dataesr/scientific-paragraphs-categorization huggingface dataset:
gov/ct2/show/NCT0 4528277?term=rifapentine&draw=2 (accessed on 3 November 2022). Efficacy and safety of 1RPT/INH in preventing latent genital TB preceding IVF among adult women with and without latent genital TB and experiencing recurrent implantation failure China Open-label, non-randomized clinical trial Phase III n = 1050 1-month (3 times/ week = 12 doses) rifapentine and isoniazid (1RPT/INH) versus no treatment PHOENIx MDR-TB (NCT03568383) https://clinicaltrials. gov/ct2/show/NCT0 3568383 (accessed on 3 November 2022). Compare the efficacy and safety of 26 weeks of DLM for preventing confirmed or probable active TB among high-risk household contacts of adults with MDR-TB Botswana, Brazil, Haiti, India, Kenya, Peru, Philippines, South Africa, Uganda, Zimbabwe Tanzania, Thailand, Multicenter, open-label, randomized clinical trial n = 5610 Phase III 26 weeks of DLM versus 26 weeks of INH ASTERoiD/TBTC Study 37 (NCT03474029) https://clinicaltrials.
gov/ct2/show/record/ NCT03474029?term=Asteroid (accessed on 3 November 2022). Compare the safety and effectiveness of a novel short 6-week regimen of daily rifapentine among people ≥12 years of age with positive TST or IGRA and at high risk of disease progression Canada, United States Multicenter, open-label, randomized clinical trial Phase III n = 3400 6-week P vs. rifamycin-based standard-of-care regimens (3HP, 4R or 3HR) SDR Risk Study (NCT04094012) https://clinicaltrials. gov/ct2/show/record/ NCT04094012?term=rifapentine&draw=6 (accessed on 3 November 2022). Compare incidence rate of systemic drug reactions under 3HP and 1HP regimen for latent tuberculosis infection treatment Taiwan Pragmatic open-label, n = 490 multicenter randomized control trial Phase III 3-month HP versus 1-month HP.
After irradiation, the mica detectors were attached for 18 minutes at 20°C in a 48% HF solution to reveal induced fission tracks. Both spontaneous and induced fission tracks were counted optically at a magnification of 1250 using an Olympus BH2 microscope and the FT-Stage 4. 04 system. Cooling ages were calculated using the RadialPlotter Version 9. 5 software  (Galbraith & Green, 1990; Vermeesch, 2009).
g. , corn grain is 36% of concentrates) and can be taken as surrogates for many of the crops. The feed mix has non-negligible contributions to the various impact categories, as also identified by other authors  [55, 56]. As described in Supplementary Materials Section S4, corn grain, soybeans, and processed feeds (DDGS, meal, and feed mix) are modeled as national commodities based on corn grain and soybean shipment data, hays are produced in the milk region (Figure  S1 ) in which they are consumed, and silages and pasture are produced in the state in which they are consumed. Nutrient field applications and losses: Field-level modeling of nutrient applications and losses was performed using the National Nutrient Loss and Soil Carbon (NNLSC) database  [57] , based on the EPIC model  [58]. In that model, phosphorus losses from agricultural fields are often driven by runoff, so precipitation plays an important role, as does topography.
Rather, they are global, legacy pollutants which are ubiquitous in the environment. Nonetheless, the impacts of these residues were assessed, and their magnitudes were compared to other human health impacts. Water consumption: In keeping with recommendations from the Life Cycle Initiative  [60] , we include consumption of water withdrawn from water bodies or groundwater reservoirs. In some frameworks, this is called "blue" water, in contrast to "green" water, which is used to distinguish precipitation from extracted water used for irrigation  [61]. Uncertainty: Uncertainties on inventory flows were systematically assessed using the same type of pedigree approach as in the ecoinvent databases  [62] , while customizing the approach to each inventory flow, depending on the database from which data are extracted and the corresponding quality of the data used (see Supplementary Material, Section S5).
Solute segregation in the carbon-depleted (CDZ) heat-affected zone of dissimilar metal welds (DMW) designed for nuclear power plant EPR TM reactors has been investigated by atom probe tomography (APT) and related to the mechanical properties (toughness test). The analysis of grain boundaries (GBs) by APT allowed the quantification of the P segregation and other solute elements, in all the analyzed samples, before and after long-term ageing heat treatments. The post-weld stress-relief heat treatment applied to the samples before the ageing heat treatments leads to important GBs segregation levels. Following thermal ageing treatments allow a relative increase of GB segregation. P segregation increases with the various thermal ageing conditions. The experimental data were used to model P segregation in high-angle GBs.
