dataesr/scientific-paragraphs-categorization huggingface dataset:
K drag of Nafion in acid form In the case of Nafion in acid form, when the water content of Nafion is between 12-22 molecules of water per sulfonate site, we found an almost constant electro-osmotic drag coefficient value (maximum around 1. 6). At high water content (>12), this electro-osmotic drag coefficient value appears small in comparison with the data from the literature  [9, 10, 15]. Indeed, most of the authors found values higher than 2. However, the conditions of measurement and the experimental methods are different. Usually, a high electro-osmotic drag coefficient is measured when Nafion membrane is kept in contact with liquid water or an electrolyte solution. Ise et al. used ENMR to measure the electro-osmotic drag coefficients of Nafion and PEEKs membranes. They used a home-made cell with a thermoregulation system.
Samples and high-energy X-ray scattering data collection for Rietveld and RMC/DSE analysis The schwertmannite reciprocal-and real-space X-ray diffraction data were the same as used for the  Fernandez-Martinez et al. (2010)  study. Both natural and synthetic samples were used. In brief, natural samples were taken as fresh precipitates from the acid drainage in the Monte Romero mine (Huelva, Spain). Sample Nat-Air was air-dried, while sample Nat-Freeze was freeze-dried. Sample SynHT was synthesized by dissolving Fe 2 (SO 4 ) 3 in deionized water at 358 K for 1 h using the procedure described by  Loan et al. (2004). The precipitate was vacuum filtered and freeze-dried to complete dryness using a VirTis Benchtop freeze-dryer (Hucoa-Erlo ¨ss). Sample Syn was synthesized at 333 K from ferric chloride and sodium sulfate solutions following the methods reported by  Schwertmann & Cornell (1991).
High-energy X-ray diffraction measurements were carried out at beamline ID15B at the European Synchrotron Radiation Facility using monochromatic X-rays with an energy of $87 keV (0. 1419 A ˚) in Debye-Scherrer geometry. Samples were loaded into 0. 8 mm-diameter polyimide capillaries that were sealed with wax. The beamline was calibrated using a CeO 2 standard (NIST 679b) for the data collection of the sample SynHT and using an Ni standard for the data collection of samples Syn, Nat-Air and Nat-Freeze. An LaB 6 standard was also measured to determine instrumental resolution effects. The diffraction patterns were collected using a MARCCD165 two-dimensional (sample SynHT) and a Pixium 4700 (samples Syn, Nat-Air and Nat-Freeze) detector. Measurements of the samples, the empty capillary and the background were performed at ambient temperature in a Q range of 1-25 A ˚À1.
XAS experiments X-ray absorption near-edge structure (XANES) experiments were performed at the BOREAS (BL29) beamline of the ALBA synchrotron light source  (Barla et al. , 2016). X-ray photon-energy scans from 2450 to 2510 eV were taken across the sulfur K-edge region at an energy resolution estimated at 0. 15 eV (1200 lines per mm grating, 30 mm vertical gap monochromator slits) and with X-ray polarization adjusted to be linear in the horizontal plane. Spectra were acquired in 'onthe-fly' grating rotation mode, taken at intervals of about 2 min with an approximate 0. 1 eV step size and 0. 1 s counting time. Data were collected at room temperature in total electron yield mode by measuring the sample drain currents with a Keithley K428 current amplifier, for which powders of the samples were fixed on conductive graphite tape and mounted in an ultra-high-vacuum chamber.
Extended X-ray absorption fine structure (EXAFS) data were collected at the XAFS (11. 1) beamline of the Elettra synchrotron light source  (Cicco et al. , 2009). Powder samples were suspended in a cyclohexane solution, filtered off with 0. 1 mm cellulose membrane filters, dried at room temperature and deposited on cellulose membranes. Room-temperature sulfur K-edge (2485 eV) EXAFS spectra were collected in fluorescence mode using a silicon drift detector (KETEK GmbH AXAS-M with an area of 80 mm 2 ). An Si(111) doublecrystal monochromator was used with about 0. 3 eV resolution at 2. 5 keV. Higher-order harmonics were effectively eliminated by using a double-flat silica mirror placed at a grazing angle of 8 mrad. The intensity of the monochromatic X-ray beam before the sample was measured by a 30 cm-long ionization chamber detector, filled with a mixture of 30 mbar of N 2 and 1970 mbar of He (1 bar = 10 5 Pa).
