dataesr/scientific-paragraphs-categorization huggingface dataset:
(Ewels et al. 2016 been developed using the Vue. js library (v. 2. 6. 12) with a set of components, and plugins (see supplemental data set 1), notably Chart. js for boxplot display. It aims at providing simple functionalities such as gene searching through different accession identifiers (based on synonymous computed across different generations of genome assemblies/annotations and arrays), gene names or functions. Expression patterns are displayed using box-plots to show the level of variability between biological replicates. Users can easily select/unselect SRA projects to be plotted, navigate through different subsets of projects or locus and display raw or log2 normalized expression values, access to gene annotations, synonymous and links to relevant external resources (genome browser, knowledge base, original data repository).
Acknowledgments We thank  Jérôme Gouzy  and  Marie-Françoise Jardinaud  ( LIPME, Castanet-Tolosan ) for valuable discussions on the MtExpress set up and  Nathalie Vialaneix  ( MIAT, Castanet-Tolosan ) for her advice on statistical analyses. We also thank  Erika Sallet ,  Ludovic Cottret ,  Ludovic Legrand  and  Léo Géré  ( LIPME ) for technical advice. Finally, we are grateful to the  Genotoul bioinformatics platform Toulouse Occitanie  ( Bioinfo Genotoul , https://doi. org/10. 15454/1. 5572369328961167E12 )for providing computing resources.
Two databases (db) give access to some M. truncatula RNAseq data, the first one as Results Retrieval, quality checking and organization of RNAseq data To build MtExpress, RNAseq data (i. e. sequenced reads, fastq) generated from Medicago were downloaded from the Sequence Read Archive (SRA) and the European Nucleotide Archive (ENA) (Fig. 1), along with associated metadata. In order to ensure that MtExpress data are reliable and well documented, only sequencing data associated with peer-reviewed publications were conserved, representing 431 different samples (1374 if considering biological replicates). The nf- core/rnaseq bioinformatic pipeline version 3. 0 (Patel et al. 2020) was used to automatically analyze the RNAseq libraries. First, FastQC/MultiQC were used to perform quality checking and reporting of sequencing data.
Sequencing reads were, then, mapped to the Medicago genome version 5. 1. 7 (Pecrix et al. 2018) (52,723 genes) and reference genomes of Medicago symbionts or pathogens used in the selected SRA projects (listed in supplemental data set 1), using STAR mapper (Dobin et al. 2013). Finally, SALMON (Patro et al. 2017) was used to quantify read counts of transcripts. To enable cross-comparisons between different experiments, we normalized the complete dataset by the trimmed mean of M-values method (TMM) (Robinson and Oshlack 2010), using the EdgeR package. All samples were then manually labelled using a coherent nomenclature and organized into different categories based on organs or physiological conditions (18 categories in this first release; e. g. leaf, part of a generic db developed by EBI on many species seed, abiotic factors, biotic stress, root nodule symbiosis…), with keywords attributed to each (https://www.
ebi. ac. uk/gxa/plant/experiments), and the second one as part of a specific study experiment, thereby enabling rapid search/selection. dedicated to nutrient-and nodulation-responsive peptides (http://mtsspdb. noble. org/atlas- internal/3880/transcript/profile/0) (de Bang et al. 2017). However, these db only show a small selection of publicly available RNAseq data. Here we present MtExpress, a gene expression atlas based upon M. truncatula RNAseq data supported by peer-reviewed publications (https://medicago. toulouse. inrae. fr/MtExpress). We ensured that MtExpress was a comprehensive, reliable, user-friendly, interconnected and upgradeable database, by combination of automated analyses and manual curation.
For each SRA project, links to MultiQC(Ewels et al. 2016) report, raw count and metadata files are provided. User Interface Database structure and scheme are available at https://lipm-gitlab. toulouse. inra. fr/EXPRESSION-ATLAS API All normalized data are accessible through a REST API developed using the Scalatra framework and exposed with Jetty servlet container. The API is fully documented using Swagger 2. 0. Documentation can be found at: https://lipm-browsers. toulouse. inra. fr/expression-atlas-api The.
