data was quantified by a nonparametric Spearman rank-correlation coefficient (95% confidence interval [CI]). Two-sided tests were performed with statistical significance defined by P <. 05. Statistical analyses were conducted using GraphPad Prism ® software (version 8. 3; GraphPad software, La Jolla, CA, USA) and R (version 3. 4. 4; R Foundation for Statistical Computing, Vienna, Austria).
VZV responders were identified based on IFN-g release strictly above 4. 83 pg/ml, corresponding to twice the maximum value observed in the control condition. The ifn-g gene expression level was measured by qPCR in a whole blood sample after 24 h of VZV stimulation. The relative differential expression between VZV-stimulated and non-stimulated conditions is given as the fold change. Immunophenotyping of lymphocyte subsets was performed by flow cytometry on whole blood. Groups were compared using the Mann-Whitney U test or the Fisher's exact test, where appropriate; P-values <. 05 were considered statistically significant and are highlighted in bold. *Data were missing for three allo-HSCT recipients. †Data were missing for three allo-HSCT recipients. Furthermore, no hematological or transplant-related characteristic was associated with VZV responsiveness.
ACKNOWLEDGMENTS This work is published on behalf of the Lyon HEMINF study group:  F. Ader ,  V. Alcazer ,  E. Bachy ,  M. Balsat ,  F. Barraco ,  M. Boccard ,  A. Conrad ,  S. Ducastelle-Lepretre ,  O. Dumitrescu ,  D. Dupont ,  V. Escuret ,  E. Ferrant ,  T. Ferry ,  G. Fossard ,  E. Frobert ,  H. Ghesquières ,  M. Heiblig ,  E. Hodille ,  L. Karlin ,  H. Labussière-Wallet ,  M-V. Larcher ,  F. Laurent ,  B. Lina ,  G. Lina ,  C. Malcus ,  G. Monneret ,  F. Morfin-Sherpa ,  T. Perpoint ,  F. Poitevin-Later ,  M. Rabodonirina ,  C. Roure-Sobas ,  V. Safar ,  P. Sesques ,  S. Trouillet-Assant ,  F. Valour ,  F. Venet ,  F. Wallet ,  M. Wallon. The authors are grateful to the  Direction de la Recherche en Santé(DRS) des Hospices Civils de Lyon , as well as to the  Reǵion Auvergne-Rhone-Alpes  for supporting the VaccHemInf-FIGHT cohort.
Database Data from the Disease Analyzer database (IQVIA) were used for this study. This database has been extensively described in the literature  (Rathmann et al. , 2018). Briefly, the Disease Analyzer database contains demographic, diagnosis and prescription data anonymously obtained from general and specialized practices in Germany. Diagnoses are coded using the German adaptation of the International Classification of Diseases, 10th revision (ICD-10), while prescriptions are coded using the Anatomical Classification of Pharmaceutical Products of the European Pharmaceutical Marketing Research Association (EphMRA). Quality assessment of the data relies on several criteria such as completeness of documentation and linkage between diagnoses and prescriptions. Several variables (e. g. , age of physician, specialty, community size category, and German federal state) are used for the selection of the panel of practices, and around 3% of German general and specialized practices are included in the Disease Analyzer database.
Clinical implications and directions for future research Based on the findings of this study, people with FVL are at a longterm increased risk of being diagnosed with depression compared with their counterparts without FVL. In this context, mental health should be assessed shortly after the initial diagnosis of FVL but also on a regular basis in the decade following this diagnosis. The occurrence of depression may be particularly high in individuals undergoing thromboembolic disorders and in women with obstetrical complications. In terms of future research, more prospective data are needed to corroborate or invalidate the relationship between FVL and depression, while more studies are warranted to better characterize factors playing a mediating role in this association.
Strengths and limitations Two strengths of this study are the large sample size and the use of longitudinal data. However, the study findings should be interpreted in light of several limitations. First, more data on the context of the diagnosis of FVL (e. g. , personal or family history of thromboembolic events) would have allowed more detailed analyses. Second, there was no data on the severity of depression, and it was not possible to conduct sensitivity analyses by depression severity. Third, as this study included general practices only, and as depression may have been diagnosed in psychiatric practices, the 10-year incidence of this psychiatric disorder may have been underestimated. Fourth, the initial FVL diagnosis is usually confirmed by hematologists, and no data from hematologist practices and no hospital data are available in the database used.
