dataesr/scientific-paragraphs-categorization huggingface dataset:
QUASAR CANDIDATE SELECTION Following on from the selection method presented in  Reed et al. (2015)  (hereafter R15) we have developed a selection method that uses all the photometric data (from WISE, VHS and DES) available for the objects. The selection method incorporated the first eight steps outlined in section 3 of R15 and is summarised in Table  1. Then the candidate list was matched to the VHS catalogue data to give J and K band magnitudes for the objects and was a fast way to remove artefacts such as cosmic rays that were present in only one of the surveys. Matching to VHS and keeping only objects with YAB -JAB < 1. 0 left 960 candidates from the original 4,195 that satisfied the first stages of selection. The cuts used are shown in Figure  2  and 3. The z -Y and Y -J cuts were chosen to be the reddest cuts that excluded all known dwarf stars used in this analysis.
WISE List Driven Aperture Photometry As the W1 -W2 colour is a discriminant between quasars and cool stars, list driven aperture photometry code was run on the unWISE images  (Lang 2014). We use these unblurred coadds from the WISE Atlas images. The unWISE coadd images are 2048 × 2048 pixels at the nominal native pixel scale, 2. 75 per arcsec pixel, rather than the 4095 × 4095 images at 1. 375 per pixel chosen in the AllWISE Release. The blurred AllWISE images are better suited for source detection but the unblurred unWISE coadds used here have better resolution, and are therefore more appropriate for forced, listdriven photometry at known positions on the sky. (Lang 2014). Forced photometry was done using imcore list from version 1. 0. 26 of the CASUTOOLS software package  (Irwin 1985)  3.
Redshift Determination Redshifts were calculated by fitting a quasar model to the spectroscopic data. The section of the spectra blueward of Lyα was modelled using an exponential to account for the rapid decay to zero flux. A Gaussian centred at 1025. 7 Å was used to approximate the Lyβ emission feature seen in some of the spectra. Lyα emission was modelled using half a Gaussian which matched onto the expo-nential at 1215. 67 Å. Redward of Lyα the NV, OI and SiIV+OIV lines were added using Gaussians centered at 1240. 1, 1304. 46 and 1397. 8 Å respectively  (Tytler & Fan 1992). The section longward of 1215. 67 Å then had a power law and a constant offset added to model the continuum emission. This model was tested using the spectroscopic data from Fan et al. (2006). While the spectroscopic data presented here do not cover the full range of lines input into the model some of the test.
Our future aim is to be able to run the selection criteria over the entire input list without any need for colour cuts and using the reduced χ 2 fits as discriminators. At the moment the colour selection is required to narrow down the list enough to make the image based steps run more rapidly. Improvements in the analysis code will allow this to be done for a larger number of images more rapidly. The catalogue based steps and the fitting steps are both fast enough (10 8 sources from ∼1500 deg 2 sources in less than 24hr on a single 4Ghz core) that they will be easily expanded to the larger ∼5000 deg 2 DES dataset when it is released. In this version of the selection code objects with a high probability of being a brown dwarf are not rejected automatically but removed on an object by object basis when image cutouts of the object are checked.
An improvement to the method would be to have automatic removal of these objects, as this will be more important for larger candidate lists generated either by relaxing the colour cuts or by a larger input dataset. The confirmed quasars compared to the rest of the sample are shown in terms of reduced χ 2 to be either a star or a quasar in Figure  13. It can be seen that the selected objects are well separated from the rest of the sample. Due to the inclement weather we did not have time to follow up any objects further down the ranking so do not know if the dashed lines should be relaxed to select a complete sample. The candidates in the bottom right region are junk as confirmed by visual inspection. In Section 3. 2 our implemented SED fitting included an arbitrary systematic flux uncertainty.
Physicochemical Characterization of the Materials The homogeneity of surface hydrophobicity was investigated by water contact angle measurements using the sessile drop technique (DSA100M goniometer, Krüss, Palaiseau, France). This apparatus deposited a sterile deionized water droplet (300 pL) with a piezo dosing unit every 1 mm on an area of 10 × 20 mm 2. The droplets were monitored for 2 s using a fast CCD (Charge Coupled Device) camera with 4x zoom and a 20x microscope objective. An automatic procedure was used for drop generation, image acquisition, contact angle determination, and the mapping of sample hydrophobicity. For each material, a cartography of wettability was constructed using Matlab 2011b software (Mathworks, Natick, USA) with a color map of visible colors from blue to red representing the contact angle to water (θ) from 0 to 100 •.