X-shooter spectroscopy Observations of the lensed image 1 of RCS0224z5, amplified by  = 29 +9 -11 (luminosity-weighted; see  Smit et al. 2017) , were taken with the VLT/X-shooter  (Vernet et al. 2011) on 11, 14, and 16  October 2018 with a total on-source time of 3. 5 h, under ESO programme ID 0102. A-0704(A) (PI: Smit); the slit was centred at  = 02:24:33. 83,  = -00:02:17. 91 (Figure  1 ) and using slit widths of 1. 2 and 0. 9 in the visible (VIS) and near infrared (NIR), resulting in a spectral resolution  ≡ /Δ ≈ 6500 and  ≈ 5600, respectively, in the two arms. Observations were taken with AB nodding with an offset of 3. 8 and individual exposures were 383 s in the VIS and 230 s NIR arm. Observations were taken with an average airmass of 1. 14 and seeing of 0. 8. Data reduction was performed using the standard X-shooter pipeline  (Freudling et al.
RESULTS In this study, we are mainly interested in line diagnostics using Lyα, C , He , the C doublet, Mg  2796, 2804 Å (Mg ),  [O ] , and [Ne ]  3870 Å ([Ne ]). 2  The measured velocity offsets, line fluxes, and rest-frame EWs (or upper limits) of these lines presented in Figure  1  are summarised in Table  1. In the following paragraphs, we will discuss the results for the X-shooter and SINFONI data sets individually. We derive the systemic redshift from the [O ]  3727, 3730 Å doublet in combination with the [Ne ]  3870 Å line for increased precision; for X-shooter, this is measured to be  sys = 4. 8737 ± 0. 0010 (uncorrected for a negligible barycentric velocity of 5-7 km/s), while for SINFONI it is  sys = 4. 8754 ± 0. 0003 (again not corrected for a barycentric velocity of up to 27 km/s or Δ = 0.
(2018)  for all high-redshift galaxies. We then consider Δ FMR (12 + log(O/H)), the deviation of the measured metallicity from the local FMR, defined as Δ FMR 12 + log (O/H) =12 + log (O/H) -12 + log (O/H) FMR. Here, we describe the metallicity predicted by the local FMR, (12 + log(O/H)) FMR , as a function of stellar mass and SFR through Equation (  5 ) in  Curti et al. (2020). The best-fit parameters obtained by Curti et al. adopt   -based metallicity calibrations (as  Bian et al. 2018 , but based on the full SDSS dataset). Uncertainties are estimated by independently varying the input variables ( * , SFR) within the 1 uncertainty range and adding the resulting deviations in quadrature. For LyC leakers in the Izotov et al. sample, we calculate the FMR offset if the metallicity has been reported.
ACKNOWLEDGEMENTS We thank  Joanna Piotrowska  for assisting with the dust correction of SDSS galaxy line fluxes. We are furthermore grateful to the anonymous referee for their helpful suggestions. JW, RS,  R. Maiolino , and MC acknowledge support from the  ERC Advanced  Grant  695671 , "QUENCH" and the  Science and Technology Facilities Council (STFC). JW, RS, and  R. Maiolino  acknowledge support from the  Fondation MERAC. RS acknowledges support from an  NWO   Rubicon grant , project number  680-50-1518 , and an  STFC Ernest Rutherford Fellowship  ( ST/S004831/1 ). NL acknowledges support from the  Kavli Foundation. R. Massey  and MS acknowledge funding from  STFC  via awards  ST/T000244/1  and  ST/T002565/1. This work has also used the following packages in : the S P library (Jones et al.
DATA AVAILABILITY The X-shooter and SINFONI data underlying this article are available in the ESO archive at https://archive. eso. org/ under ESO programme IDs 0102. A-0704(A) and 075. B-0636(B), respectively. The HST data underlying this article are available in the MAST archive at 10. 17909/T9-9KG5-HG27. The reduced data underlying this article will be shared on reasonable request to the corresponding author.
