dataesr/scientific-paragraphs-categorization huggingface dataset:
Determination of the praseodymium oxidation state in Pr2NiO4+δ The XANES data collected under air for Pr2NiO4+δ in the temperature range 25°C < T < 700°C are plotted in Figure  2. The differences between the various curves cannot be easily distinguished because they are quite perfectly superimposed. The XANES spectra for the praseodymium standards recorded at room temperature in air are also shown for comparison. XANES data collected at the LIII-edge of praseodymium, for PrF3 and Pr0. 5Ce0. 5O2 at room temperature in air. For Pr2NiO4+δ, the spectra were collected in the temperature range 25 -700°C in 20% O2 diluted in helium. The Pr-LIII XANES spectrum recorded for PrF3 has a unique absorption peak characteristic of Pr 3+ cations. On the contrary, the corresponding spectrum related to Pr0.
Moreover, the thermal evolution of the nickel K-edge in Pr2NiO4+δ is in good agreement with that previously determined for La2NiO4+δ by Park et al. [32]. These authors also observed a slight shift of this edge towards higher values when the δ value increases, i. e. when the mean nickel oxidation state increases. Numerical assessments were performed by evaluating the position of the maximum of the first derivative of the XANES curve (edge shift) or considering the area under the curves (integral). The second method has the benefit to be less sensitive to the energy sampling and it is particularly sensitive to the presence of isosbestic or pseudo-isosbestic points. In a second step, the first way to evaluate the average oxidation state consists in interpolating the previous values obtained at a given temperature to the straight line passing from the data recorded on model compounds (Ni 2+ (NiO) and Ni 3+ (LaNiO3)).
Edge Assuming the absence of Pr 4+ cations, as shown above, and according to the modeling reported by Minervini et al. [33] , we used the following charge compensation equilibrium for calculating the oxidation state of Ni: O 2-+ 2Ni 3+ ↔ ½ O2 + 2 Ni 2+ (1) The experimental TGA data recorded in air  [22]  are plotted in Fig. 4  (-right axis, red curve). The corresponding mean nickel oxidation state of nickel, deduced from equilibrium (1), is reported on the left axis of Fig. 4. Both set of data (XANES and TGA), which have been independently determined, can be compared. The mean oxidation state slightly decreases when the temperature increases for T > ~ 400°C (Fig. 5 , left axis). However the two curves (one relating to XANES data, the other relating to TGA data) exhibit a difference in shape; they only agree at low temperatures (T ≤ 300 °C) with a good agreement only at RT and at 800 °C.
However such a difference should be expected due the different kinetics of the two experiments. Indeed, for both techniques a similar heating rate has been used but in the in-situ XANES the Pr2NiO4+δ powder was mixed with BN and compressed in self-supporting pellets, consequently at low temperatures the transformation kinetics was not only limited by oxygen conductivity but also by the gas diffusion in the pellets, while at high temperature this gas diffusion limitation becomes negligible. Finally, even if partly overestimating the absolute mean oxidation state of nickel, the XANES data recorded at the Ni-K edge evidence its progressive decrease upon heating, in rather good agreement with the TGA evolution. A significant increase of the nickel mean oxidation state and the corresponding oxygen composition 4+δ is observed at the Low Temperature Orthorhombic (LTO) High Temperature Tetragonal (HTT) phase transition (T ≈ 400 °C) (Fig.
In this study we are focusing on the retrieval of cirrus cloud properties. Thus, the backscatter-to-extinction coefficient for aerosols is assumed to be constant and is fixed to 64 sr. This value originates from the Optical Properties of Aerosols and Clouds (OPAC) database  (Hess et al. , 1998b)  and corresponds to a water-soluble urban aerosol. Since our measurement site is located in an urban/industrial area, we used the optical properties for this aerosol type (for the visible lidar wavelength as well as for the TIR as will be discussed in Sect. 4. 1) from the OPAC database to test our new algorithm. This parameter needs to be refined in future studies depending on the aerosol type that is actually present during the measurement obtained from additional information. However, as discussed above for the multiple-scattering factor, we also assign a large uncertainty to the lidar ratio of aerosols to account for our rather poor knowledge of it.
