dataesr/scientific-paragraphs-categorization huggingface dataset:
The cell nuclei were stained with Hoechst (Enzo). The obtained samples were analyzed by Axio Vert. A1 fluorescence microscope (Zeiss) with Axio Cam ICc 5 (Zeiss) and ZEN lite 2012 software. Phagocytic activity was also quantified. After 24-h incubation with E. coli bioparticles, the cells were fixed in 4% PFA-PBS, and their fluorescence (Ex = 494 nm, Em = 518 nm) was measured using a Synergy HT Multi-Detection Microplate Reader (BioTek). The level of fluorescence was proportional to the amount of phagocytized particles. The results are expressed as the percentage of phagocytosis compared with control values (100%).
5 g for nanoparticles, 1. 5 g for impregnated sandstone, and 14 g for sandstone. The contribution of the empty sample holder was systematically measured and subtracted to all data to improve accuracy. The isosteric heat of adsorption, Q ST , was calculated using the isosteric method with the Microactive software (from Micromeritics, Norcross, USA) from three adsorption isotherms at 0, 25, and 50 • C, based on the Clausius-Clapeyron equation  [51] : - Q ST R = ∂ ln (P) ∂ (1/T) ( 5 ) where R is the universal gas constant (8. 314 J mol -1 K -1 ), P is the absolute pressure, and T is the temperature. The excess adsorbed CO 2 amount (N exc , g CO2. g adsorbent -1 ) was equal to the absolute adsorbed CO 2 amount (N ads, g CO2. g adsorbent -1 ) minus the product of gas density in the bulk phase by the volume of the adsorbed phase.
From the adsorption results of each N 2 and CO 2 isotherm, it was possible to predict the selectivity by applying the ideal adsorbed solution theory (IAST), which allows estimating the competitive adsorption of the compounds in a mixture of gases as the flue gas. Based on the literature, a model flue gas comprising 80% N 2 and 20% CO 2 was selected. The IAST was implemented in a Python routine (package-pyIAST from  Simon et al. [56] ).
The isothermal interpolator is a tool included in the PyIAST package. After that, PyIAST takes the interpolated data for its calculations. Calculations are done using a predesigned routine  [56]. In the present case, the experimental data of each pure component (CO2 and N2) were obtained by HP-TGA at 50 °C, between 0. 1 and 2. 5 MPa, and with a flow of CO2 or N2 (50 mL min -1 up to 1. 0 MPa and 70 mL min -1 up to 2. 5 MPa). The prediction was calculated at 50 °C by varying: 1) the system pressure from 0. 1 to 2. 5 MPa at constant CO2 concentration of 20% and 2) the concentration of CO2, from 5 to 100% at a constant pressure of 2. 5 MPa. The evaluated materials were CN. LYS2 and sandstone from a real reservoir (RS) impregnated with a mass fraction of 20% of CN. LYS2. It is important to mention that it might be possible to obtain considerable adsorbed quantities for a cleaning and adsorption balance of more than 12-24 hours, because the nanomaterials (main adsorbent) are micro/mesoporous.
The isothermal interpolator is a tool included in the PyIAST package. After that, PyIAST takes the interpolated data for its calculations. Calculations are done using a predesigned routine  [56]. In the present case, the experimental data of each pure component (CO 2 and N 2 ) were obtained by HP-TGA at 50 • C, between 0. 1 and 2. 5 MPa, and with a flow of CO 2 or N 2 (50 mL min -1 up to 1. 0 MPa and 70 mL min -1 up to 2. 5 MPa). The prediction was calculated at 50 • C by varying: (1) the system pressure from 0. 1 to 2. 5 MPa at constant CO 2 concentration of 20% and (2) the concentration of CO 2 , from 5 to 100% at a constant pressure of 2. 5 MPa. The evaluated materials were CN. LYS2 and sandstone from a real reservoir (RS) impregnated with a mass fraction of 20% of CN. LYS2. It is important to mention that it might be possible to obtain considerable adsorbed quantities for a cleaning and adsorption balance of more than 12-24 h, because the nanomaterials (main adsorbent) are micro/mesoporous.
Tryptase, released during mast cell degranulation, has been demonstrated to specifically activate PAR2 through the cleavage of its N-terminal domain. 31, 32 Therefore, Par2 expression was assessed in the colon of mast cell-deficient mice. In control condition, Par2 mRNA levels were not modified in mast cells deficient mice. However, aluminuminduced upregulation of PAR2 in WT mice was abolished in Kit W-sh/W-sh mice (Figure  6E ). These data show that aluminum-induced mast cells activation is required for PAR2 upregulation.