Plasma Precipitation Rate The SW ions entering in the magnetosphere: (a) reach the planet's surface at the cusp regions causing ion sputtering and ion neutralization and back-scattering, hence producing neutral atoms and ions with energies up to hundreds of eV; (b) partially are diffused toward closed field lines and circulate in the magnetosphere; (c) partially exchange their charge with the thermal exospheric atoms, producing a Hydrogen-ENA signal in the keV range. The intense flux of SW protons circulating at low altitudes, toward and from the planet will be measured by MIPA. Clear signatures of plasma precipitation in the northern cusp is evident in both MES-SENGER plasma and magnetic field data in the vast majority of orbits that cross this region  (Winslow et al. 2012). In fact, Mercury's dayside magnetopause is frequently experiencing reconnection as a result of the low Alfvénic Mach number (M A = V SW /V A ) conditions, where V SW is the SW bulk velocity and V A is the Alfvén speed and hence low-β Fig.
g. Kallio and Janhunen 2004; Massetti et al. 2003) , as well as the protons circulating inside the magnetosphere  (Mura et al. 2005)  can interact with the exospheric atoms via charge-exchange, hence producing a Hydrogen-ENA signal in the energy range between several hundreds of eV and tens of keV. When MPO will occasionally point off-nadir, towards the limb, as well as when considering the edge pixels of the field-of-view, which will observe the limb when the MPO spacecraft approaches the apoherm, ELENA will have the opportunity to observe the ENA, thus providing useful information on the plasma circulation close to the planet. The combination of the data on convection in the Hermean magnetosphere provided by PICAM and MIPA associated to magnetic field information by MAG and together with correlated measurements onboard MMO, will help in understanding the structure and dynamics of the magnetosphere of Mercury.
The observation time must be at least half of an orbit for latitudinal characterization with a resolution lower than 10 min/mass spectrum and at least 1 full orbit for local time asymmetries investigation with a resolution lower than 30 min/mass spectrum. Note that the integration time on board will be always less than 100 s, so when talking about time resolution, we refer to continuous measurement to be integrated on ground. Due to the slow precession of the orbital plane over the planet, multiple orbits (10-15) can be co-added to reach the desired Signal-to-Noise ratio. The investigation of exospheric time variability as a function of external conditions requires measurements in similar positions but occurring during different Sun activities. Considering that MPO arrival will be in the Solar cycle ascending phase, an appropriate statistical dataset can be reasonably achieved by collecting some minutes of observations every 3 hours for a total integration time of at least 1200 minutes in similar positions (for instance, close to sub solar point, close to dawn or dusk terminator).
If, for example, the shocked SW is gyrotropic, but with a pronounced temperature anisotropy, it may be sufficient to resolve two down-going directions. If the temperature anisotropy is fairly constant it may be sufficient to estimate the total flux. The binning of MIPA data must be flexible enough to allow for a trade-off between energy, time and angular resolution in the low telemetry modes. The choice must be made based on actual observations made in a high time resolution mode before. Suitable time resolutions must be < 1 minute. Seki et al. (2013)  estimated the Na + ion fluxes from the magnetosphere toward the surface on the night side up to 10 6 cm -2 s -1 sr -1 , with energies up to 10 keV (Fig. 5 ). Both MIPA and PICAM sensors have a wide FOV able to catch at least partially the heavy ion precipitating fluxes and will be operated to maximise the geometrical In summary, MIPA will measure the flux of SW circulating and precipitating ions at Mercury.
Heavy Ions Precipitation For this scientific investigation the simultaneous magnetic field data from MAG at a time resolution comparable or higher to the sampling rate of PICAM and MIPA are necessary to define ions pitch-angle distribution and precipitating ion flux. During particular MPO-Mio configurations (when both spacecraft will be located along the same flux tube of precipitating ions, see for example the two orbits in Fig. 8  the ion fluxes could be observed from two vantage points (by Mio/MPPE sensors and MPO/SERENA IS), thus greatly improving the study of dynamical behaviour  (Milillo et al. 2020). The frequency of such useful configurations will be evaluated when mission phases and detailed operations will be defined.
Angular and Azimuthal Coverage and Resolution In order to check the overall performance and the axial symmetry of the instrument, a beam of 0. 2, 0. 5 and 1 keV He + ions, respectively, was directed toward the centre of each sector at different elevation angles between 25 • and 90 •. For each elevation angle the count rates of each pixel were recorded by operating PICAM in measurement mode 1 (i. e. , full angular resolution, without sweeping through the energies. A comprehensive list of modes is given in Table  11 ). After a time interval of 40-160 sec, the data recording was terminated and  identical measurements were repeated for the next elevation angle. In this way the count rates of all pixels according to the incoming ion beam have been obtained.