RMC/DSE refinement A code for RMC/DSE refinements was specifically written in order to model a three-dimensional finite-sized particle. The positions of the atoms in the particle were iteratively modified depending on the agreement of the calculated S(Q) and PDF with the corresponding experimental data. During an RMC cycle an atom is randomly chosen and randomly moved from its position within a maximum displacement. A constraint on the minimum distance between two atoms is imposed in order to avoid physical inconsistency of the models. After each move S(Q) is calculated using the DSE on an Nvidia Ge-Force GTX 690 GPU. The PDF is calculated by applying the FT to the S(Q). The two calculated functions are compared with the corresponding experimental data through an agreement parameter and each move is accepted depending on the improvement of the agreement parameter.
RMC/DSE analysis RMC/DSE analysis was conducted in order to obtain a three-dimensional finite size particle representative of the structure of schwertmannite and to fine-tune the short-range order of these nanoparticles, so the previously mentioned differences are accounted for. Reciprocal-and real-space data from samples SynHT and Nat-Freeze -the two samples showing more and less goethite content, respectively -were used in order to perform a quantitative analysis. For each sample two refinements were conducted: in the first refinement only a 30 A ˚spherical particle built from the modified Bigham cell was considered; in the second also a 30 A ˚spherical particle of goethite was refined. In the latter case, the amount of goethite content was evaluated before RMC analysis by applying linear combination analyses to S(Q).
Methods The project for this paper was developed following the 9th International Congress on Vascular Dementia, 2015, held in Ljubljana, Slovenia. Articles published between 1994 to 2016 with the keyword 'CADASIL' were retrieved from the PubMed database. Figure  1  reports the number of PubMed articles on CADASIL during the past years. Only English language papers with high scientific relevance were reviewed. In total, 163 articles related to CADASIL were reviewed and included in the paper.
METHODS The MULTI-VP model consists of a large ensemble of contiguous uni-dimensional wind solutions used to derive the three-dimensional structure of the solar wind. The numerical model is therefore a multiplexed version of a mature one-dimension numerical model representing the heating and acceleration of the solar wind (called, hereafter, the baseline model). MULTI-VP operates, typically, under the following work flow (see Fig. 1 ): 1. Choice of a magnetogram data source. The source magnetograms can be full carrington maps (e. g from Wilcox Solar Observatory -WSO) or adaptative/forecast magnetograms (for example, from the Helioseismic and Magnetic Imager -HMI -, or from the Air Force Data Assimilative Photospheric Flux Transport model -ADAPT -, at much higher temporal cadences). 2. Choice of a coronal field reconstruction method.
Comparison with coronograph observations Figures  13  and 14  show Carrington maps of synthetic WLpB built using west limb cuts at two different heights (r = 7 and 13 R ) with the corresponding real maps constructed using STEREO-B/COR2 data made available by the Naval Research Laboratory. Once again we observe that the main features of the WL maps are very well reproduced (positions, slopes and widths) in our simulations, except for the signatures of coronal transients (CME) which appear as vertical traces in the COR2 maps. MULTI-VP adopts a new approach that complements past and present efforts both on modelling the solar wind at global scales using full 3D MHD  (Yang et al. 2012 (Yang et al. , 2016;; van der Holst et al. 2014; Lee et al. 2009; Gressl et al. 2013; Oran et al. 2013 , among many others) and on modelling the heating and transport processes occurring at smaller scales on the wind flow (e.
This effect is intrinsically related to the uniformisation of the magnetic flux amplitude which takes place between the corona and the interplanetary medium. Future work will study the inclusion of alternative magnetogram data sources and coronal field reconstruction methods and of more sophisticated heating scenarios (theoretical or data-driven). We plan on performing detailed comparisons between the results of our model with other well-established methods on the near future. We aim at increasing the integration with other solar and heliospheric data and models, and at progressively approaching the requirements of real-time forecasting. R. F. P. and A. P. R. acknowledge funding by the FP7 project #606692 (HELCATS). The numerical simulations of this study were performed using HPC resources from CALMIP (Grant 2016-P1504).
Acknowledgments: The authors acknowledge funding from the  European Research Council (ERC)  ( FP/2014) / ERC  Grant-Project  670116-ARPEMA. The authors would like to thank  Matthieu Saubanère  and  Marie-Liesse Doublet ,  ICGM AIME Montpellier , for fruitful discussions as well as  Jean-Marie Tarascon ,  Collège de France Paris , for his support and fruitful discussions.