Post-processing was performed using the Orientation Imaging Microscopy (OIM) program  [21, 22]. APT volumes of all the samples were acquired using a CAMECA LEAP 3000XHR setup in electric mode. The analyses were performed at 80 K with a pulse rate of 200 kHz. The pulse fraction was set to 20 % and the evaporation rate to 0. 2 %/pulse. APT sample preparation was carried out using a dual beam SEM/FIB FEI Helios NanoLab 600. The samples (i. e. tips) were prepared using a standard lift-out method, followed by annular milling  [23]. The reconstruction mode used to process APT data is voltage, with an evaporation field of 33 Vnm -1 representative of iron. The k factor is 3,3. The interfacial excess number (N ex ) was determined for each of the different species from APT measurements in order to quantify their GB segregation.
2 and 3). The average diffusion length of a given element i, can be calculated according to: L i d = ̅̅̅̅̅̅̅̅ ̅ 2D i t √ (2) With D i the diffusion coefficient of the considered element defined as: D i = D i 0 exp ( - Q i RT ) (3) D i 0 and Q i being respectively the diffusion coefficient pre-factor and the activation energy. In this work, the values of D i 0 and Q i were extracted from the database MOBFE2  [29]  included in the software DICTRA. The calculated diffusion lengths of the elements of interest are presented in Table  4. The values calculated for the sample MC4-A correspond to atomic diffusion during the SRHT. For all the other samples, the diffusion lengths correspond to the ageing times and ageing temperatures indicated in Table  2. Substitutional atom diffusion is strongly dependent on temperature in the investigated temperature and time ranges, while the effect of temperature is less discriminating for interstitial elements such as carbon.
The effects of temperature and GB structure were mainly evaluated in systems for which a single element is segregating  [10, 40]. However, it is known that interactions between segregated elements exist in complex alloys  [39, [41] [42] [43]. These interactions can be attractive or repulsive, and the interacting elements can have their equilibrium segregation modified. For example, our results obtained on the segregation in GBs and in dislocations show that Mn-C and Mo-C interactions are clearly attractive. This is in agreement with the tendency of Mo to form carbide precipitates or with the high solubility of Mn in cementite. The APT results presented in this paper confirm that P does segregate in the GBs of all the analyzed samples. However, simulations using these experimental data as input parameters show that P segregation is kinetically limited by its bulk diffusion in the studied temperature range (below 450 • C).
77 at nm -2 , Fig. 11 ). One can note that the P diffusion length corresponding to annealing at 350 • C for 60 years is 60 nm according to the data from DICTRA (Table  4 ), corresponding to a P excess of 1 at. nm -2 on the GB if the entire amount of P located 60 nm around the GB before segregation is located on the GB after ageing, considering the P bulk concentration in our material. A similar study was carried out on P segregation at grain boundaries in the same mock-ups but in the base metal  [44]. P segregation at GBs was essentially estimated by AES. In this case, the most widely used analysis method to quantify the P segregated quantity consists in relating the phosphorus AES peak height IP120 to the iron peak height IFe703. This ratio, commonly known as the GB coverage ratio, represents the fraction of GB atomic sites occupied by P atoms.
The results of Joly et al. show an increase in coverage rates as a function of temperature and ageing time  [44]. Indeed, the coverage rate goes from a ratio of 0. 08 for an ageing at 350 • C -50,000 h (order 1) to 0. 11 for ageing at 400 • C -30,000 h (order 3) and finally to 0. 15 for ageing at 400 • C -5000 h. These results are very interesting to compare with the results presented in the present work since the ageing conditions are the same as in our study. Although the microstructure in the CDZ of DMW differs from that of the base metal, the P segregation data measured by APT from our study can be compared with those measured by AES (Table  10 ), since the steel composition is similar. P excess in at. nm -2 measured by APT can be converted into a coverage rate, similar to that obtained from AES measurements.
However, the increase of Mn and Ni segregation that is also known for lowering GB cohesion may also play a role on intergranular embrittlement. Furthermore, the segregation of other elements, such as Mo and C, which can improve GB cohesion is also observed. The contribution of GB segregation on mechanical property variations observed before and after thermal ageing is not obvious due to the combination of interactions and effects of the different solutes. The APT experimental data were used to model and simulate P segregation in HAGBs. The simulations not only show that P segregation is kinetically limited by bulk diffusion, but also that P segregation increases with temperature. The ageing heat treatments allow an increase of P segregation compared with the post heat treatment in this kinetic regime of segregation.
