dataesr/scientific-paragraphs-categorization huggingface dataset:
ImageJ was used to assign red, green, or blue color to the three detection channels and define ac ommon intensity range (same minimum and maximum values for all channels). Theresulting overlay images provide RGB codes (between 0a nd 255 for R, G, and B) for each pixel, which can then be transferred into RGB ratios and directly related to the four different FRET nanoparticles. As afirst evaluation of biocompatibility,the stability of the RGB codes was analyzed for the four different nanoparticles incubated in PBS buffer at different pH (5. 3, 6. 8, and 7. 5) for 2hand 4h. The TG PL intensity ratios of each code were nearly invariant (Figure  S8 ), which provided first good evidence regarding the compatibility of our temporal PL encoding approach with live cell imaging. To demonstrate the actual application for live cell imaging,HeLa cells were incubated with Tb-QD/SiO 2 (6 nm), Tb-QD/SiO 2 (12 nm), Eu-QD/SiO 2 (6 nm), and Eu-QD/SiO 2 -(12 nm), respectively.
Electrochemical tests: Anode-cathode coupling and measurement of galvanic corrosion current After two days of immersion in the saline solution, each anodic specimen was placed with the corresponding cathode in a sodium hydroxide solution (NaOH) with a part of the test pieces that remained above the level of the solution as shown in Fig. 3. All tests are done at a temperature of 20 °C in a temperature controlled room. The anode and cathode samples separated by a distance of 30 cm were connected by a potentiostat controlled by EC-LabⓇ software using the ZRA (Zero Resistance Ammeter) electrochemical technique. This application consists in applying zero volts between the connected electrodes and then measuring the galvanic coupling current between them. The microstructure differences between both electrodes result in the fact that one of them behaves anodically and the other one behaves cathodically.
Tomography Dual-scale laboratory X-ray tomography scanning was used is this study to capture both the macroscopic deformation of the foam during loading and the initial local microstructure, as detailed below. The tomograph (phoenix|x-ray v|tome|x s, GE Company, Boston, MA, USA) produces a series of N radiographs corresponding to N angular positions of the sample. Based on the Beer-Lambert law, every line integral of the attenuation coefficient along the beam path corresponds to an element in the recorded projection  [22]. The resulting images are superimposed information of a three-dimensional (3D) object in a two-dimensional (2D) plane. The detector is a charge-coupled device (Varian Paxscan, Varian Medical Systems Inc. , Palo Alto, CA, USA). It records radiographs passed through the sample, which are imported into a commercial reconstruction software (datox|x, GE Company, Boston, MA, USA).
The latter was parallel to the (y, z) axes of Figure  2a , where a sketch of the setup is illustrated. Then, these high-resolution 3D images were combined and concatenated to retrieve the whole 3D volume, but this time with a small voxel size. The difference between big and small voxel size tomography is illustrated in Figure  2b, c. It is very clear from this image that the higher resolution allows the local presence of intermetallic particles to be captured. Then, aluminum, intermetallic particles, and void-cells were segmented by standard thresholding based on gray levels distribution and were attributed gray (125), white (255), and black (0) 8-bit values for visualization, respectively. The volume fraction of solid (intermetallic particles and aluminum) and gaseous phases were calculated by counting the number of voxels of each corresponding color using the Fiji software  [26].
The results are given in Table  2. The amount of porosity in the solid phase was less than 0. 01 %, which is negligible. The volume fraction of voids and intermetallic particles in the 20 PPI sample were slightly lower than those in the 30 PPI sample. In addition, the image analysis of the tomographic data revealed that most struts presented a close-to-triangular cross section. The solid phase was analyzed with the local thickness plugin of the Fiji program. The plugin estimates the local thickness by the largest sphere that fits inside the solid phase and contains its voxels. The result of the analysis is a 3D stack of the foam structure, where the local thicknesses can be represented with a given color map. The average size and distribution of void-cells in three directions were evaluated by the analysis of the gaseous phase of the 3D binary image using a 3D Watershed plugin implemented in the Fiji program.
Mesh Generation In this study, the initial 3D tomographic volumes of the foam samples were used to generate 3D image-based FE meshes. First, a surface mesh was generated from the solid phase boundaries in the volume. Next, the surface mesh was simplified and remeshed to reduce the number of triangles while preserving a proper description of the surface. Finally, the solid volume was filled by first-order tetrahedra  [13]  (C3D4 elements in the Abaqus software), allowing for explicit non-linear simulations including damage and fracture. The whole meshing procedure was performed using the commercial Avizo R software  [39]. Each foam sample was meshed with four different mesh sizes in order to investigate the mesh size sensitivity of the results, as detailed in Section 3. 4. The number of tetrahedra and nodes of the reference volume meshes are detailed in Table  4.