Regardless of the Compression/DPU state, the following SERENA SCU operational modes can be commanded:  • STAND-BY: this is the default mode, in which SCU is switched following a reset or a power-up. This mode is realized with the Reduced S/W stored in PROM and can be de-   The way to transfer science data can be commanded in raw mode or in compressed mode. The application S/W must be started via the 'jump to Application S/W' through TC ZSE01044 to enter the compressed mode. There is no automatic switching mode except failures. The SCU science mode should not be split between high and nominal science, as there are several combinations of sensor science modes. Such sensor combinations are not reflected in the high level SCU modes (only one SCU FULL SCIENCE mode is foreseen). All SCU scientific functionalities are available when the Main program is running.
Each sensor unit foresees at least these basic modes: • STAND-BY MODE • SCIENCE MODE(s): one or more science modes with different power and telemetry budget defined for each unit. The "Nominal science mode" indicates the default/preferred one. The "High science mode" indicates the mode with high science performances. • Other possible modes for calibration/diagnostics. • When SCU runs in FULL SCIENCE mode, all the sensor modes can be commanded with the following constraint: -Not overcome the overall SERENA data telemetry budget (38000 bit/s). -Possibly avoid simultaneous full-operational modes for PICAM and MIPA.
TM Traffic Due to INSTRUMENT SCIENCE TM(21, 3) The sections hereafter provide the TM traffic tables due to all the SERENA sensors. The estimated compression factor is 2, but this may vary depending on the data itself. RICE compression factor depends on entropy, can be as small as 1 and as high as 4 -> 2 is a best estimate. MIPA uses 15 modes which are mapped according to MIPA SICD BC-SRN-TN-40000 Iss2 Rev. 17 to actual binning parameters and will be used for science planning and in-flight. -Science modes 0 to 13 are nominal. Science mode 14 "Raw" is a contingency mode to find errors and will not be used routinely. -Mode 0 "Full" will be used in a few occasions during commissioning and initial operations. -The difference between Mode 0 and 1 is only the long term average telemetry production.
Acknowledgements SERENA general management, SCU and ELENA are funded by the  Italian Space Agency (ASI)  and by the  Italian National Institute of Astrophysics (INAF) ; SERENA ground base activity is also funded by the  European Space Agency (ESA). Strofio is funded by  NASA  through  Marshall Space Flight Center  under the Discovery Program Office. PICAM is funded mostly by the  Austrian Space Applications Programme (ASAP)  of the  Austrian Research Promotion Agency (FFG) , and partially by the  Programme de Dévelopement d'Expériences scientifiques (PRODEX). MIPA is funded by the  Swedish National Space Agency. We thank  M. Dosa ,  A. Galli ,  A. Madar ,  F. Stenbeck ,  A. Timar , and  A. Vorburger  for their support to the  SERENA program. We thank  H. Laakso  for providing Fig. 7 and info about his spacecraft potential model.
The PETALE experimental program in the CROCUS reactor intends to provide integral measurements on reactivity worth and dosimetry measurement to constrain nuclear data relative to stainless steel heavy reflectors. The experimental setup consists in eight successive plates of pure iron, pure nickel, pure chromium, or nuclear-grade stainless steel set at the close periphery of the core. The plates are interleaved with up to nine dosimeters that consist of thin activation foils with different possible materials to be sensitive to different ranges of the neutron spectrum. A precise measurement with a good estimation of the uncertainties and correlations is required, especially when comparing reaction rates, e. g. transmission measurement and/or spectral indices. The present work focuses on the validation of the dosimetry technics developed in preparation of this experimental program.
INTRODUCTION Dosimetry is an efficient way to measure the neutron flux without introducing an important perturbation thanks to a reduced detection mass and volume. In the PETALE program, various possible materials and reactions will allow to be sensitive to different ranges of the neutron spectrum  [1, 2]. The flux amplitude weighted by the cross section is obtained assuming a very fine characterization of the whole measurement procedure. We focus in this paper on the validation of the experimental protocol and of the numerical tools developed for the analysis of the transmission phase of the experimental program PETALE. The objective is to compare the Monte Carlo calculations to the test experimental results, taking into consideration all the intermediate aspects such as monitor normalisation, efficiency of the High Purity Germanium (HPGe) spectrometers, dosimeter self-absorption and geometrical corrections.