Statistics Data are expressed as mean ± SD. For Mice visceromotor response, a repeated-measures 2-way analysis of variance was performed. For all other parameters, differences between groups were compared using the Mann-Whitney nonparametric U test (GraphPad Prism version 5. 03, GraphPad Software, La Jolla, CA) (*P <. 05, **P <. 005, ***P <. 0005 in the figures).
Context. The census of stellar and substellar companions of nearby stars is largely incomplete, in particular toward the low-mass brown dwarf and long-period exoplanets. It is, however, fundamentally important in the understanding of the stellar and planetary formation and evolution mechanisms. Nearby stars are particularly favorable targets for high precision astrometry. Aims. We aim to characterize the presence of physical companions of stellar and substellar mass in orbit around nearby stars. Methods. Orbiting secondary bodies influence the proper motion of their parent star through their gravitational reflex motion. Using the Hipparcos and Gaia's second data release (GDR2) catalogs, we determined the long-term proper motion of the stars common to these two catalogs. We then searched for a proper motion anomaly (PMa) between the long-term proper motion vector and the GDR2 (or Hipparcos) measurements, indicative of the presence of a perturbing secondary object.
Introduction In the present work, we examine the proper motion (hereafter PM) of the nearby stars located within 50 pc from the Sun using the Hipparcos catalog (van Leeuwen 2007, hereafter 'Hip2', see also  Perryman et al. 1997 ) and Gaia's second data release (GDR2; Gaia  Collaboration et al. 2016b Collaboration et al. , 2018a)). Developing the approach presented by  Kervella et al. (2019)  for Galactic Cepheids and RR Lyrae stars, we took advantage of the long time baseline of 24. 25 years between the Hipparcos and GDR2 position measurements to determine the mean long-term PM vectors of nearby stars with a high accuracy. We then compared this vector to the individual measurements obtained by Gaia and Hipparcos to test for the presence of anomalies that indicate the presence of an orbiting secondary body.
Observational data For the collection of most of the data used in the present work, we made extensive use of the astroquery set of tools  (Ginsburg et al. 2017 ) distributed as part of the Astropy library  (Astropy Collaboration et al. 2018)  to access the ViZieR online database  (Ochsenbein et al. 2000)  at the CDS. For simplicity, we note µ α the PM along the right ascension axis µ α cos(δ).
Hipparcos astrometry The position, PM, and parallax data of our sample are taken from Hip2 (van Leeuwen 2007) and the GDR2  (Gaia Collaboration et al. 2018a ) catalogs. We checked that the results of our computations do not depend significantly on the chosen version of the Hipparcos reduction. We computed the PMa of a representative set of objects using the original Hipparcos catalog positions  (Perryman et al. 1997; ESA 1997 ) (Hip1) and we did not notice any significant difference in the results. To determine the proper motion accelerations of the Hipparcos catalog stars,  Brandt (2018)  recently adopted a linear combination of the Hip1 and Hip2 reductions of the Hipparcos data to estimate the star positions. This original approach is intended to mitigate the systematics associated with each of the catalogs considered individually.
Acknowledgements. We thank  Dr Vincent Coudé du Foresto  and  Dr Timothy D. Brandt  for fruitful discussions that led to improvements of the present paper. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (http://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This research made use of Astropy 4 , a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013, 2018). This research has made use of the Washington Double Star Catalog maintained at the  U. S. Naval Observatory. We used the SIMBAD and VIZIER databases and catalog access tool at the  CDS, Strasbourg (France) , and  NASA 's Astrophysics Data System Bibliographic Services.
Bioinformatics Analysis Quality control of the RAW sequencing reads was performed using FastQC 0. 11. 7. RNA-seq reads were processed by cutadapt v1. 16 trimming adaptor sequences on the 3 end with a 10% error rate. Based on the FastQC results, 10 nucleotides were cropped from the 5 end of each read, and reads with a length of less than 20 bp were discarded. Samples were then directly quantified using Salmon v0. 12. 0  [14]  on an index created from the GRCm38 transcript using GENCODE vM18, tRNA and piRNA precursors. Then, transcript counts were imported into tximport for all downstream analysis and were then summed by gene symbol  [15]. Quantifications were also done using featureCounts. For differential transcript analysis, only protein-coding transcripts and long non-coding RNA that had at least 20 reads in each sample were fixed.