A careful attention is given to both the effect of the plasma cleaning step and on the role of the ion bombardment during a nitriding treatment. We will first present a literature review on the wettability of nitrided austenitic stainless steel surfaces as well as preliminary results which motivated this study. The evolution of the geometric parameters of the drops (contact angle and radius) as a function of time for nitriding conditions in which the bias voltage has been modified will be described and commented. Observations carried out by optical and confocal microscopy as well as by SEM and by TEM will allow us to propose explanations on the differences of contact angles measured on the nitrided surfaces. In this paper, evaporation data are presented for the first on   nitrided austenitic stainless steel.
Before measurements, the samples were cleaned using ethanol, acetone and distilled water, and dried. Profiles of the treated surfaces were measured using a white light interferometer (smartWLI extended GBS-Ilmenau GmbH) with a × 50 magnification (lateral resolution 0. 66 μm). Data analysis were performed using the Mountains Map© software. For the preliminary results presented in part 3, a simple procedure based on profile analysis was applied. A more robust procedure was applied to the other measurements. The procedure used meets the ISO 25178 standard for which we have applied a robust Gaussian filter to separate undulation and roughness profiles. The sampling area was 155*215 μm 2 and the measurements were repeated 2 times. The analysis of the microstructure of the nitrided samples was performed by scanning electron microscopy (SEM, EI Quanta-FEG-650 -EDS QUANTAX Bruker) and optical microscopy.
Acknowledgments This work and the post-doctoral stay of S. Tsareva were supported by the  French Government  through the programs  Institut Carnot IC ÉEL  and referenced to as 2017-11-IDA-COSTHED, and " Investissements d'avenir " operated by the  French National Research Agency (ANR)  and referenced to as  ANR-11-LABX-0008-01  ( LabEx DAMAS ).
Filters characterization The linear absorption of the filters was measured with an Agilent UMA Spectrophotometer (Cary 7000), capable to measure in the UV-VIS-NIR region up to 3300 nm. Since the thickness affects the linear absorption, the optical limiting filters were processed in such a way as to obtain slightly comparable thicknesses in the range of 4-5 mm. The results are shown on Fig. 7. In an attempt to have a better visibility, we limited the graphs of Fig. 7  to the most diluted filters (PMMA3 and PLA2), wherein the pure azophloxine dye suspension in ethanol is displayed as a reference. It is noteworthy that both filters present a broad high transmission in the VIS and NIR ranges of the spectrum. Regarding the azo dye solution, our measurements reveal a peak absorption wavelength located at λ max = 538 nm in total accordance with literature data  [22].
For this study, the four observation sites used are first presented. They have been chosen to represent different latitudes and environmental conditions. Secondly, the data and models are described. Thirdly, we introduce the Signal-to-Noise Ratio (SNR) GNSS-R method to obtain SSH. Fourth, we compare SSH derived from GNSS-R to tides gauges measurements through a harmonic analysis as a first validation step of the GNSS-R processing method. Finally we analyze the physical content of non-tidal residuals. This latter is compared to a sea surface response computed using LIB and DAC models.
GNSS The GNSS observations of ILDG, FFT2, TAR0 and LYTT are provided by the Service d'Observation du Niveau des Eaux Littorales  (SONEL Wöppelmann (2004) ) as a part of the Global Sea Level Observing System (GLOSS,  Woodworth, 1991). Each data-set is available in RINEX format   (Carrere, 2003). with a sampling rate of 30 seconds. GPS and GLONASS constellations are considered. For both constellations, SNR from both the L1 (C/A code) and legacy L2 (P(Y) code) are used. The sampling rate of 30-second of the RINEX data set is enough to process properly the data for each sites. Actually  Santamaría-Gómez et al. (2015)  gives a theoretical maximum antenna height of about 13 meters while processing 30 seconds RINEX with both L1 and L2. This threshold value is above all of our antenna heights (the maximum height in our study areas being 8.