The different sources of uncertainties like the neutron cross sections related to the dosimeter activation reactions will be quantified including their correlation with respect to the different dosimeter positions and materials. This aims at demonstrating the capability to characterize the neutron spectrum with a precision around the percent for the observables of interest in order to be able to provide data for heavy reflector cross section validation or assimilation  [3, 4]. The experimental description is detailed in section 2. The experimental results and modeling are respectively presented in sections 3 and 4. Finally, all the detailed intermediary steps are combined in section 5.
Propagation of the nuclear data uncertainty on dosimetry reactions The IRDFF dosimetry database  [9]  has been used for the detector estimation in Serpent2. Together with the estimation of the reaction rate itself, a fine spectrum is computed in order to propagate the dosimetry covariance matrix on the reaction rate using the sandwich rule. In this study the dosimeters are composed of different materials, then there is no correlation between the dosimeters regarding the nuclear data uncertainty on the dosimetry cross section. This would be different if the same dosimeter were used in different positions with a different apparent neutron spectrum. The example of the nickel dosimeter is given in figure  6. The uncertainty propagation for the iron, aluminium, nickel, and gold dosimeters gives respectively 2.
Thus, the virtual environment disinhibited participants with a high propensity to take risks, leading to divergent thinking and mental flexibility. In the same line, Wiederhold (2013) compared participants' performance in a brainstorming task and in a negotiation task while using either a video conference software (i. e. skype) or gesture-based avatars. The results indicate that the skype condition led to a greater number of ideas but the avatar condition led to higher quality ideas and to a more positive change of the partner's perception. Other experiments performed by  Guegan et al. (2017)  and Buisine and Guegan (2020) investigated the effect of social identity cues (SIC) manipulation on creative performance and group identification in both physical and virtual groups. The results of their study suggest that having SIC (i.
Because radiomic features are typically extracted from a previously delineated tumor volume, the impact of the segmentation step on the resulting intrinsic value of radiomics has been examined in several studies. The robustness of a subset of textural features used to quantify 18 F-FDG PET uptake, depending on the segmentation technique was first investigated in esophageal cancer treated with radiochemotherapy  (6). A later study  (7)  investigated the test-retest variability of radiomic features in a dataset of 11 NSCLC patients with repeated scans and the inter-observer delineation variability in a set of 23 patients. Later, the impact of reconstruction and delineation was studied using 11 NSCLC full-body 18 F-FDG PET/CT scans in order to investigate the repeatability and the effects of the reconstruction methods and delineation  (8).
Patient Cohort Since stage 1 patients have a very different (and more favorable) prognosis compared to those with stage II or III disease, mostly driven by treatment [(surgery vs. (chemo)radiotherapy], we focused here on patients with stage 2 and 3 tumors, where the potential impact of radiomics is likely to be the most important  (16). The inclusion criteria were confirmed NSCLC, stage 2 or 3; curative (chemo)radiotherapy treatment, and pretreatment FDG PET/CT imaging. Data from 138 NSCLC patients treated at the University Hospital of Poitiers, France, were collected (Table  1 ). The data of the first 87 patients were collected retrospectively, whereas the next 51 patients were recruited prospectively within the PRINCE project (INCa, PRTK-2015, registered trial NCT03199599). The study was conducted according to the guidelines of the Declaration of Helsinki.
PET/CT Imaging All patients underwent a combined 18 F-FDG PET/CT acquisition as part of the diagnosis and staging before treatment. A Biograph mCT 40 ToF with axial field of view of 21. 6 cm (Siemens, Erlangen, Germany) was used, relying on the routine clinical protocol. PET/CT acquisition began after 6 h of fasting and 60 ± 5 min after injection of 2. 5 MBq/kg of 18 F-FDG (421 ± 98 MBq, range = 220-695 MBq). Non-contrast-enhanced, non-respiratorygated (free breathing) CT images were acquired (120 kVp; Care Dose ® current modulation system) with an in-plane resolution of 0. 853 × 0. 853 mm 2 and a 5-mm slice thickness. PET data were acquired using 3. 5 min per bed position, and images were reconstructed using a CT-based attenuation correction and the standard routine clinical protocol, as we recently showed no improvement in the prognostic value of radiomic features when using different settings (either smaller voxels or smaller full width at half maximum of the Gaussian post-filtering) (18): OSEM-TrueX-TOF algorithm, with time-of-flight and spatial resolution modeling (three iterations and 21 subsets, 5-mm 3D Gaussian post-filtering; voxel size, 4 × 4 × 4 mm 3 ).