They created a database of optical ice cloud properties comprising 145 wavelengths between 0. 2 and 120 µm. This database was used by  Vidot et al. (2015)  to develop a new ice cloud parametrization that predicts the single-scattering properties named above as functions of the in-cloud temperature and IWC without the need of a priori information on the shape and the effective diameter of the ice crystals. For the remainder of this paper, we will call this microphysical model BV2015. Since our algorithm seeks to retrieve the IWC for cirrus cloud layers, the extinction required in the lidar equation (Eq. 1) is calculated from the scattering and absorption coefficients obtained from the BV2015 parametrization as a function of the IWC of each cloud layer. The necessary temperature information is obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis by matching atmospheric temperature profiles to the corresponding cirrus cloud altitude.
When the cloud is optically very thin, the signal in the TIR radiometer measurements is very small, resulting in a large uncertainty in the retrieval, which seems to be rather logical. However, it is important to keep in mind that the results depend on several factors. All retrievals shown in this study were performed for a multiple-scattering factor of η = 0. 75 for ice clouds, and the backscatter-to-extinction ratio for aerosols was fixed to the value for an urban water-soluble aerosol from the OPAC database. Changing those values may change the retrievals, and further sensitivity studies with our algorithm are necessary to evaluate the effects of (1) a varying multiple-scattering factor and (2) using other aerosol models. Furthermore, when regarding ground-based TIR radiometer measurements, a good characterization of the surrounding atmosphere, especially the water vapor profile, is crucial since the TIR radiances are very sensitive to water vapor, which is spatially and temporally highly variable.
Acknowledgements. The authors thank the  Région Hauts-de-France , the  Ministère de l'Enseignement Supérieur et de la Recherche  ( CPER Climibio ) and the  European Fund for Regional Economic Development  for their financial support. The authors thank the  CaPPA project (Chemical and Physical Properties of the Atmosphere ) funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract  ANR-11-LABX-0005-01  and by the  Regional Council "Hauts-de-France " and the  European Funds for Regional Economic Development (FEDER). The ACTRIS-FR research infrastructure is acknowledged for financial support. Edited by: Alexander Kokhanovsky Reviewed by: two anonymous referees.
-Social networks: Four Samples of the Facebook Network are used. The ego-Facebook network collected from survey participants using the Facebook app. 1   [27]  and the Facebook friendship network at 3 US universities (Caltech, Princeton, Georgetown) collected by Traud et al  [28]. Nodes represent individuals (survey participant or members of the University), and edges represent online friendship links between two individuals. In the University network, in order to obtain data that are relevant for the spread of epidemic infections, only the relationship of individuals who live in the same dormitory or study the same major are considered. -Communication network: The Email-Eu-core 1 network has been generated using email data from a large European research institution. The dataset contains only communication between institution members.
The epidemic threshold values α th for the networks used in this study are reported in Table  3. In all the experiments, we use the same value of the transmission rate (α = 0. 1). Naturally, it is much larger than the values of the epidemic threshold α th of all the dataset. The value of the recover probability is also constant (σ = 0. 1). We choose this small value so that each infected node may have many chances to infect its neighbors with the probability α before changing to the recovered status. The process continues until there is no more infected node in the network. Finally, when the spreading process stops, the number of nodes in the state "Recovered" R is used to measure the spreading efficiency of the fraction of the initially infected nodes. The larger the value of R, the more influential the initially selected nodes.
Equipment and LC-MS/MS conditions The mass spectrometer devices consisted of a Dionex HPLC system controlled with Chromeleon® software and with an Endura (Thermo Fisher Scientific, San Jose, California) triple quadrupole mass spectrometer controlled with TSQ Tune Application® Software. The quantitative analysis was performed using the Tracefinder® software. A Heated Electrospray Ionization Source (HESI) in positive mode was used with the following parameters: ionization voltage set at 4000 V, vaporizer temperature at 250°C, capillary temperature at 250°C, sheath gas flow at 35 arbitrary units. The used transitions are reported in Table  3. The liquid chromatography was performed in isocratic condition with the following elution solvent at room temperature and a flow rate of 500 µl/min for 0.
5 min and then 300 µl/min for 2 min (92% V/V of acetonitrile and 8% of water in 0. 1% formic acid). Separation was performed on Zorbax Bonus-RP analytical column (150 x 2. 1 mm, 1. 8 µm) placed in a thermostated column heater at 50°C. The run time is 2. 5 min. Data acquisition was performed using Tracefinder® software. The ion values used for the method for each compound are described in the Table  3.