Counterpart Association We identify the redshifts of the candidates from the line search by exploiting the extensive multiwavelength data that are available over the HDF-N as compiled by the CANDELS and 3D-HST surveys  (Grogin et al. 2011; Koekemoer et al. 2011; Brammer et al. 2012; Skelton et al. 2014; Momcheva et al. 2016) , including spectroscopic redshifts (e. g. ,  Barger et al. 2008) , as well as the Herschel data  (Elbaz et al. 2011). While for the brightest and/or lower-redshift galaxies, the counterpart association is often clear, the relatively large beam of the NOEMA data (compared to typical galaxy sizes) can sometimes lead to ambiguities in the association. There may be multiple galaxies near the peak of the emission, or, especially at lower S/N, the peak of the emission may be offset from the position of the counterpart.
Cosmic Molecular Gas Density and Cosmic Variance The evolution of the cosmic molecular gas density is determined from the CO LFs at different redshifts. We combine the constraints from NOEMA in the HDF-N and ASPECS in the HUDF by integrating the Schechter fits to both surveys down to the lowest ¢ L probed in the data (i. e. , we do not extrapolate the faint end). This is consistent with earlier work, and the integral should trace the bulk of the molecular gas mass given that the knee of the LF is well sampled. Indeed, the stacking results at 1 z < 2 from ASPECS  (Inami et al. 2020)  imply that there is not a large amount of gas mass missed at fainter luminosities. To convert the observed CO emission to a gas mass, one needs to adopt a CO-to-molecular gas conversion factor, α CO (including He), and an average excitation correction that is representative for galaxies around the knee of the LF.
A. Precipitation of intermediate and final SrSO 4 phases All solutions used for the precipitation experiments were created by mixing equal volumes of equimolar solutions (100 mM) of SrCl 2 (99% extra-pure SrCl 2 ⋅ 6H 2 O, Acros Organics) and Na 2 SO 4 (≥99% purity, Roth) dissolved in deionized water. The saturation index with respect to pure celestine -Ω = log[a(Sr 2+ ) ⋅ a(SO 4 2-)/k sp,celestine ], where a(Sr 2+ ) and a(SO 4 2-) are the activities of the Sr 2+ and SO 4 2-ions in solution, and K sp,celestine is the solubility product of celestine (10 -6. 62 )-was calculated for all solutions using the code Phreeqc and the BRGM Thermoddem geochemical database. The optical characterization of the nucleation reaction was conducted with a UV-VIS Cary 3500 (Agilent) instrument. Strontium sulfate precipitation experiments were carried out by mixing equal volumes of SrCl 2 and Na 2 SO 4 stock solutions into a 100 mM NaCl solution (to maintain a constant background ionic strength).
B. Characterization of solid samples 1. Powder x-ray analysis The composition of the solid samples was first evaluated with powder x-ray diffraction (PXRD). PXRD patterns were recorded with a Bruker D8 powder diffractometer equipped with a SolXE Si(Li) solid state detector from Baltic Scientific Instruments using CuKα 1,2 radiation. Intensities were recorded at 0. 026 ○ two-theta step intervals from 5 ○ to 90 ○ with 6 s counting time per step. Data were evaluated using the code DIFFRAC. EVA (Bruker) for comparison to previously published structures (ICDD PDF 00-005-0593 for celestine and ICSD 167 054 for hemihydrate). Approximately 1 g of powder was analyzed for each sample. The analyzed celestine powder had a granulometry <50 μm. The hemihydrate was broken up as much as possible inside a plastic beaker using a spatula to produce a fine-grain powder (hemihydrate rapidly transformed to celestine when using a sieve or mortar and pestle).
Powder x-ray diffraction (PXRD) In situ PXRD was conducted in a TTK Anton Paar chamber with a ProUmid humidity controller. Samples were kept at 23 ○ C during data collection, whereas the desired relative humidity value (RH) was maintained (±2% RH) by using a constant flow of mixed dry/saturated air. RH was continuously monitored with a hygrometer located next to the sample. Samples were equilibrated at the desired humidity (ranging from 70% to 90%) for a minimum of 10 min before starting data collection. To increase the time resolution, counting times were reduced to 3 s, and scanning was limited to the 2θ ranges from 12 ○ to 16 ○ and 30 ○ -34 ○ in order to focus on one unique peak for hemihydrate (14. 28 ○ ) and one unique peak for celestine (32. 79 ○ ). The mass fractions of the phases were estimated using a semi-quantitative reference intensity ratio (RIR) technique,  40, 41  based on XRD peak intensities (peak heights) and defined by Ia = Kaxa μsρa , where Ka is a material parameter for phase a, ρa is the density of the phase, μs is the permittivity of the entire sample, and xa is the mass fraction of phase a.