Tide gauges Data from the tide gauges are made available by the GLOSS organizations, the Permanent Service for Mean Sea Level and the University of Hawaii Sea Level Center (UHSLC). In the case of French sites, ILDG and FFT2, the tide gauges are part of the Réseau d'Observation du NIveau de la Mer. This network is operated by the Service d'Hydrographie de la Marine. Both tide gauges are Krohne radar type sensors  (Voineson, 2013)  located in a stilling well. The measurement sampling rate is of one minute. For the Spanish site, TAR0, the sea level observations are issued from a miros radar tide gauge  (García and Molinero, 2006)  which is part of REDMAR network  (Pérez et al. , 2014). The raw measurements are retrieved from the Joint Research Center database (JRC,  Remus et al. , 2010)  and they are provided with a sampling of 5 min.
Atmospheric pressure Atmospheric pressure observations near ILDG, FFT2 and TAR0 are provided by Infoclimat (https://www. infoclimat. fr last access on the 13/12/21) with a sampling of 1 hour. For the French sites, these measurements are issued from METeorological Aerodrome Report (METAR) stations operated by Meteo-France. Distances between GNSS and weather stations are 13 km, 7. 5 km and 1 km for ILDG, FFT2 and TAR0 respectively. The atmospheric pressure data at LYTT site are available from the Lyttelton Port Company (https://www. lpc. co. nz) with one-minute sampling and are located in the same harbour as the GNSS antenna and tide gauge.
Non-Tidal Residual (NTR) processing and analysis methods The time series of GNSS-R SSH with tides removed is called the Non-Tidal Residual (NTR  Gregory et al. , 2019). Tides are computed using the T_TIDE Matlab package  (Pawlowicz et al. , 2002). A harmonic analysis is conducted on GNSS-R SSH based on a list of 45 astronomical and shallow-water constituents. An automated algorithm selects the appropriate constituents of significant amplitudes. The amplitudes and phase lags of the selected constituents are saved in a table to allow the prediction of a local tidal model. Then, GNSS-R NTR is computed as the difference between GNSS-R SSH and the tidal prediction from the local harmonic coefficients. On the other hand, the response of the sea surface to short-term atmospheric forcing is determined by the LIB approximation and DAC products.
Validation of the method with tide gauges The GNSS-R SSH estimated is compared to tide gauges measurements over one year for the 4 sites, ILDG, TAR0, FFT2 and LYTT through the harmonic analysis using T_Tide software,  Pawlowicz et al. (2002). Table  2  focuses on major diurnal, semi-diurnal and quarter diurnal constituents. This comparison is based on the complex difference Δz between tide gauges records and GNSS-R estimates, for each tidal constituents defined as (e. g. Tranchant et al. , 2021) : Δz = A TG e iΨTG -A SNR e iΨSNR (4) with A the amplitude and Ψ the phase of the corresponding tidal constituents. A Root-Sum-Square (RSS) based on these differences gives the combined error derived from individual tidal constituent: RSS = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 1 2 ∑ constituents |Δz| 2 √ (5) For each site, the harmonic analysis gives good results with a RSS less than 3% of the site tidal range, featuring acceptable amplitude discrepancies and good agreement for phase lags.
GNSS-R Non-Tidal Residual at short time scales The GNSS-R NTR are first directly compared to times series derived from LIB and DAC. The correlation coefficients are computed before and after application of a band-pass filter of 2 days to 2 months,  (Lyard and Roblou, 2003). Fig. 4  shows that bandpass filtered data are generally better correlated. For ILDG, TAR0 and LYTT the correlation between GNSS-R and LIB (respectively DAC) increases of 0. 47, 0. 28 and 0. 47 (respectively 0. 44, 0. 32 and 0. 49) after filtering. These results agree with the fact that the sea level response to atmospheric forcing is a dynamic phenomenon that differs from the IB approximation at subtidal time scales till 2 months  (Carrere, 2003; Lyard and Roblou, 2003). Analyzing the filtered series, the correlation values between GNSS-R NTR and meteorological forcing models are above 0.
Finally, an isotropic electron distribution may reveal the dominant role of a pitch angle scattering mechanism, for instance linked to interaction with waves. Equatorial magnetic curvature scattering can also scatter electrons when the electron gyro-radius becomes comparable with the equatorial magnetic field curvature. However, this process may be inefficient in the Jovian magnetosphere for keV and MeV energetic electrons  (Birmingham, 1982 (Birmingham, , 1984)). From 1995 to 2003, the Energetic Particle Detector (EPD) experiment onboard NASA's Galileo mission observed the energy spectrum and PAD of energetic electrons. This dataset remains unique and unprecedented even compared to contemporary observations by Juno, because: (a) the equatorial orbit of Galileo enabled EPD to sample most of the equatorial PAD at all times, and (b) Galileo-EPD was designed to observe energetic electrons with kinetic energies greater than 1 MeV, unlike the Juno instrumentation.