Each measurement was averaged over five data points at different positions (four in the corners of the window and one in the center). The laboratory measurement of the haze was performed on small samples (4 × 9 cm 2 ) after in-field/laboratory transfer for each measurement. Haze value is according to ASTM D1003 defined as the ratio of diffuse to total luminous transmittance (DT/TT %). Luminous transmittance is weighted with regard to the relative sensitivity of the human eye in the photopic state. DT and TT were measured on a Perkin Elmer Lambda 900 UV-VIS spectrometer (Waltham, MA, USA) that is equipped with an integrating sphere. Both transmittances were measured in the 380-780 nm range using 10 • CIE standard observer and CIE standard illuminant A. Transmittance spectra were recorded at 23 ± 2 • C.
, 2016)  deeper than 900 m (1200 m for C. angulatus). Reproduction also impacts crab distribution through aggregating behaviors of adults in late winter  (Pereyra, 1966; Workman et al. , 2001). Chionoecetes crabs are opportunistic and feed on benthic invertebrates: mostly amphipods, copepods, smaller juvenile Chionoecetes, ophiuroids, clams, worms and detritus  (Phillips and Lauzier, 1997). Since 2009, the Ocean Networks Canada (ONC) observatory has provided continuous interdisciplinary data at various locations off Vancouver Island (British Columbia, Canada), from coastal areas to the abyssal plain. One of ONC's research themes focuses on the study of bentho-pelagic coupling in Barkley Canyon, a mid-slope submarine canyon located in the Northeast Oxygen Minimum Zone (dissolved oxygen <0. 5 mL/L).
We determined the overlapping component distribution that gives the best fit to the histogram with the "mixdist" package  (Macdonald and Pitcher, 1979; Macdonald and Du, 2012 ) in R  (RStudio Team, 2016). Measurements made on juveniles and sub-adults sampled in Oregon (United States) FIGURE 3 | An example of a snapshot taken at 985 m depth at the axis (pod 1) in Barkley Canyon (British Columbia, Canada) combined with the perspective grid (each square = 10 cm × 10 cm). The red square delimits the area where measurements were made with sufficient precision. The purple line represents a 10-cm vector and the yellow line the measurement of carapace width. (Tester, 1975)  and British Columbia (Canada)  (Gillespie et al. , 2004)  revealed nine consistent modes between 1 and 10. 5 cm (Table  3 ). Input values in the function MIX can be used to test the validity of certain constraint parameters, here, the mean size for each cohort based on findings reported in the literature.
Thus, we pooled the first three cohorts (i. e. , cohort 1, 2, and 3 in Table  3 ) as a single group, labeled cohort 1 thereafter. We computed the density time-series of each cohort and environmental factors with a Whittaker-Robinson (WR) periodogram to reveal potential periodicities. In the WRperiodogram analysis, time series are folded into Buys-Ballot tables with periods of 2 to a maximum of n/2, where n is the total    (Tester, 1975)  and British Columbia (Canada) between 1999 and 2003  (Gillespie et al. , 2004). number of observations. Amplitude is the underlying statistic used in this periodogram function and equals the standard deviation of the means of the columns of the Buys-Ballot table  (Legendre and Legendre, 2012). The WR periodogram can handle missing values in the datasets. Prior to periodogram analyses, trends were removed after linear regression and residuals were used in the periodogram analysis.
Literature To identify potential groups of seasons/years in the local population, we used Principal Component Analysis to visualize the distribution of individual samples (i. e. , densities of measured crabs for each observation date) on the first two principal axes. PCA was performed on Hellinger-transformed density data, preserving the Hellinger distance which is appropriate in Euclidean -based multivariate methods. This transformation also mitigates the importance of zero-inflated data (i. e. , to avoid inferring similarity between sites from double absences)  (Legendre and Gallagher, 2001). This analysis suffered from a horseshoe distortion (Guttman effect) characterized as a onedimensional ecological gradient that bent into an arch in axis 1 -axis 2 space. This type of distortion limits interpretation and is often due to the progressive change of samples along an environmental gradient  (Legendre and Legendre, 2012).