ACKNOWLEDGMENTS The authors are grateful to the  French national research agency ,  ANR , Project No. CATCH ,  ANR-18-CE05-0035-03. Finally, we express our gratitude to  Francine Roussel at the Grenoble INP. The use of the geochemistry-minerallogy platform of ISTerre is acknowledged. The FTIR instrument was supported by funding from  CPER "Montagne 4. 0" and the regional platform "CEMBRO" (Changement Environnement et Biodiversité: Rétro-Observation et Observation). This work used also the platforms of the Grenoble Instruct-ERIC center (ISBG;  UAR 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INBS-0005-02 ) and GRAL, financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ).
Given its large abundance, CO is the next most reliable molecular tracer in the universe (for an in-depth review, see  Bolatto et al. 2013). Over the last decade, extensive CO mapping across nearby galaxies has been possible thanks to the great sensitivity and speed of single-dish radio telescopes and interferometers. These routine observations have tremendously improved our understanding of the physics regulating star formation in galaxies. High spatial resolution datasets (e. g. , 700 pc) have allowed studies such as the seminal work by  Bigiel et al. (2008)  to show that a KS-type power law with an index N = 1. 0 ± 0. 2 holds down to subkiloparsec scales between Σ SFR and Σ H2 across a large sample of spiral, star-forming galaxies. Later studies have also found N ∼ 0. 8-1. 4 for this KS relation (e.
2016; Moretti et al. 2020; Cramer et al. 2020 Cramer et al. , 2021;; Lee et al. 2020; Boselli et al. 2022). Investigating whether there are significant differences in the KS law between star-forming regions in cluster galaxies and those of normal, star-forming disks is key to understand the imprint of cluster environments on the star formation process at subkiloparsec scales. To address these questions, we employed a wide set of multiwavelength data to trace star formation and molecular gas, and we measured Σ SFR and Σ H2 in a sample of 37 galaxies from the Virgo cluster of galaxies. The present study constitutes one of the first science papers from the Virgo Environment Traced in CO (VERTICO,  Brown et al. 2021 ) program. While recent efforts have been made in this direction, VERTICO is the first survey probing the molecular gas content at subkiloparsec scales in a complete and statistically significant number of galaxies of the Virgo cluster.
By measuring CO in a homogeneous fashion, over a wide variety of regions, VERTICO is ideal for revealing the dependencies of local star formation efficiency (SFE) with gas density and location as a function of environmental influence for the first time. By analyzing the KS relation in cluster members, this study is in a unique position to unravel the mechanisms that regulate star formation at subkiloparsec scales inside a galaxy cluster environment. This paper is organized as follows: in Sect. 2 we describe the VERTICO observations and data reduction, while Sect. 3. 1 provides an overview of the ancillary data used to complement the VERTICO dataset. In Sect. 4 we detail the main methods to convert the observable quantities into the main physical parameters. We present our main results in Sect.
5 and provide a discussion and an interpretation in Sect. 6. Finally a short summary of our work can be found in Sect. 7. A3, page 2 of 25 Notes. The right ascension and declination of the galaxies optical centers are taken from the NASA/IPAC Extragalactic Database (https:// ned. ipac. caltech. edu/). The orientation parameters (optical inclinations and position angles) are drawn from  Brown et al. (2021) , and are calculated using fits to SDSS photometry. The average star formation rates and integrated stellar mass are adopted from  Leroy et al. (2019). HI deficiencies are adopted from  Zabel et al. (2022) , and are calculated using the predicted HI mass from field galaxies at fixed stellar mass.
Observations and data reduction We use a subsample of 37 galaxies included in the VERTICO 1  (Brown et al. 2021)  survey. The full VERTICO sample consists of a total of 51 Virgo Cluster galaxies, selected from the Very Large Array Imaging of Virgo in Atomic Gas survey (VIVA,  Chung et al. 2009)  because their environment seems to 1 https://www. verticosurvey. com/ be actively affecting them, via ram pressure stripping, starvation, and tidal interaction. In addition, these sources cover a wide range of star formation properties and span two orders of magnitude in stellar mass. Our selected subsample only excludes highly inclined (i > 80 • ) targets from the original dataset, in order to minimize any major projection effects. A summary with the basic properties of the working sample of galaxies can be found in Table  1 , orientation parameters are drawn from A3, page 3 of 25  Brown et al.