Data were acquired using Xcalibur (v2. 07, Thermo Fisher Scientific) using a datadependent method comprising two steps: first, a full scan MS in an Orbitrap analyser (from 300 to 1,400 in profile mode with a resolution of 15,000 at m/z 400) and second, fragmentation and the detection of daughter ions in the linear trap (qz = 0. 22, activation time 50 ms and collision energy fixed at 35%) in centroid mode. The dynamic exclusion time was set at 45 s. To enhance mass accuracy, the lock mass option was activated on dimethylcyclosiloxan (m/z 445. 120029).
Protein Quantification The raw files produced under Xcalibur were first converted to mzXML files with MSconvert (http://sashimi. sourceforge. net, version 3. 0. 3768) and in a second step, proteins were identified using X!tandem software (X!tandem Piledriver 2015. 04. 01. 1; http://www. thegpm. org) (Craig and Beavis, 2004 ) against a P. aeruginosa PAO1 protein database (NCBInr, NC_002516. 2; 5566 sequences) associated with a standard proteomic contaminant database. The X!Tandem search parameters were (i) trypsin specificity with three missed cleavages and (ii) variable oxidation states of methionine. Semi-tryptic peptide detection was included by activating the option in the data refine mode. The mass tolerance was fixed at 10 ppm for precursor ions and 0. 5 Da for fragment ions. The final search results were filtered using a multiple threshold filter applied at the protein level and consisting of a Log10 protein E-value lower than -2.
6 identified with a minimum of two different peptide sequences, detected in at least one analysis as having a peptide E-value lower than 0. 05. These criteria led to false discovery rates estimated using the decoy database of 0. 07 and 0. 10% for peptide and protein identification, respectively. X!TandemPipeline 3. 3. 5 analysis  (Langella et al. , 2017)  (http:// pappso. inra. fr/bioinfo/xtandempipeline/) was used to filter and group the peptide/protein identifications from MS/MS mass spectra. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Vizcaíno et al. , 2016)  partner repository with the dataset identifier PXD006881 and 10. 6019/PXD006881. PRIDE Converter 2 was used to convert our data in PRIDE-xml format  (Côté et al. , 2012).
Statistical Analyses Physicochemical results (contact angles and roughness) were considered to be significantly variable using ANOVA when the p-value was <0. 05. A spectral counting approach was used to identify proteins detected in statistically different quantities between the conditions. Data from the mass spectrometry protein quantification of five biological replicates were compared. First, all conditions were compared (70 proteins significantly different) and second, the conditions were compared between each other and the results were merged. Filtering and statistical analysis were performed using MassChroqR, an R package developed by PAPPSO (http:// pappso. inra. fr/). A small difference in the number of spectra may be noise-induced, so proteins displaying a minor variation between the conditions (whose mean difference was less than three spectra) were discarded from the analysis.
Heat Map Representation Heat maps were generated under MassChroqR using the heatplot function from the made4 package (http://www. bioconductor. org). The heat map revealed a gradient of colors assigned to the different numbers of spectra (centered and reduced) and the dendrogram provided a graphical representation of a hierarchical cluster with distances calculated using the Pearson correlation coefficient (default distfun argument of the heatplot function of the "made4" of the R package); the aggregation criterion was the average-linkage or UPGMA (Unweighted Pair Group Method with Arithmetic mean).
Materials Impact the Adhesion Capability of P. aeruginosa Bacteria were allowed to adhere to these three surfaces for 3 h before being recovered and observed or counted. Figure  2  shows that 8. 4 × 10 6 (± 1. 3 × 10 6 ) CFU/cm 2 , 2. 2 × 10 6 (± 0. 9 × 10 6 ) CFU/cm 2 , and 6 × 10 5 (± 2. 5 × 10 5 ) CFU/cm 2 (P<0. 05) were detached from SS, G, and PS, respectively, indicating that the material had an impact on the number of adherent cells. Direct epifluorescence microscopic observations of the contaminated materials (Figure  2 ) were correlated to the enumeration results by showing a higher coverage rate of bacteria on SS (31%), an intermediate rate on G (11%), and a lower rate on PS (2. 6%). Taken together, these data suggest that P. aeruginosa cells were more likely able to adhere to SS and G rather than PS surfaces under our experimental conditions.