As an alternative method, we computed extended dissimilarities  (De'ath, 1999) , allowing us to generate an ordination where the sites are ordered more linearly along the gradient. The step-across function in vegan was used to transform the dissimilarity matrix on which we applied a Principal Coordinate Analysis (PCoA)  (Legendre and Legendre, 2012). All statistical analyses were performed with R statistical software 3. 3. 2 (RStudio Team, 2016).
Depending on the orientation of the camera and variations in total surface area across the different recording periods (Table  1 ), the proportion of enumerated crabs that could be measured varied over time. From June 2012 and February 2014, 35-63% of the counted crabs on the images could be measured. Between August 2014 and January 2015 this proportion dropped to 18%. A total of 4,985 crabs were measured at the axis (pod 1). No crabs were measured at the wall because of low video quality. However, even if a temporal approach was not possible, with the addition of data from a ROV video transect, we were able to identify adults, subadults (i. e. , immature) and juveniles of C. tanneri at the wall (Figure  2 ). At the axis site, the mean carapace width (CW) of individuals in the local crab population was 2.
ACKNOWLEDGMENTS We are grateful to all  PIs of ONC  for accessing their temporal data. We especially thanks  Dr. F. De Leo  and  S. Mihaly  for their help during the 2016 cruises. We are also very thankful to  Dr. Greg Workman ,  Dr. Aimee Keller , and the survey team who helped to identify taxa. Karen Douglas  provided the bathymetry data used by  Dr. Ronan Autret  to build the map of  Barkley Canyon.
Statistical analysis The software Curve Expert 1. 3 was used to make a standard curve for measuring protein concentration. Differences among the experimental groups were analyzed by one-way analysis of variance (p Ͻ 0. 05 and p Ͻ 0. 01). Values were considered to differ significantly when p Ͻ 0. 05. All data are expressed as the mean Ϯ 1 S. D. All experiments were repeated at least three times.
, 2015) may explain the differences in the pangenome content (6602 vs. 5426 genes). In contrast, the Salmonella enterica pan-genome has a higher gene number but smaller core genome than the S. Typhi isolates in this study. This difference is to be expected, as the Salmonella enterica dataset included multiple serovars  [36]. Interestingly, our S. Typhi pan-genome contains a higher number of genes than S. Paratyphi-A (4670; 41 isolates) and S. Enteritidis (4750; 159 isolates), but a smaller number of genes than S. Typhimurium (7603; 47 isolates;  [37]. These findings corroborate the hypothesis that the pan-genome size does not reflect the host range or ability to colonize multiple hosts  [37, 38]. However, the accessory genome or serovar-specific core genome could be responsible for such host variety  (Seif et al.
Moreover, 40% of class L (DNA repair) genes in the dispensable genome were located in prophage regions. Prophage-like elements in Salmonella serovars can play a role in recombination, contribute to virulence in the host and carry specific virulence-associated genes such as sopE. In the case of S. Typhi, these regions could even cause more subtle intra-serovar variation (Boyd and Brüssow 2002;  Thomson et al. 2004 ). These prophage regions have also been reported to contribute to mechanisms of DNA repair, possibly as a part of the bacterial SOS regulatory system (Balbontín et al. 2006). Our core S. Typhi genome lacked a number of COG classes, including Y (genes related to nuclear structure), B (Chromatin structure and dynamics) and Z (Cytoskeleton). This result could be an artifact of COG class annotation, as the current version of the COG database does not include in their classes bacterial genes related to chromatin-like organization or cytoskeletal formation, even though there are only but a few genes on those related functions for bacteria.
Brothmicrodilution was employed to determine the minimum inhibitory concentrations (MIC) for ciprofloxacin (cip) and ceftriaxone (cro) (Sigma Aldrich, St. Louis, MO, USA). All zone diameters and MIC data were interpreted according to EUCAST v8, 2018 ([67], 2018). Metadata for all 73 isolates (sample, organism, year of isolation, setting, patient age [months] and sex) are presented in Additional file 4: Table  S1.