(2021) , global SFR and stellar masses are adopted from  Leroy et al. (2019) , and HI deficiencies are calculated using the predicted HI mass from field galaxies at fixed stellar mass, from  Zabel et al. (2022). Based on  Mei et al. (2007) , we employ a common distance of 16. 5 Mpc to all Virgo galaxies throughout the paper. The observations for VERTICO were carried out using the ALMA Atacama Compact Array (ACA) during Cycle 7 (2019. 1. 00763. L. ). Out of the total 51 galaxies in the sample, 37 targets were newly observed, while 15 galaxies were already surveyed and the data were publicly available in the ALMA archive  (Cramer et al. 2020; Leroy et al. 2021 ). From our selected subsample, 13 galaxies had archival data  (NGC 4254, NGC 4321, NGC 4293, NGC 4298, NGC 4424, NGC 4457, NGC 4535, NGC 4536, NGC 4548, NGC 4569, NGC 4579, NGC 4654, NGC 4694).
To recover the CO extended emission at large angular scales, Total Power (TP) observations were needed for 25 out of 37 targets. The VERTICO program targeted spectroscopic observations of the 12 CO (2-1) transition, as well as the main isotopologues 13 CO (2-1) and C 18 O (2-1) and ALMA Band 6 continuum. For each galaxy, a Nyquist-sampled mosaic was performed in order to map their molecular gas disk. The nominal flux calibration uncertainty of ALMA in Band 6 during Cycle 7 was 5-10% according to the ALMA Cycle 7 Technical Handbook. A detailed description of the data reduction and imaging process is available in  Brown et al. (2021). After imaging, two working datasets were generated by convolving each datacube to 9 resolution and 15 resolution, which correspond to physical scales of 720 pc and 1.
Ancillary data All galaxies in the VERTICO sample are well-studied Virgo galaxies with existing datasets in a wide range of wavelengths, which allows us to characterize and compare the distributions of gas, stars and recent star formation. We employ a combination of near-UV as well as near-and mid-IR photometry to derive star formation rates and stellar masses, following  Leroy et al. (2019). In particular, we use near ultra-violet λ = 231 nm data (hereafter NUV) from the Galaxy Evolution Explorer (GALEX,  Martin et al. 2005). The near and mid-IR data were taken from the Wide-field Infrared Survey Explorer (WISE,  Wright et al. 2010) , from which we use WISE bands 1 (λ = 3. 4 µm), 3 (λ = 12 µm) and 4 (λ = 22 µm).
The resolved working observables are calculated direcly from the HERACLES data products at native resolution, using the VERTICO pipeline (see  Brown et al. 2021 , for a detailed description). The HERACLES sample of galaxies is drawn from previous extragalactic surveys that provide data across different wavelengths such as The HI Nearby Galaxy Survey (THINGS,  Walter et al. 2008 ) and the Spitzer Infrared Nearby Galaxies Survey (SINGS,  Kennicutt 2003). As previously described in  Zabel et al. (2022) , these samples were constructed with galaxies following an approximately flat far-infrared (FIR) luminosity distribution. As a result, the HERACLES sample under-represents FIR-faint galaxies and it is likely biased toward galaxies with large gas mass by design.
Star formation rates Our resolved star formation rate measurements are obtained from GALEX and WISE photometry following the procedure laid out in  Leroy et al. (2019). All images are convolved from their native resolution to a common working resolution of 9 , using the  Aniano et al. (2011)  convolution kernels. All Gaia Data Release (DR) 2 stars within the image area were masked. Image backgrounds are estimated and subtracted with the Astropy Background2D function. Star formation rate maps were constructed from a combination of GALEX NUV and WISE3 photometry. The left panel of Fig. 1  shows an example of SFR map for the VERTICO galaxy NGC 4501. We use WISE3 images to quantify the obscured SFR at an angular resolution of 9 similar to the one reached by ALMA. The WISE3 band is preferred to the WISE4 band at 22 µm for its better angular resolution, although the latter is believed to be a better SFR tracer due to less contamination from polycyclic aromatic hydrocarbons (PAH) emission  (Boselli et al.