Among the 834 proteins identified on these two materials (Figure  3 ), 57 proteins showed significant differences in their abundances: four were specifically present in this pair  comparison, 8 in SS vs. G and 7 in PS vs. G. By contrast, comparing SS vs. G and PS vs. G led to 21 and 39 differentially abundant proteins (Table  S4 ). Taken together, these data showed a strong bacterial response that was specific to the material used for adhesion. The data were then subjected to hierarchical clustering which focused on the 70 proteins that displayed differential content following adhesion on at least two materials. The abundance of these 70 proteins was compared using the normalized quantities of spectral counting from five biological replicates (Figure  4 , Table  S4 ). Protein levels were represented as a color map ranging from red to blue, indicating proteins produced at lower and higher levels, respectively.
ru. In June 2016 top editors of the RBC news group (which includes a television channel, a newspaper, and a news website) were ousted after they published inquiries on the assets of the country's main leaders. This recent case is in keeping with those described in this article. 4 Konstantin Benyumov, "How Russia's Independent Media Was Dismantled Piece by Piece," Guardian, May 25, 2016 (http://www. theguardian. com/world/2016/may/25/how-russia-independent-media-was-dismantled-piece-by-piece). We have gathered empirical data on the arguments put forward during disputes from the press and interviews with the journalists concerned. We encountered difficulty in scheduling interviews with chief editors, and when the latter were available, they were pressed for time. Our method was then to systematically combine these interviews with anonymized interviews of some of their coworkers with whom we met several times to obtain more in-depth information.
However, our research was hampered by the fact that Russia has no unified database on the press analogous to France's Europresse or Factiva. We had no other choice but to perform the painstaking work of manually collecting opinion statements on these terminations from several websites. The collected material provided information complementing and adding to the interviews and formed a basis for more precise wording of the questions of our interview guides. Last, as concerns the third termination case, we used a commemorative collection of eyewitness accounts posted on Lenta. ru by journalists who had left the online publication. Some of the most committed journalists opposing the political regime are indeed intent on producing retrospectives of these independent outlets and their histories.
A great advance for the study of mechanochemical dynamics has been the development of real-time in situ (RI) monitoring by synchrotron X-ray powder diffraction (XRPD),  [17, 18]  complemented by Raman spectroscopy. [19, 20] These developments offer significant advantages over conventional stop-start Mechanochemical methods offer unprecedented academic and industrial opportunities for solvent-free synthesis of novel materials. The need to study mechanochemical mechanisms is growing, and has led to the development of real-time in situ X-ray powder diffraction techniques (RI-XRPD). However, despite the power of RI-XRPD methods, there remain immense challenges. In the present contribution, many of these challenges are highlighted, and their effect on the interpretation of RI-XRPD data considered.
Experimental Section In Situ Milling: Real time in situ milling experiments were conducted at the European Synchrotron Radiation Facility (ESRF), beam line ID11, experiment CH4313. Ball milling was done in a modified MM400 Retsch mill. For each reaction, 300 mg of stoichiometric mixture of oxalic acid dihydrate and glycine was used. Perspex milling jars (14. 5 mL) were used  [34]  with a single stainless-steel ball (7 mm diameter). Monochromatic X-ray of wavelength 0. 141696 Å was used, and powder patterns were collected every 0. 4 s. Data were averaged by summing 10 detector frames, giving a total time resolution of 4 s. Integration of 2D data was performed using the PyFAI azimuthal integration methodology. Hybrid-Methodology: To ensure sufficient sampling across the time domain, 20 integrated powder diffraction patterns were Rietveld refined using GSAS,  [35, 36]  Figure  S1.
4. 1. 1 (Supporting Information), and quantitative phase information extracted, Figure  S1. 4. 1. 2 (Supporting Information). Patterns were selected to capture key phase evolutions throughout the process. It is noted that the refined quantities of G 2 O are within the error limits of the Rietveld refinement (<3 wt%), and its resulting dynamics profile is therefore an upper estimate of its phase composition throughout the process. Integrated data were subsequently background corrected using the Sonneveld-Visser algorithm  [37]  in Powder3D. [38] All patterns were numerically normalized to unity, in order to account for stochastic fluctuations in the quantity of diffracting sample, and the major peak of each phase integrated; both procedures performed using a custom-designed program. Integration was performed using a trapezoidal algorithm on an evenly spaced grid.
