dataesr/scientific-paragraphs-categorization huggingface dataset:
Novartis, Novo Nordisk, Eli Lilly, Johnson & Johnson, AstraZeneca, Boehringer Ingelheim, Janssen, Intarcia, Metacure, Pfizer, MSD, Roche Diagnosis, Medtronic, Menarini Diagnosis, Abbott, Lifescan, Vitalaire, Dinno Health, Ork'yn, ANSM, CNAMTS, CEPS, SFD, SFE and NSFA. Compliance with Ethics Guidelines. All participants provided written informed consent and the study was conducted in accordance with ethical standards of the responsible committee on human experimentation (institutional and national) and with the revised Declaration of Helsinki, 1964 (ethical agreement number 1-15-22, CPP Sud-Ouest et Outre-Mer 1). Availability. The datasets analyzed during the current study are available from the corresponding author on reasonable request. distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Modelling PDF data up to 30 Å results in a proper description of experimental data (R wp = 0. 165; Figure  3 ). The refined Si1-D4 distance is 1. 53 Å with an occupancy (SOF) of the D4 site of 0. 35. Two of tetrahedral Ca 4 void are fully occupied. The corresponding SOF parameters were fixed during the last cycles. The D3 site is under-occupied as well (Table  1 ), giving a chemical formula CaSiD 1. 08. Inorganics 2019, 7, x FOR PEER REVIEW 3 of 13 Furthermore, the deuteration process was monitored in situ by neutron diffraction of a CaSi sample under a deuterium gas atmosphere up to 5. 5 MPa and elevated temperatures. At high temperatures (>520 K), hydrogen-poor phases CaSiDx<1 occur while the hydrogen-rich phase is formed upon cooling. Crystallographic information (CIF) for all phases can be found in the Supplementary Materials.
We want to emphasize that this step is a crucial one. The deuterides were prepared in a Nicrofer 5219Nb-alloy 718 autoclave (home built) at deuterium gas pressures of 15 MPa and 523 K. The samples were annealed for 72 h. X-ray diffraction was done on a laboratory Stadi P diffractometer (STOE & Cie, Darmstadt, Germany). The instrument is equipped with a sealed copper X-ray tube and a germanium monochromator (Cu Kα 1 radiation). Data were collected with a position sensitive Mythen 1K silicon-strip detector. Measurements were done in Debye-Scherrer geometry using 0. 2 mm glass capillaries. Neutron powder diffraction was performed at the E9 FIREPOD diffractometer  [30]  at the reactor source BERII of the Helmholtz-Zentrum Berlin (HZB), Germany on samples enclosed in 6 mm vanadium cans at a constant wave length of 1.
7982 Å. In situ neutron diffraction of the deuteration process was performed at the D20 instrument  [31]  at the reactor source of the Institut-Laue-Langevin (ILL), Grenoble, France. The experiments were done in home-build sapphire single-crystal cells attached to a gas supply system. Heating was realised by two infrared laser beams. Details of the setup were already described elsewhere  [32, 33]. Measurements were done at a constant wavelength of 1. 86829 Å. Powder diffraction data were treated by the Rietveld method  [34, 35]  as implemented in the program Topas  [25]. Neutron total scattering experiments were done at the time-of-flight (TOF) instrument POLARIS  [36, 37]  of ISIS spallation source, Didcot, UK, on samples enclosed in 8 mm vanadium cans. Data and Proposal information can be accessed under reference  [38].
Data reduction was done using the GunrunN package  [39]. Data were corrected for background using measurements of an empty vanadium can and the empty instrument. To get an absolute data scale, intensities were normalized to the measurement of a vanadium rod with 5 mm diameter. The maximum of reciprocal space data used for the Fourier transformation was set to 39 Å -1. The minimum real space radius for the Fourier transform was set to 1. 15 Å. The obtained real space pair distribution function was evaluated using small box modelling. A crystallographic model (full symmetry) was fitted to the data using pdfGUI  [40]. All graphs were prepared using the program Gnuplot  [41]. Molecular and crystal structures were prepared using VESTA  [42, 43].
Basic characteristics of chronic HBV-infected patients We collected data on 2565 chronically HBV-infected subjects, including 493 CHB with HBsAg sroclearance (361 males and 132 females) and 2072 CHB without HBsAg serclearance (1325 males and 747 females) in this study. The numbers of CHB male patients in the 2 groups included 361 with HBsAg seroclearance and 1325 without HBsAg seroclearance (P <. 001). Average ages in the CHB patients with HBsAg seroclearance and without HBsAg seroclearance group were 50 and 46. 3 years, respectively (P <. 001), with an age range of 30 to 65 years. Mean of serum ALT values were 17. 7 and 16. 5 U/L, respectively (P =. 24) (Table  1 ). The lengths of follow up duration in the CHB patients with HBsAg seroclearance and without HBsAg seroclearance group were 6.