In this article, we analyze for the first time the PAD of MeV electrons (specifically >1 MeV and >10 MeV) observed by Galileo-EPD in the magnetosphere of Jupiter. The PAD of MeV electrons will be contrasted with the anisotropy of lower energy electrons (20-700 keV). Section 2 starts with a description of the Galileo-EPD design and operational modes which is necessary to introduce the state of the art of previous investigations of Galileo-EPD PADs. Section 2 also discusses electron PADs observed by other missions, including the Juno orbiter. Section 3 presents the considered dataset and the method adopted to analyze the Galileo-EPD electron PADs. Section 4 reports the PADs observed by EPD. Finally, Section 5 summarizes our findings and discusses their implications on our understanding of the physical processes that regulate MeV electrons in the Jovian magnetosphere.
Galileo-EPD had two Data Acquisition Modes 1. The slow survey "real-time" mode, which accumulated electron measurements at a low cadence and in a limited number of angular sectors. The typical time resolution was of about 10-12 min Table  1  shows that real-time data for >1 MeV electrons (B1, DC2, DC3 channels) were omnidirectional and therefore do not inform on MeV electron PAD. 2. The high-cadence "record" mode, which accumulated electron fluxes in 64, 32, or 16 sectors (Table  1 ) with time resolutions lower than 1. 4 s. Here, the number of angular sectors actually gives the number of data points collected per Galileo spin, that is, the 16-sector product gives 16 direction-resolved measurements per 20-s Galileo spin. The major drawback of the record-mode dataset is its extremely sparse coverage in time and space (see Section 3.
On this question, the observation of >1 MeV and >10 MeV PADs by Galileo-EPD can contribute to determine (a) if the intensity of the field-aligned beams of MeV electrons is sometimes greater than the flux of equatorial electrons, so that auroral electrons may be in sufficient number to contribute to the total content of energetic electrons, and (b) whether or not 'scattered' beams of MeV electrons exist, possibly showing that a mechanism scatters MeV field-aligned beams to populate the middle and outer magnetosphere with trapped MeV electrons. NÉNON ET AL. 10. 1029/2022JA030627 6 of 21 In the next section, we present the time and space coverage of the considered record-mode dataset and the method employed to analyze the keV to >10 MeV electrons PADs collected by Galileo-EPD during these time periods.
Methods The electron record-mode data that we use here were originally delivered to NASA's PDS PPI node. However, it is only recently that Kollmann et al. (  2019 ) computed reliable EPD electron and ion fluxes corrected for instrument deadtime, background contamination, anomalous instrument performance, and instrument degradation. The dataset created by  Kollmann et al. (2019)  has been submitted to NASA PDS and can be accessed at http://sd-www. jhuapl. edu/Galileo_EPD/. The  Miller et al. (2021)  dataset absorbs the particle fluxes corrected by  Kollmann et al. (2019)  and includes additional quantities, two of which have been key to the present analysis. First, the Miller dataset gives the running spin number of Galileo, which enabled to study electron PADs on a spin-by-spin basis as done in  Mauk and Saur (2007)  (see Section 3.
In this article, we do not analyze Galileo-EPD electron measurements collected inward of 9 Rj because they are unreliable  (Kollmann et al. , 2019). The present analysis focuses on record-mode periods collected at distances to the center of Jupiter lower than 30 Rj, because the counts accumulated during one 20-s Galileo spin drop to negligible level outward of this distance. Future work may try to accumulate direction-resolved data over several Galileo spins to advance on the question of the origin of MeV electrons in the outermost part of the Jovian magnetosphere  (Kollmann et al. , 2018). We therefore analyze the data gathered during 21 record-mode data periods at radial distances between 9 and 26 Rj following the approach detailed in the next section.