Differential transcript analysis of the D and R fractions in mouse sperm RNA-seq samples was performed with DESeq2 (version 3. 8)  [16]  and edgeR R package  [17]. The p-values were adjusted using the Benjamini and Hochberg method  [18]. An adjusted p-value (Padj) of 0. 05 was set as the significant differential transcript threshold. Plots were generated using the ggplot2 R package (version 3. 1. 0) [19]. Hisat2 was used to align trimmed reads to the reference genome (mm10)  [20]. we ensured quality of alignment using stats and plot-bamstats utilities of samtools  [21]. Visualization of alignment was performed using Integrative Genome Viewer (IGV) version 2. 5. 2  [22]. Gene ontology (GO) enrichment and pathway analysis were carried out using the DAVID functional and enrichment tool  [23].
The resulting GO terms with a corrected p-value of less than 0. 05 were considered significantly enriched. For heatmaps, log2 (1-normalized counts) was used. RNA-seq reads were also quantified for tRNA-derived fragments and piRNA. An index of tRNA and piRNA sequences was constructed using tRNA sequences from GtRNAdb  [24]  and piRNAdb with parameters "-kmerLen 15 '. Percent identity matrix of the mapped reads was visualized using R package heatmap.
Overview of Transcripts Analysis GO term enrichment analysis of reads mapping to differentially detected transcripts in D versus R molecules in sperm (log2-fold change >2, Padj < 0. 05) were assessed with DAVID Bioinformatics Resources  [25]. Figure  5  shows most significant GO terms were enriched for the biological process, cellular component and molecular function, among other things (see Figure  5A , Supplementary Materials File S5), and clearly reflect transcripts for critical functions. These results showed that most of the RNAs found in D fraction sperm samples versus R fraction were related to most transcription networks. The compartments to which the most significant transcripts were localized and molecular functions are demonstrated (Figure  5B ,C and Supplementary Materials File S5).
The time difference T S -T 0 is measured with a multi-hit fast time to digital converter (TDC). The data reduction process was performed with the AGS code  [4] , developed at the JRC Geel. The experimental transmission is mostly derived from the ratio of the TOF spectra taken with and without a sample, both corrected for dead time and background. For measurement in a good transmission geometry, i. e. a parallel incident neutron beam, a homogeneous sample and a fixed geometrical neutron path length through the sample, this experimental ratio can be directly related to the areal density and total cross section through the Lambert-Beer law. The geometry for the measurements of the cylindrical MINERVE samples does not fulfil the good transmission geometry conditions. Part of the neutron beam does not interact with the sample and the geometrical path length of the neutrons in the sample varies (Fig.
Neutron Resonance Transmission Analysis with the REFIT code The composition of the sample was derived by a least squares fit to the data using the resonance shape analysis code REFIT  [7]. This code is optimised to determine resonance parameters by comparing the experimental transmission with the theoretical one. It accounts for various experimental effects such as Doppler broadening and response function of TOF spectrometer. The theoretical transmission as a function of the incident neutron energy in case of a homogeneous sample and a constant neutron path length is given by the Lambert-Beer law, which is not valid for the MINERVE samples with cylindrical geometry. Therefore, a new analytical expression was implemented in the REFIT. The transmission through a cylindrical sample starting from an incident parallel, uniform neutron beam is expressed as: T (E) = r -r 1 -(x/R) 2 exp -2r 1 -(x/r) 2 k ρ k σtot,k (E) dx r -r 1 -(x/R) 2 , (2) where ρ k is the volume number density of nuclide k, σtot,k its Doppler broadened total cross section and r and R are the sample and beam radius, respectively.
Discussion and Conclusions As indicated in the Introduction, an interpretation of pile oscillation measurements at the MINERVE facility to determine the reactivity worth of specific elements reveals substantial differences between the calculated and experimental results. The tungsten contamination observed in this work could cause such a difference. This assumption was investigated for the silver samples of the Burn-Up Credit programme (AG9C1, AG9C2 and UNC) by including tungsten in the neutron transport calculations. The reactivity worth in a PWR-UO 2 lattice was calculated with the PIMS tool  [11] , using the APOLLO2 deterministic code  [12]. The impact of the tungsten content was calculated for the AG9C1 and AG9C2 sample, but also for the reference sample UNC, that only contains pure UO 2 pellets without silver.