SNP annotation of e-QTLs of rs7944135 To elucidate the association between SNP rs7944135 and gene expression, we used the publicly available databases GTEx Portal (http://www. gtexportal. org/home/) to obtain the tissue e-QTLs. As shown in Table  4  and Figure  3  the AA genotype of rs7944135 had highly expressed of MPEG1 gene if compared to AG and GG genotype.
Furthermore, the MPEG1 gene has a role in immune response through encoding perforin-like protein and recognition of antigen. [35] The perforin-like protein is predicted to be a perforin domain of membrane attack complex that helps cytotoxic T cells and natural killer cells kill virus infected cell. [36, 37] In our study, we demonstrated from The GTEx portal database that rs7944135 AA genotype shows the highest expressed of MPEG1 gene in compare to GG and AG genotype. It infers that patients with AA genotype on rs7944135 could have higher expression of MPEG1 and lead to higher immune response against HBV, and results in seroclearance of the HBsAg. Additional genes, such as HLA, cytokine TNF-a, chemokine receptor 5,  [38]  and MCP1,  [21]  were found to be associated with the incidence of HBsAg seroclearance through cell-mediated immune responses.
The temperature was measured with a K-type thermocouple attached close to one of the diamond anvils, while internal pressures were determined from the refined unit cell volumes of admixed NaCl and its equation-of-state parameters. 28 High-T PXRD data were collected under vacuum on a Bruker D8 Advance diffractometer equipped with a MRI radiation-heating stage (Physikalische Geraẗe GmbH, Germany). All heating-and pressure-increase rates as well as equilibration times are detailed in Supporting Table  2. Structure solutions and Rietveld and Lebail refinements were performed using the software package TOPAS-Academic V6. 29  Details on structure solution and refinement strategies can be found in the Supporting Information, alongside the observed diffraction data.
A similar mechanism was described for the ZIF-4-ZIF-4-LT transformation. 16 An overlay of the asymmetric units of ZIF-cp-II and ZIF-4-LT is shown in Supporting Figure  11. ZIF-4-cp-III. The structure of ZIF-4-cp-III could not be reliably determined due to poor data quality. However, since the transition from ZIF-4-cp-II to ZIF-4-cp-III is instantaneous, we suggest that this orthorhombic to monoclinic transition can be classified as a displacive transition and that the structure retains the cag topology. The unit cell volume continuously decreases across the ZIF-4-cp-II-ZIF-4-cp-III transition (Supporting Figure  10 ). ZIF-hPT-I. The structure of ZIF-hPT-I could not be solved based on the present data. However, we propose a unit cell based on successful structureless refinements (Table  2 ).
Model validation To validate the numerical model, the simulation was carried out based on a similar dew point cooler module which had published with numerical or experimental results (Hasan  [10]  and Riangvilaikul et al. [11] ). Table  1  shows the cooler parameters used in each of these studies as well as inlet air climatic conditions. Figure  3  shows simulation dynamic profiles for air temperature in the dry and wet channel as well as the wet wall surface temperature. These results are obtained from the SPARK model applied to Hasan  [10]  case study. Air in the dry channel flows in the opposite side of the air in the wet channel. One can notice that inlet air temperature drops from 30°C to 17. 3°C in the dry channel which is lower than air wet bulb temperature (18. 8°C). Fig. 4. Temperature profiles given by Hasan  [10].
9, 10   ecent studies also showed that glucocerebrosidase 1 (GBA1) deficiency may lead to the destabilization of αS tetramers. 11 n the presence of sodium dodecyl sulfate (SDS) micelles (for example, PDB ID: 1XQ8), αS adopts a broken α-helical structure with residues Val 3 -Val  37  and Lys  45  -Thr 92 forming two curved αhelices. 12 4] [15]  In a previous study, we rationally designed a broken αS tetramer with residues Val 3 -Val  44  and Lys 51 -Thr 92 forming two α-helices, which is predicted to be more stable than the SDS-bound conformation in aqueous solution. 16 αS and βS tetramers were optimized by tight packing of their hydrophobic non amyloid-β component (NAC) regions (residues 61-95 for αS and 61-84 for βS). 16 Our computational study revealed that quadruple mutant (E46K + H50Q + G51D + A53T) could raise the energy barrier to tetramerization,  16  consistent with the experimental finding that this familial mutant decreases the tetramer:monomer ratio.