UbiK is almost exclusively present in Proteobacteria E. coli UbiK (UniProtKB entry Q46868) is a 96-residue protein that belongs to the BMFP (Brucella membrane fusogenic protein) superfamily (Pfam PF04380). Bioinformatics analysis revealed that the 596 members of the BMFP family known to date (Pfam 30. 0, March 2017, http://xfam. org/) 5 are almost exclusively found in ␣-, ␤-, and ␥-proteobacteria, where UQ 8 is present (Ref. 40 and supplemental Fig. S2 ). Interestingly, ubiK sequences are not found in the archaebacterial kingdom where MK is the major quinone (1). This exclusive presence of ubiK in the proteobacteria phylum is consistent with the involvement of ubiK in UQ 8 biosynthesis and not in MK biosynthesis. An alignment of a subset of UbiK sequences from diverse proteobacteria species is shown in supplemental Fig.
2B ). Analysis of the spectrum using K2D3 and CDSSTR softwares allowed to estimate the percentage of helical structure of 52-55%, random coils of 42-45%, and ␤-sheets of 3-4%. Using analytical ultracentrifugation with fluorescence detection, a trimeric oligomer was observed at a concentration down to 250 nM, at which point the protein aggregates suggested it is unstable as a monomer. A thermal unfolding experiment was performed by monitoring the CD spectrum of UbiK as a function of temperature (supplemental Fig. S4 ). The midpoint of the unfolding transition (T M ) was 66. 5 Ϯ 0. 1 °C and the observed conformational transitions were fully reversible, which allowed us to determine the Van't Hoff enthalpy of unfolding, ⌬H ϭ 250. 6 Ϯ 2 kJ/mol. These values are indicative   S1B ). E, quantification (peak area) of OPP from chromatograms at 275 nm (see supplemental Fig.
Structural model of UbiK UbiK and human Brick1 share 25% sequence identity. Human Brick1 (UniProtKB entry Q8WUW1) forms the core subunit of the Wave (Wiskott-Aldrich VErprolin proteins) complex involved in actin polymerization. Brick1 contributes to the architecture of the complex by serving as a platform on which the four other proteins assemble. To gain insights into the structural organization of the UbiK trimer, a structural model was generated using Swiss-Model (http://swissmodel. expasy. org/). The PDB codes for the main templates used were: 3PP5 (Brick1 from Dictyostelium discoideum), 3P8C chain E (Brick1 from Homo sapiens), and 4LL7 (She3p from Saccharomyces cerevisiae)  (16). When purified alone, Brick1 assembles as an ␣-helical triple coiled-coil along a well conserved hydrophobic patch (17) (Fig.
Circular dichroism Circular dichroism in the far-UV region was performed at 20 °C using a spectropolarimeter (Jasco J-810) equipped with a water-cooled Peltier unit. UbiK spectra were recorded in a 0. 01-mm path length cell (121. QS, Hellma) from 185 to 260 nm with 11 mg/ml of UbiK sample in 50 mM Tris-HCl, 150 mM NaCl, pH 7. 5, buffer. For each sample, an averaged spectrum was produced by merging three consecutive scans; the spectra were corrected using buffer baseline measured under the same conditions. Data were recorded in mdeg then converted to ⌬⑀ (M Ϫ1 cm Ϫ1 ). Estimations of secondary structure were predicted by using CDSSTR, SELCON3, and CONTIN methods included in the DICHROWEB server  (34) , or using the K2D3 method  (35).
Analytical ultracentrifuge (AUC) Sedimentation velocity experiments were conducted with a Beckman XL-I (Beckman-Coulter, Palo Alto, CA) AUC using an An-50Ti rotor. Increasing concentrations of UbiK and UbiK-UbiJ complexes and P2 were loaded into Epon charcoal-filled two-sector 12-mm path length cells. 400 l of samples and 410 l of buffer (50 mM Tris-HCl, pH 7. 5, 150 mM NaCl) were centrifuged at 42,000 rpm (128,297 ϫ g) and 20 °C. Absorbance at 280 nm and interference scans were collected every 5 min and sedimentation velocity data were analyzed using SEDFIT software  (38). Viscosity and density parameters were measured by using, respectively, a Anton Paar microviscosimeter and density meter (DMA 4500). Partial specific volumes of proteins were predicted by SEDNTERP software. Contrary to Multi-Signal Sedimentation Velocity experiments  (39) , which require 3 signals (2 different wavelengths in absorbance and interferometry) to determine protein stoichiometry, the present case is easier because the extinction coefficient of UbiK is close to zero (see SEC-MALS under "Results").