, 2008). An accurate knowledge of changes in coastal Antarctic surface mass balance (SMB), an evaluation of the ability of climate models to resolve the key processes affecting its variability, and thus an improved confidence in projections of future changes in coastal Antarctic surface mass balance are important to reduce uncertainties on the ice sheet mass balance and its contribution to sea level change  (Church et al. , 2013). Meteorological observations have been conducted since 1957 in manned and automatic stations  (Nicolas and Bromwich, 2014) , and considerable efforts have been deployed to compile and update the corresponding dataset Published by Copernicus Publications on behalf of the European Geosciences Union. (Turner et al. , 2004). This network is marked by gaps in spatio-temporal coverage  (Goursaud et al.
, 2017). Several authors have thus identified the potential to identify changes in moisture sources using dexcess  (Delmotte et al. , 2000; Sodemann and Stohl, 2009; Ciais et al. , 1995). The comparison between multi-year isotopic precipitation datasets with the identification of air mass origins using back trajectories showed, however, a complex picture, with no trivial relationship between the latitudinal air mass origin and d-excess  (Dittmann et al. , 2016; Schlosser et al. , 2008). A few studies have also explored sub-annual dexcess variations and suggested that seasonal d-excess signals cannot be explained without accounting for seasonal changes in moisture transport (e. g. Delmotte et al. , 2000). These features have been explored through the identification of back-trajectory clusters and their relationship with δ 18 Od-excess relationships, including phase lags  (Markle et al.
Instrumental data To assess potential climate signals archived in our firn core records, we extracted meteorological data to explore regional climate signals, and outputs of atmospheric models to explore synoptic scale climate signals. (Cavalieri et al. , 1996). D'Urville summer sea ice extent was estimated by extracting the number of grid points covering the area (50-90 • S, 135-145 • E) where the sea ice concentration is higher than 15 %, from December to January (included) for each year from 1998 to 2014. SMB measurements from stake point data were obtained from the GLACIOCLIM observatory (https://glacioclim. osug. fr/, last access: January 2018). We extracted data from the three stakes closest to the TA drilling site, namely 18. 3 (66. 77 • S, 139. 57 • E; 1. 04 km from the TA drill site), 19.
Database of surface snow isotopic composition In order to compare the d-excess record from the TA firn core with available Antarctic values, we have updated the database of  Masson-Delmotte et al. (2008) , by adding 26 new data points from precipitation and firn core measurements provided with d-excess (Table  1 ). This includes data from five ice cores from the database constituted by the Antarctica2k group  (Stenni et al. , 2017). Altogether, the updated database includes 777 locations. This includes 64 coastal sites at an elevation lower than 1000 m a. s. l. , (with 19 new datasets). These data were extracted from our updated isotope database  (Goursaud et al. , 2018a)  archived in the PANGAEA data library (https://doi. org/10. 1594/PANGAEA. 891279). Table  1. Site, latitude ( • ), longitude ( • E), and reference of new d-excess data added to  Masson-Delmotte et al.
25 [Na + ]. For depths lower than 10 m w. e. , summer (December-January) peaks were identified (i) from nssSO 4 values higher than 100 ppb, synchronous with MSA peaks (with no threshold), and (ii) for nssSO 4 values higher than 200 ppb (with or without a simultaneous MSA peak). Double nssSO 4 peaks were counted as one summer  (e. g. 2012, 2003, and 2001). For depths higher than 10 m w. e. , summer peaks were identified for nssSO 4 values higher than 27 ppb. The outcome of layer counting allowed us to estimate annual layer thickness, which, combined with the density profile, allowed us to estimate annual SMB in the firn core. This estimated SMB was then compared with stake area data. The three stake data closest to the TA firn core  (18. 3, 19. 2, and 20. 3, not shown)  depict the same inter-annual variability (pairwise coefficient correlations, r > 0.
g. assumption of synchronicity between temperature seasonal cycles and water stable isotope records). We note an uncertainty in layer counting of 3 years when comparing the outcome of layer counting using chemical records with δ 18 O TA peaks, which have nonetheless been excluded from our dating, as they do not improve the correlations, either between the reconstructed SMB and the stake data or between our records and the ECHAM5-wiso simulations (Tables  S1 to S3 ). As a result, we consider the "best guess" chronology results from the annual layer counting based on nssSO 4 and MSA refined with the comparison with stake data, giving a total of 18 summer peaks (green vertical lines, Fig. 3 ). In the following, we thus use the dated firn core records covering the complete period 1998-2014. We note that our chronology is more robust for the period 2004-2014, for which stake area SMB data are available.