1  and Fig. S1  in ESI †. The initial conformations of all helical multimers are shown in Fig. 2. All protein structures were represented by the CHARMM36m force field,  21  and the corresponding modified TIP3P water model was used to solvate the protein structure. MD simulations were conducted using GROMACS-5. 0. 4 software. 22 Each structure was solvated in a water box, with any protein atom at least 15 Å from the box edge, and salt ions were added to neutralize formal charge with an additional 0. 15 M NaCl to represent physiological ionic strength. MD simulations were conducted at 310 K and 1 atm. The temperature was maintained using a velocity rescaling thermostat,  23  and the pressure was controlled by the Parrinello-Rahman coupling method. 24 The short-range van der Waals and electrostatic interactions were truncated using a cut-off of 12 Å, and the long-range electrostatics treated using the particle mesh Ewald (PME) method.
Introduction Observations and monitoring are fundamental to know and characterize the environment in a broad sense, including renewable energies. To ensure quality of observational datasets as the credibility of the climate record, the Global Climate Observation System (GCOS) has introduced the concept of essential climate variables that denotes a set of variables describing the environment which are of preeminent importance for the climate community  (GCOS 2003). This introduction was meant to answer the need for enhancing coordination in climate observations  (Bojinski et al. 2014; Houghton et al. 2012). The concept of the essential climate variables has been widely endorsed by scientists, operators of observing systems, programme planners and policymakers. It helps these users to focus more on these variables in terms of observational, scientific and funding priorities, and ensure that appropriate efforts are made on the dissemination of these variables, so that they are made available to the various stakeholders.
The collection of such data have guided the development of several databases, web services, etc. that among other aspects have tackled challenges to take into account the GEOSS interoperability requirements in operational and commercial information systems for renewable energies (see e. g. Gschwind et al. 2006; Ménard et al. 2012 Ménard et al. , 2015)). It does not comprise the variables relating to social and economic aspects supporting decision making in investment nor those relating to civil engineering that are needed to erect a plant or farm. The present paper aims to propose a definition of essential renewable energies variables (EREV) and to provide lists of essential renewable energies variables (EREV) resulting of this long history/ large experience and specific to four energies: hydropower, solar, wind, and marine energies.
It will be seen later that with respect to renewable energies and due to the overlapping nature of the various domains and SBA, there are common needs and that it is possible to converge towards a limited set of variables. In a given domain, several variables are possible candidates to be essential. Once an initial list of variables set-up,  Bojinski et al. (2014)  or the ConnectinGEO project  (Maso et al. 2015)  recommend to select only those that are feasible, cost-effective for systematic observation and relevant. Variables must obey the following principles:. Feasibility: quantifying the variable, either from observations or derived methods, on a global scale is, in principle, technically feasible using proven, scientifically understood methods;. Cost effectiveness: the cost of generating and archiving data on a variable is affordable; observation should mainly rely on coordinated observing systems using proven technology, taking advantage where possible of historical datasets.
Conclusion This work has proposed a definition for Essential Renewables Energies Variables (EREVs). These EREVs form a subset of Essential Climate Variables and diverge on the observation requirements due to their usage by the stakeholders of the renewables energies domain. The cases of hydropower, solar, wind, and marine energy have been considered. These variables should to be considered for the long-term measurement, assuming that it is feasible to measure them in terms of cost, effort, and impact. Moreover, considering than most of the EREVs are comprised in the ECVs data set, it could promote collaboration. The main difference consists in the observational requirements in time and spatial resolution. The ECVs observational requirements can evolve during time and converge with those of EREVs.
These parameters are illustrated in Figure  2C. This dataset and the printing speed are converted to a programming language compatible with the printer. One layer at a time, the extruder head (and/or the build platform on which the part is manufactured) moves in the (x,y) plane and extrudes material shaped as a flatten filament in order to create one cross-section of the part. After completing the layer, the extruder head lifts (or the building plate lowers) to the height of a layer thickness to build the next layer of the part on top of the previous layer, and this process continues until the part is completed. Various geometrical parts (2D and 3D) were printed using the upgraded printer and pastes described in Section 2. 1. First, single filaments of 100 and 200 mm were extruded using a 0.