, 2014; Mauk & Saur, 2007). However, we find that the limited number of data points per Galileo spin and the most-of-the-time narrow pitch angle range covered by EPD during one spin prevent from fitting the PAD shape with such a function. We instead performed the visual inspection of two kinds of plots: (a) individual PADs accumulated during each independent Galileo spin, and (b) one PAD time spectrogram for each record-mode period, that is, 21 spectrograms (see details two paragraphs below).
Creating Individual Spin PAD Plots (One Per Galileo Spin) With 11 Subpanels (11 Electron Channels) Including Poisson Counting Error The local pitch angle observed by the center of each detector field of view is computed thanks to the Galileo magnetometer observations integrated in the  Miller et al. (2021)  dataset that we use here. The M-shell parameter provided in the Miller dataset has been computed by  Kollmann et al. (2019)  using the internal magnetic field model VIP4  (Connerney et al. , 1998)  combined with the current sheet model of  Khurana and Schwarzl (2005). We present in this article local pitch angles rather than equatorial pitch angles because the magnetic field model can be inaccurate in the middle and outer magnetosphere, and because we find that using equatorial pitch angle rather than local does not bring more information for the studied electron PADs (in particular, most local isotropic distributions are found to correspond to equatorial isotropic distributions and do not reflect the observation of a narrow range of equatorial pitch angles).
The HET end of EPD-LEMMS suffered from obscuration when the stepping motor was in positions number 3 to 7, so we follow the recommendation of  Kollmann et al. (2019)  to consider only HET data accumulated with motor positions equals to 0, 1, or 2. For each individual Galileo spin with valid B1 (>1 MeV), DC2 (>1 MeV), or DC3 (>10 MeV) direction-resolved data, we plot the PAD observed by the 11 EPD electron channels (E0-E3, F0-F3, B1, DC2, and DC3 channels). These figures enable us to study whether the PAD morphologies observed at low and high energies are similar and may therefore be caused by a common physical mechanism. However, the comparison of all electron energies at a given time does not account for possible energy-time dispersed phenomena such as injections (see Section 5). The example of an electron PAD accumulated at M = 20.
Creating 21 PAD Spectrograms In addition, for each of the 21 record-mode period, a spectrogram of electron PADs is created. Figure  3  shows the PAD spectrogram of energetic electrons observed by EPD during the 1996-310 orbital segment at R = 19 Rj (no moon). For each Galileo spin, the PAD is normalized to the flux maximum. To analyze the PAD spectrograms, the reader is invited to look for the pitch angle position of green, blue, and black colors that indicate a reduced flux compared to the spin maximum flux. On the example spectrogram of Figure  3 , one can clearly note that the DC2 and DC3 channels provided much less direction-resolved data than the other channels because of the HET end obscuration issue. In addition, one can note the persistence of field-aligned distributions (with a minimum near 90°) for the E0, E2, F1, B1, and DC2 channels.
This feature is further discussed in Section 4. 3. While the time spectrograms enable us to easily visualize the temporal variability of electron PADs, we find that the individual spin plots most of the time give more information and details than the spectrograms alone. In particular, in the regions of low counts, the individual spin plots enable to appreciate if the anisotropy is larger than the count ing errors. The analysis presented in the present paper relied mostly on the inspection of the individual spin plots. Finally, for record-mode data collected during a close encounter with a moon, we use published simulations of the complex local electromagnetic environment of the moons to guide our analysis and extract as best as possible characteristics of magnetospheric electron PADs unperturbed by the local presence of the moon.
At R = 15 Rj, M ∼ 15-18 At R = 15 Rj, all EPD record mode data were acquired during close encounters with Ganymede, specifically during orbits G2, G7, G8, G28, and G29. Figure  4  shows the trajectory of Galileo projected in the X-Y plane of the GPhiO coordinate frame, where + X points along the corotation direction (hence Galileo was upstream of Ganymede at X < 0) and +Y points toward Jupiter (position data were retrieved on NASA's PDS PPI). It can be seen that Galileo remained upstream of Ganymede during the G8 and G28 encounters, downstream of the moon during G7 and G29, while the G2 encounter is an upstream-downstream pass over the north pole of Ganymede. The terms "upstream" and "downstream" refer here to the cold plasma flow. However, electrons with a kinetic energy greater than a few MeV can drift in the direction opposite to the Ganymede velocity from the downstream to the upstream hemispheres of Ganymede  (Liuzzo et al.