Acknowledgements The authors thank  Dr. B. Zakharov  for useful discussions and  R. Clark  for assistance with ARR. The authors thank  Russian Ministry of Science & Education , Project No. 1828  (A. A. L. M. ), an  Edinburgh Global Research Scholarship  (A. A. L. M. ),  RFBR   16-33-60086 (I. A. T. ) , and  SB RAS , Project  0301-2014-0002  (E. V. B. ). A. A. L. M and C. R. P. also thank  EPSRC EP/I033459/1  ( EPSRC CMAC)  for funding. Final thanks to  Prof. V. V. Boldyrev , the founding President of the  International Mechanochemical Association , for on-going support and countless fruitful discussions. All of the experiments in this work were performed at  ESRF ID11 (CH-4313 ).
2  up to 500 MPa i. e. 5000 bars) is not satisfied anymore. To achieve this purpose, the fugacity will be redefined in order ensure an accurate description of hydrogen based on thermodynamic data of hydrogen. The resulting EOS will then be compared to the original AbeleNoble EOS and applied to evaluate the pressure built up within the cavity. Considering the definition of Gibbs energy, a differential relation (  5 ) between state quantities can be expressed as follows (where V is the considered volume of gas, n the amount of H 2 , m the chemical potential and s the entropy of the system): dG ¼ Vdp À sdT þ mdn (5) Since the derivatives are continuous, Schwarz theorem leads to symmetric second derivatives (6): vV vn P;T ¼ vm vp T;n (6) By using the general expression of EOS, the dependence of chemical potential on pressure can be quantified (where v represents the molar volume of hydrogen and r its inverse)  (7) : vm vp T;n ¼ ZRT p ¼ v ¼ 1 r ( 7 ) This parameter is then integrated (8) between a standard state, noted E 0 , and any eligible state designated by E: Z E E 0 dm ¼ Z p p 0 1 rðpÞ dp (8) The chemical potential is then evaluated knowing the pressure evolution of hydrogen density as a function of its pressure.
Otherwise, the fugacity of a gas is defined as its thermodynamic activity. The fugacity is therefore linked to a chemical potential defined with respect to a reference which is in this case the standard state and can be expressed by (  9 ): m ¼ RT lnðf Þ (9) By combining both expressions of the chemical potential (  8 ) and (  9 ), the real gas fugacity can be defined as a function of the pressure (10): f ¼ f 0 exp 0 B @ 1 RT Z p p 0 1 rðpÞ dp 1 C A ( 10 ) The advantage of this approach is that no prior assumptions on the type of EOS have been done. As a consequence, this expression of fugacity may be directly linked to available thermodynamic data or experimental measurement in order to best fit the real behavior of gaseous hydrogen at any pressure but for temperatures above 200 K (above this threshold temperature, there is no effect of cohesion pressure parameter a).
Thermodynamic data of gaseous hydrogen The National Institute of Standards and Technology (NIST) have gathered thermodynamic data concerning hydrogen in order to build a comprehensive database containing relevant isothermal and isobaric properties of various entities, including hydrogen. In the considered case, the parameters of interest are the isothermal and isobaric density of the gas. Fig. 4-a ) provides the inputs to evaluate hydrogen fugacity at room temperature in the whole pressure range (the hydrogen density is then to be integrated by using the previous expression). Fig. 4-b ) compares the fugacity resulting from Fig. 2  e Compressibility factor provided by 3 models of gas: ideal gas, AbeleNoble and Van der Walls. This figure clearly underlines that the ideal gas corresponds to the limit case of very low pressures.