the-cryosphere. net/13/1297/2019/ The Cryosphere, 13, 1297-1324, 2019 Figure  3. Identification of annual layers in the TA192A ice core based on the dual identification of nssSO 2- 4 and MSA summer peaks and comparison of estimated accumulation with annual accumulation measured at the closest stakes (not shown). The thick vertical green lines correspond to summer peaks identified from chemical signals, while the thin vertical green line shows the additional identification of summer 2008 added to the counted summers from the comparison between the estimated accumulation and the accumulation record from the closest stake data (see Fig. 3 ). Vertical dashed lines highlight equivocal summer peaks with a sometimes small signal in only one of the chemical records. Water stable isotope records were not used to build the timescale.
Note that double peaks in both δ 18 O and d-excess occur repeatedly within one counted year. stable through time, and if there is no significant smoothing due to post-deposition effects, we should obtain a ratio of 1. However, it is expected to be above 1 in the case of large post-deposition smoothing. We obtain a ratio of 0. 5 for δ 18 O TA data, possibly reflecting the inter-annual variability of the δ 18 O seasonal amplitude. We repeated the same exercise with all eight other sub-annual δ 18 O records from our database (Table  S4 ). Discarding an outlier (NUS 08-7), all ratios are between 1. 0 and 2. 9. Ratios based on d-excess amplitudes are similar to those found for δ 18 O (Table  S5 ). For the TA firn core, we again obtain a ratio of 1. 1 for d-excess TA. We also note high ratios for d-excess data in the NUS 08-7.
1957-2014 T s ws wd RH T s ws wd RH Prec. ERA ( • C) (m s -1 ) ( • E) (%) ( • C) (m s -1 ) ( • E) (%) (cm The average δ 18 O TA value is -19. 3 ‰ ± 3. 1 ‰, close to the average δ 18 O S1C1 of -18. 9 ‰ ± 1. 7 ‰, and the average d-excess TA is 5. 4 ‰ ± 2. 2 ‰. Compared to the 64 points located at an elevation lower than 1000 m a. s. l. from our database, the δ 18 O TA and d-excess TA average values are slightly higher than the average low-elevation records (-22. 7 ‰ ± 8. 8 ‰ and 4. 8 ‰ ± 2. 3 ‰ for δ 18 O and d-excess respectively, Fig. 5 ). Finally, the average TA concentrations in Na + , MSA, and nssSO 4 are 126. 0 ± 276. 5, 4. 5 ± 5. 6, and 36. 5 ± 44. 2 ppb respectively. Note that the Na + average concentration value is affected by strong peaks in 2003 and 2004 with annual values of 369.
61 ‰ ‰ -1 ). In order to understand the specificities of the TA record, we explored the temporal correlation between δ 18 O and dexcess from all available Antarctic records (Table  5 , cells in bold for significant relationships), using all data points (in order to be able to exploit non-dated depth profiles) as well as inter-annual variations, when available. When focusing on significant results, we note that most precipitation datasets depict an anti-correlation between d-excess and δ 18 O (three out of nine precipitation records). Although we are cautious with the short DDU precipitation time series (with only 19 points, and p values = 0. 08, cell in italics in Table  5 ), it shows a positive relationship, similar to the one identified in the TA record. We conclude that the positive correlation observed in the TA records is specific to the coastal Adélie Land region, which is unusual in an Antarctic context.
3), Dome C, or Dome F (a factor of ∼ 4). In addition to DSSA, the average seasonal amplitude obtained from TA dexcess is also comparable to the one obtained in the KM, BI, and IND25B5 firn cores in Dronning Maud Land but is systematically higher (by a factor higher than 3) than in precipitation datasets. This calls for systematic comparisons of d-excess seasonal amplitudes in precipitation and snow data. Due to their common symmetric aspect, significant positive linear relationships emerge from the mean seasonal cycles of (i) temperature, nssSO 4 , and MSA (r > 0. 93 and p < 0. 05), (ii) nssSO 4 and MSA (r = 0. 97 and p < 0. 05), (iii) nssSO 4 and Na + (r = 0. 93 and p < 0. 05), and finally (iv) δ 18 O with nssSO 4 (r = 0. 75 and p < 0. 05). Due to the asymmetry of water stable isotope seasonal cycles, no linear relationship is detected between the seasonal cycles of DDU near-surface temperature, δ 18 O TA , and d-excess TA.