So the interferometric signal (⌬J) is due to both complex components, whereas the absorbance signal is only due to UbiJ. ⌬J UbiJ/UbiK , ⌬J UbiJ , and ⌬J UbiK correspond to fringes displaced, respectively, due to the UbiJ-UbiK complex, UbiJ and UbiK. ⑀ IF UbiJ , ⑀ IF UbiK correspond to molar increments of UbiJ and UbiK in Rayleigh interferometry and ⑀ UbiJ corresponds to a molar extinction coefficient at 280 nm (all three last parameters are predicted thanks to calculator module of SEDFIT software by using protein sequences). l is the path length of analytical ultracentrifugation centerpiece (1. 2 cm). ⌬J UbiJ/UbiK ϭ ⌬J UbiJ ϩ ⌬J UbiK (Eq. 8) Thanks to Equation  8 of Padrick  (39) , we can write the following. ⌬J UbiJ Х ⑀ IF UbiJ l C UbiJ and (Eq. 9) ⌬J UbiK Х ⑀ IF UbiK l C UbiK As only UbiJ absorbs we can apply the Beer-Lambert equation.
This work was supported by the  Agence Nationale de la Recherche  ( ANR ),  ANR Blanc (An) aeroUbi  ANR-15-CE11-0001-02 ,  Aix-Marseille Universite (AMU) ,  Centre National de la Recherche Scientifique (CNRS)  Grant  PICS07279 , and  French State Program "Investissements d'Avenir" Grants "LABEX DYNAMO"  and  ANR-11-LABX-0011. The authors declare that they have no conflicts of interest with the contents of this article. This article contains supplemental Figs. S1-S10.
DESCRAMBLE project One of the objectives of the DESCRAMBLE project (Drilling in dEep, Super-CRitical AMBient of continentaL Europe), funded by the European Commission within the framework of the H2020 funding scheme, is to deepen the existing Venelle-2 well in the Larderello geothermal field in Italy from 2. 2 km (and 350 °C) to a depth of 3-3. 5 km in 2017. As in previous attempts in the 1980s, the goal is to characterize and test the deep high-temperature resource below the currently exploited reservoir horizons (which is Fig. 2  Conceptual model of the JBBP project for supercritical geothermal systems in northern Honshu, Japan  (Asanuma et al. 2015). These systems lie above shallow magma chambers that are associated with Miocene and younger caldera complexes in the Tohoku region expected to have a temperature of ≈450 °C, e.
Such concepts are currently being developed in the DEEPEGS, JBBP, and GEMex projects. Different stress regimes and rock types may impact the ability to create and sustain open fractures under supercritical conditions. Testing and eventually producing high-temperature geothermal fluids from the current field laboratories in Iceland and Italy will help to validate new technology to handle the hostile downhole conditions. Current developments to utilize supercritical geothermal reservoirs are mainly driven by the momentum from ongoing H2020 projects funded by the European Commission. To further accelerate the development of high-temperature geothermal systems worldwide, a closer collaboration between associated research institutions and the geothermal industry is key.
Data sharing The US DOE Geothermal Technologies Office (GTO) now requires that data created by all funded projects be uploaded to the Geothermal Data Repository (GDR), a node of the National Geothermal Data System (NGDS)  (Allison et al. 2013; Weers and Anderson 2015, 2016). As part of the NGDS initiative, large amounts of existing data and reports were also catalogued and uploaded into this system. The system also includes a number of data visualization and query tools, such as Geothermal Prospector  (Getman et al. 2015). These data and tools are freely accessible to any interested party and can also be accessed via the geothermal energy page of the OpenEI website (http://en. openei. org/ wiki/Gateway:Geothermal), a wiki page that allows interested parties to contribute information. One of the activities of the Integrated Methods for Advanced Geothermal Exploration (IMAGE) project has been the development of an international petrophysical property database (P 3 ) that can provide inputs for numerical models.
Acknowledgements We thank  METI ,  AIST , and  NEDO  for organizing the workshop on international joint R&D on high-temperature geothermal development at the Innovation for Cool Earth Forum 2016 that led to this manuscript. P. Dobson  was supported by the  U. S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE) ,  Geothermal Technologies Office (GTO)  under Contract No. DEAC02-05CH11231  with  Lawrence Berkeley National Laboratory. Many thanks to  Jeff Hulen  and  Dennis Kaspereit  for sharing their knowledge of the IID-14 well at the Salton Sea geothermal field. Thanks also to  Jeff Winick ,  Wilfred Elders ,  Jim Shnell , and  Will Osborn  for their constructive reviews. Many thanks to  Ruggero Bertani  for his contribution to the Italian presentation at ICEF with an introduction to DESCRAMBLE.