7 mm diameter nozzle at a linear flow ranging from 3 to 5 mm s -1 at approximately 20 cm above the printing surface. Figure  2B  shows the typical filaments obtained. Five different geometric models with increasing complexity were tested: (i) a 6 cm 3 cube; (ii) a 4. 0 cm high monofilament rectangular cuboid with a 4. 00 cm 2 square cross-section; (iii) a 1. 5 cm long bridge with a 1 cm high pillar and rectangular cross-section of 0. 5 × 1. 0 cm²; (iv) a 3DBenchy model composed of complex 3D printing shapes, including a 40° overhang, 1. 0-2. 0 cm bridge, and 1 cm high narrow pillar with a 0. 15 cm² crosssection; and (v) a 5. 0 cm high double spiral vase with an opening diameter of 1. 6 cm. Models (iv) and (v) were downloaded from the Thingiverse database  (Thingiverse. com, March 2018)  Drying: Once printed, the samples (filament and 3D parts) were dried using two different methods.
Acknowledgements This work was supported by the  Idex UGA  grant (AGIR). The  LGP2  and  3SR  laboratories are part of the  LabEx Tec 21 (Investissements d'Avenir : grant agreement no. ANR-11-LABX-0030 ) and the  PolyNat Carnot Institute  ( Investissements d'Avenir : grant agreement no. ANR-16-CARN-0025-01 ). This research was possible owing to the facilities of the  TekLiCell  platform funded by the  Région Rhône-Alpes  (ERDF:  European Regional Development Fund).
69 and 5. 01 ppb (0. 5 %-1. 6 %), which are within the uncertainties of the two datasets. The NDACC N 2 O retrieval has good sensitivity throughout the troposphere and stratosphere, while the TCCON retrieval underestimates a deviation from the a priori in the troposphere and overestimates it in the stratosphere. As a result, the TCCON X N 2 O measurement is strongly affected by its a priori profile. Trends and seasonal cycles of X N 2 O are derived from the TCCON and NDACC measurements and the nearby surface flask sample measurements and compared with the results from GEOS-Chem model a priori and a posteriori simulations. The trends and seasonal cycles from FTIR measurement at Ny-Ålesund and Sodankylä are strongly affected by the polar winter and the polar vortex. The a posteriori N 2 O fluxes in the model are optimized based on surface N 2 O measurements with a 4D-Var inversion method.
Subsequently,  Wells et al. (2018)  applied the same model framework in a multi-inversion approach to place new top-down constraints on global N 2 O emissions. To our knowledge, there have not yet been any studies investigating differences between the TCCON and NDACC N 2 O measurements. In this paper, an intercomparison between the TCCON and NDACC X N 2 O measurements at seven sites in the 2007-2017 period is carried out. The target of this study is to better understand the discrepancies between the TCCON and NDACC N 2 O measurements, and to know whether two networks can be combined with atmospheric chemistry models for evaluation, seasonal cycles and long-term trend analyses. Section 2 describes the TCCON and NDACC data used in this paper. The biases between TC-CON and NDACC X N 2 O measurements are shown in Sect.
TCCON and NDACC measurements The ground-based FTIR sites used in this study are shown in Fig. 1. Both TCCON and NDACC N 2 O measurements are available at these sites. The coordinates of the sites together with the time coverages of the data are listed in Table  1. They cover a large latitudinal range from 45. 0 • S to 78. 9 • N. Note that there are two observatories at Réunion, one is at Saint-Denis recording NIR spectra and the other one is at Maïdo recording MIR spectra  (Zhou et al. , 2016). At Lauder, two spectrometers, Bruker  120HR (2004 -2011 ) and 125HR (2010-present) , have been applied to record TCCON spectra, and the same Bruker 120HR instrument is applied to record NDACC spectra. Details on the measurements can be found in  Pollard et al. (2017). In this study, only the TCCON measurements from the Bruker 125HR at Lauder are used.
, 2014; Blumenstock et al. , 2014; De Mazière et al. , 2014; Griffith et al. , 2014; Sherlock et al. , 2014). NDACC retrieval strategies can vary from site to site, depending on sitespecific conditions, e. g. , humidity, instrument and retrieval software. Table  3  lists the NDACC retrieval settings for each site. Two microwindows (MWs) (2441. 8-2444. 6, 2481. 1-2482. 5 cm -1 ) are employed at Ny-Ålesund and Bremen, while the other sites use four microwindows (2481. 3-2482. 6, 2526. 4-2528. 2, 2537. 85-2538. 8 and 2540. 1-2540. 7 cm -1 ). The Wollongong site uses the atm. 101 spectroscopy, while the other sites use the HITRAN 2008  (Rothman et al. , 2009). In fact, N 2 O line parameters are the same in these two spectroscopic databases. The optimal estimation method (OEM)  (Rodgers, 2000)  is applied to construct the regularization matrix of the a priori information at Ny-Ålesund, Bremen, Wollongong and Lauder, while the Tikhonov method (Tik)  (Tikhonov, 1963 ) is applied at Sodankylä, Izaña and Réunion.