It also puts in evidence that for high pressures, the ideal gas model cannot be used and both Van der Walls and AbeleNoble equation of states provide equivalent results. Fig. 3  e Relation between hydrogen fugacity and pressure considered as an AbeleNoble real gas at room temperature. AbeleNoble, Van-der-Waals EOS with the data issued from NIST database taken as reference. This figure points out that the cited EOS is valid for pressures up to approximately 3000 bar. Beyond this pressure, the compressibility factor can no longer be considered as linearly dependent on pressure. As a consequence, for a given value of fugacity given by the Sievert's equilibrium an increase of pressure within the cavity is expected as compared to both AbeleNoble and Van-der-Waals EOS. These observations provide therefore the motivation to refine the description of gaseous hydrogen considered as a real gas for high pressures, beyond 300 MPa.
Expression of molar co-volume To determine an accurate description of the pressure and temperature dependence of the molar co-volume b, the identification of the molar volume v is first needed. The preliminary identification of this parameter will be done using the data from NIST database. Fig. 5a  presents the evolution of the pv parameter between 0 C and 750 C for 5 pressures from 0. 8 kbar up to 20 kbar. The evolution is clearly linear. In addition, the curves are all parallel which means that the temperature dependence is pressure independent. Then, the pv factor is assumed to be properly fit by  (11)  (where a is a constant and B a function of the pressure): pv ¼ aT þ BðpÞ (11) In parallel, Fig. 5b  presents the pressure dependence of pv for two temperatures (i. e. standard and normal temperatures, respectively 0 C and 20 C).
These two cases are schematized on Fig. 8. The relevance of the refined EOS presented in the first part of the paper will then be highlighted by comparing results issued from the regular AbeleNoble law. The differences between the inputs parameters considered in the following simulations are synthetized by Table  1. In order to conduct these simulations, the diffusive properties of the heterogeneous alloy and related data have been taken from Ref. [26]. Based on process guideline of such components, a 30 C h À1 cooling rate was applied and the initial amount of hydrogen was taken to be equal to 1 wt ppm corresponding to the maximal admissible concentration of hydrogen usually encountered during such processes. Hydrogen outgassing from the matrix to the cavity: effect of the refined EOS on the equilibrium pressure Two calculations using a 5 cm simulation box have been performed to emphasize the effect of the refined EOS on the equilibrium pressure in the cavity.
Conclusion A refined EOS based on AbeleNoble model has been proposed to accurately evaluate the equilibrium pressure within internal cavities up to 20 kbar. This alternative approach is based on the fugacity pressure dependence deduced from NIST database, valid up to 20,000 bar and for temperatures above 200 K. The quadratic fugacity-pressure dependence results in a decreased fugacity at high pressure as compared to the reference AbeleNoble description. This new EOS for hydrogen is completely based on NIST thermodynamic datasets. The updated fugacity-pressure dependence implies a decreased chemical activity of gaseous hydrogen for extreme pressures, and consequently, for a given fugacity, resulting from Sievert's equilibrium; the corresponding pressure is higher than that calculated from that AbeleNoble EOS.
Calibrating the shear measurements of Euclid could require an appreciable fraction of the HST archive to statistically account for the impact of color gradients in galaxies  (Voigt et al. 2012; Semboloni et al. 2013). Calibrating shear measurements for LSST will require a similar data set, like the Euclid imaging or the Euclid shear measurements, especially from the Euclid deep fields, which will be of comparable depth to the LSST lensing sample. Calibrating multiplicative bias in shear measurements can potentially be done via external measurements of the lensing of the cosmic microwave background, or possibly LSST supernovae (but only at the level of a few percent;  Schaan et al. 2017 ). This would be sufficient for some, but not all, of the tomographic redshift bins of Euclid and LSST; the development of robust shape measurement methods will need to continue in order for both surveys to reach their shape measurement systematics requirements.
Cluster Mass Estimates LSST and Euclid will constrain cosmological parameters using galaxy clusters, primarily measuring the shape and evolution of the halo mass function. To harness the power of this approach will require a precise and accurate cluster mass calibration  (Dodelson et al. 2016). Cluster weak lensing measurements are clearly the best opportunity to deliver the required mass estimates for ensembles of clusters  (Hoekstra et al. 2015; Mantz et al. 2015; Krause & Eifler 2017). The combination of Euclid and LSST data will also help with cluster detection. For accurate cluster masses from weak lensing, it is essential to separate the lensed background population from foreground galaxies, especially cluster galaxies. The combination of LSST and Euclid provides more robust photometric redshift estimates, allowing one to minimize the roles of external priors and calibration.