This curated superscaffold was subjected to gap filling in two steps. In the first step, we used GapFiller v1. 11  [74]. In step 2, remaining intraand inter-scaffold gaps that were not closed by GapFiller v1. 11 were locally assembled using Newbler v3. 0. Reads present at both ends of a gap were selected and assembled. Contigs obtained this way and that spanned the gap and anchored on consensus sequences on both sides were added to the consensus, thus filling the gap. Step 2 was done manually to each remaining gap from step 1 and subjected to confirmation of each closed gap to avoid erroneous gap filling. Scaffolds that did not align to the chromosome of S. Typhi CT18 were aligned to the nt NCBI database in order to verify if they had plasmid origin. Those with plasmid origin were assembled using the approach described previously.
Visual aid to the assembly process can be found in the Additional file 5: Figure  S2. A similar approach was previously used to close a Klebsiella genomes  [75]. The SABIA pipeline was used for automated annotation  [76]. Assembled sequences were submitted to the NCBI (accession IDs are shown in Additional file 4: Table  S1 ). During the superscaffold formation step, the presence of a Salmonella genomic island (SGI) was noticed in 21 strains, due to careful manual curation of the superscaffold formation step. This island carries several resistance genes and is not present in the reference S. Typhi genome. Based on sequence similarity and gene content, the SGI was identified as Salmonella genomic island 11 ([SGI11] accession number KM023773;  [15]. According to Chiou et al. [15] , SGI11 can be located at two chromosomal positions, interrupting the yidA gene or nlpC gene.
Contigs were aligned against the SGI11, nlpC and yidA sequences to determine the presence of the island and its position of insertion. For strains in which there was evidence of a SGI11 element but neither yidA nor nlpC were interrupted, the extremities of the contigs harboring the segments of the island were locally assembled and the gene neighborhood was determined after annotation. To observe conserved synteny blocks, we aligned all isolates and S. Typhi CT18 using Progressive Mauve  [77]. We used RPS-BLAST, a variant of PSI-BLAST  [78]  to identify proteins with homology to COG protein profiles in the NCBI database at an e-value of 0. 001. The best hit was selected and COG cluster classification was transferred to the protein query. Pan and core genome analysis was performed using PGAP v1.
2. 1  [79]. Coding sequences, protein sequences and COG classification for all isolates were used as input, along with an e-value of 1e-10 at 70% identity and similarity. All protein sequences were aligned against each other using blastall and the resulting output was imported into MCL  [80] , a part of PGAP, to cluster the genes. After clustering, PGAP computes the pan and core genomes by strain combination from 1 to n strains, where n denotes the maximum number of strains (n = 73 in this study). Heap's law and an exponential law were employed to fit the pan and core genomes, respectively  [79]. PanGP was used to plot the pan and core genome curves  [81]. After assembly and annotation of the chromosomes and plasmids, AMR genes on the SGI11 and plasmids were manually annotated. We also used Abricate  [82]  to corroborate the AMR genes using the following databases: Resfinder, ARG-ANNOT, CARD, NCBI Bacterial Antimicrobial Resistance Reference Gene Database, EcOH, PlasmidFinder, VFDB and Ecoli_VF.
Measurements: Standard descriptive analysis. Logistic multiple regression analysis tested: PE; pregnancy-induced hypertension; preterm delivery; small for gestational age; explicatory variables: age; BMI; parity; comorbidity (kidney diseases; immunologic diseases; thyroid diseases; other). Delivery over time was analyzed according to Kaplan Meier; ROC (Relative Operating Characteristic) curves were tested for PE and pre-term delivery, employing serum creatinine and e-GFR as continuous variables. The analysis was performed with SPSS v. 14. 0 and MedCalc v. 18. Results: In keeping with ED indications, maternal age was high (44 years). Comorbidity was common: at least one potential comorbid factor was found in about 40% of the cases (kidney disease: 3. 7%, immunologic 6. 4%, thyroid disease 18.
Egg-Donation The selection of the cases of pregnancy in women undergoing egg-donation included all patients who delivered at the Sant'Anna Hospital between January 2008 and February 2019. Pregnancies from egg-donation were identified in two main sources: the first was the hospital's discharge database in which medical history, birth data and discharge letters are recorded. The search was carried out using the Italian keywords related to "egg-donation" in various combinations (e. g. , "ovodonazione, ovodonazioni, ED eterologa, OD, fecondazione eterologa"). Secondly, we searched the computerised records of pregnant women referred to the Prenatal Screening Center at the Sant' Anna Hospital, where information on type of conception is mandatorily recorded (egg-donation, other types of assisted fertilisation techniques, spontaneous pregnancy).