01 ppb (0. 5 %-1. 6 %). Since the random uncertainty of the TCCON measurement is about 1. 0 % and the systematic and random uncertainties of the NDACC N 2 O retrievals are about 2. 0 % and 1. 5 %, the difference between the TCCON and NDACC measurements is within their combined uncertainty. However, there is a large difference between TCCON and NDACC data in February-May at Ny-Ålesund and Sodankylä, which will be explained in the next section. In addition, the X N 2 O trend derived from NDACC measurements is slightly larger than that derived from TC-CON measurements. This will be discussed in Sect. 5. 4 Case study -Sodankylä The time series of TCCON and NDACC co-located X N 2 O hourly means together with their difference and correlation at Sodankylä are shown in Fig. 4. There is no FTIR measurement during the northern winter season due to the polar night.
The TCCON X N 2 O measurements are very close to the NDACC data in northern summer and autumn but are lower than the NDACC data during spring. The air above Sodankylä is frequently affected by the Arctic polar vortex in winter and spring  (Kivi et al. , 2001 (Kivi et al. , , 2007;; Karppinen et al. , 2016; Denton et al. , 2018). The high potential vorticity (PV) value at a constant potential temperature of 430 K is a useful index to identify the polar vortex  (Schoeberl and Hartmann, 1991). The PV data in this study are downloaded from the ECMWF ERA-Interim reanalysis dataset  (Dee et al. , 2011). We find that the low X N 2 O values in the TCCON measurements in N 2 O measurements from the ACE-FTS satellite are applied to assess the change of the N 2 O vertical profile when Sodankylä is inside the polar vortex.
ACE-FTS uses the solar occultation technique to measure mole fractions of atmospheric trace gases, mainly in the stratosphere, with a vertical resolution between 1. 5 and 6 km  (Boone et al. , 2013). The latest ACE-FTS level 2 v3p6 N 2 O data are used in this study. It is assumed that ACE-FTS measurements are representative of the N 2 O variability in the stratosphere. Sheese et al. (2017)  showed that the differences between ACE-FTS v3p6 and MLS and MIPAS N 2 O measurements are within 20 % below 45 km. ACE-FTS pixels are selected within ±4 • × 8 • (latitude by longitude) of Sodankylä during 2012-2016. In total, there are 43 individual days when TCCON, NDACC and ACE-FTS measurements are all available. The day is identified as being within the polar vortex if it satisfies the following two criteria: (1) PV value at 430 K on that day is larger than 20 × 10 -6 Km 2 kg -1 s -1 ; (2) the daily mean of X N 2 O derived from TCCON differs by more than 6.
However, the TCCON retrieval overestimates the deviation from the a priori profile in the stratosphere (see Fig. 2 ). When Sodankylä is inside the polar vortex, the ACE-FTS measurement (used here as the reference dataset) is much lower than the TCCON a priori profile in the stratosphere. As a result, the TCCON retrieved N 2 O column overestimates the magnitude of the N 2 O decrease, explaining why these data are always lower than the NDACC measurements in spring during polar vortex overpasses. Figure  6  compares the standard TCCON and NDACC X N 2 O retrievals with updated versions using the ACE-FTS measurement as the a priori profile (above 10 km) for days inside the polar vortex. As expected, changing the a priori profile does not lead to much change in the NDACC retrievals, whereas the TCCON retrievals using the ACE-FTS profile as the a priori profile increase significantly and are more similar to the NDACC retrievals.
Note that model output and flask sample data are both for the 2007-2014 period, whereas all available FTIR measurements are from the 2007-2017 period (see Fig. 3 ). The numbers of FTIR measurements before 2014 are very limited at Sodankylä and Réunion. As the NOAA GMD surface N 2 O measurements show that atmospheric N 2 O increases with a constant annual growth rate during the last decade, it is assumed that these two different time periods do not introduce the discrepancy in the trend and seasonal cycle computations. The a priori GEOS-Chem X N 2 O trend (about 1. 25 ppb yr -1 ) is too large based on all the observational datasets in Fig. 8 , implying an N 2 O flux overestimate in the a priori inventories used in the model. Conversely, the X N 2 O trend in the a posteriori GEOS-Chem simulation (0.