Funding  U. S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE) ,  Geothermal Technologies Office  ( DEAC02-05CH11231 )(GTO): PD;  FP7 International Cooperation (BE ) (608553): TR, BS;  H2020 Societal Challenges (654497 ): TR, BS;  H2020 Societal Challenges (727550 ): EH, BS, FP;  Helmholtz-Gemeinschaft  ( Helmholtz Program Geothermal Energy Systems ): EH, TR;  Istituto Nazionale di Oceanografia e di Geofisica sperimentale-OGS, Italy: FP.
Acknowledgements The Italian MIUR through the  PRIN 2015 Project SMARTNESS  ( 2015K7FZLH ) "Solar driven chemistry: new materials for photo-and electro-catalysis" is gratefully acknowledged for financial support. G. T. thanks  Fondazione CR Firenze  for its support to the  HORIZON  project. G. G. gratefully thanks  Prof. G. Pacchioni  and  Dr. F. Mercuri  for fruitful discussions.
Results and discussion The selection algorithms explained in section are executed in the Matlab environment, with the results presented in this section. In the prescreening stage, a temperature range from 90 to 120 °C is used as the constraint to prescreen a database of available materials, producing a list of PCMs as displayed in Table  2. Then, MADM techniques based on TOPSIS and AHP are employed to rank the list of PCMs. Table  3  shows the relative importance of thermo-physical properties, as well as their weights based on AHP method, whereas Table  2  illustrates the materials and their thermo-physical properties in the ranked order. The objective function examination is performed by plotting f 2 , the equivalent diffusivity against f 1 , the volume energy density, as displayed in Figure  1.
FTIR-ATR analysis Freeze-dried algae were analyzed by Fourier Transform Infrared Spectroscopy with attenuated total reflectance. The FTIR spectra were recorded on a Nicolet iS5 FTIR spectrometer (Thermo Scientific) using an iD7 ATR module with a diamond crystal. All spectra resulted from 32 scans and a resolution of 1. 928 cm -1 from 500 to 4000 cm -1. The OMNIC software was used for data acquisition (absorbance mode). A background reference was obtained before each sample measurement. All samples were recorded in triplicates. The spectra were studied between 600 and 1800 cm -1 zone, to avoid absorbance of water zone (3600 -3100 cm -1 ).
Relations between biochemical composition and meteorological factors Correlation between the biochemical composition of S. muticum and environmental parameters was evaluated in order to better understand the influence of these factors on the variation of S. muticum biochemical composition. Relations between variables (meteorological and biochemical) and individuals data are represented with a PCA (Figure  1 ). The analysis was performed on 36 samples (three per month), using following variables (nitrogen, sulphated compounds, proteins, uronic acids, phenolic compounds content; meteorological data: air temperature, seawater temperature, sunlight, rainfall). It is noticeable that, from June to September, the macroalga was influenced by temperature and sunlight (Figure  1 ). April and October data follow the axis of proteins and February, the axis of uronic acids.
One should then note that data were greatly influenced by respectively proteins and uronic acids contents. Precipitations seem to influence the distribution of data obtained in May 2015. Furthermore, biochemical composition of S. muticum analyzed with colorimetric method was not directly related to the meteorological factors (temperature, sunlight). Meanwhile, the rainfall was inversely related to phenolic content of S. muticum. Moreover, nitrogen and uronic acids were inversely related to seawater temperature. Nitrogen and uronic acids followed the same trend and protein content followed the same direction but did not overlap them (Figure  1 ). FTIR spectra of each monthly sample are presented on Figure  2  (500 -4000 cm -1 ). The comparison of the spectra was performed from 600 to 1800 cm -1 with a Principle Component Analysis (PCA, Figure  3 ).
Correlation with meteorological factor and lifecycle of the alga Biochemical composition (proteins, nitrogen, polysaccharides, sulfated and phenolic compounds) did not present any strong correlation with temperature (air or seawater) and sunlight. Balboa et al. (2015) experienced the same observation, obtaining a weak correlation between the biochemical composition and temperature. An absence of correlation between temperature (air or seawater) and proteins content might be due to a light imprecision on the measured temperatures. Indeed, the meteorological data were not obtained from an in-situ measurement, but they were provided by another source (a real-time measurement from the buoy placed off the sampling location). Therefore, for further analysis, in-situ measurements of water temperature and oxygen salinity are suggested.