Two classes of FE models were considered in this work: (i) homogeneous (or microstructure "blind") models, where the local constitutive behavior corresponds to the nominal average behavior of the aluminum alloy everywhere; and (ii) heterogeneous models, where the local constitutive behavior depends on the local microstructure (intermetallic fraction in the present case) as informed from the detailed initial 3D tomographic scans of the samples. The procedure used to generate the heterogeneous FE model is described in  [40]  and briefly recalled here. For each element of the 3D image-based FE mesh, a python script retrieves the voxels of the segmented tomographic volume located in the element interior. The number of white (intermetallic) voxels is then evaluated in order to compute the local intermetallic volume fraction f I M in the element.
The sensitivity of the simulation results with respect to the element size is illustrated in Figure  6a ,b with the predicted apparent Young's modulus obtained with the homogeneous non-porous models. It converged to about 125 MPa with decreasing element size in the case of the 20 PPI foam sample and to about 210 MPa in the case of the 30 PPI foam sample. The characteristic element size of 150 µm was therefore chosen as the reference element size for both samples in Table  4. However, one can note that these predicted values of Young's moduli were overestimated with respect to the experimental measures based on the (3D) image-based macroscopic tensile strain. Two modeling choices in the present study might contribute to this situation: (i) The use of first-order tetrahedral elements imposed by the complexity of the structure and the compatibility with the Explicit solver of the Abaqus FE software.
Acknowledgments:  Pierre Simon  gratefully acknowledges the  French Direction Générale de l'Armement (DGA)  and the  French-German Research Institute of Saint-Louis  for funding this work. The experimental tests were carried out at the  French-German Research Institute of Saint-Louis and at the Laboratory of Microstructure studies and Mechanics of Materials from University of Lorraine. George Z. Voyiadjis gratefully acknowledges the financial support provided by a grant from the  National Science Foundation EPSCoR CIMM  (grant number # OIA-1541079 ).
For example, the pressure dependence of the viscosity of B 2 O 3 , studied by Sperry and Makenzie To date, the latest high-pressure dataset available for SCHOTT N-BK7  is based on relatively fast-cooled glass under high pressure (0. 5 GPa to 1. 5 GPa using a piston cylinder). Combined with the post-mortem density and volume recovery measurements,  28  which also indicate a linear viscosity increase with increasing pressure (∆/∆P =0. 05 log 10 (Pa•s)/100 MPa 28 ). Several phenomenological models  29, 30, 31, 32  describe the time dependence of glass relaxation as a function of both temperature and pressure history. A recent relaxation model was developed by Ding et al. ,  28  following the approach of Gupta  33  to use two internal parameters, named fictive temperature 34 ( ) and fictive pressure 33 ( ).
The temperature was kept constant using a proportional-integral-derivative (PID) controller during the mechanical experiment as a small variation in temperature could result in significant variations of viscosity. A data point is recorded each 1 or 2 s. After the end of the deformation, the piston was maintained in position, and the temperature was decreased at a rate of 70 °C/min by decreasing power. Afterward, the pressure was slowly released at a rate of ~10 MPa/min to ambient pressure.
Maxwell model We use both a single Maxwell and a two-Maxwell viscoelastic model to fit the experimental data combined with an optimization algorithm (MATLAB scripts are provided as supplementary materials). The single Maxwell viscoelastic model (1-Maxwell) is defined by  52  (3) where is the total strain rate, is the strain rate of elastic deformation, is the strain rate of viscous deformation, is the shear stress converted from the net uniaxial stress by assuming a constant Poisson's ratio of 0. 206 for SCHOTT N-BK7  , is the viscosity and G is the elastic shear modulus. To keep the model simple, we neglect the contributions from the volumetric bulk deformation strain. Under a constantly applied strain rate , the stress can be solved by integrating over time as: ( where is the Maxwell relaxation time can be calculated by Eq.
However, the elastic shear modulus is known to be nearly constant in both glass science and geoscience applications  54, 55, 56, 57  for small pressure changes (<2 GPa). Therefore, we tested fitting a single set of material parameters to all experimental mechanical data using a 1-Maxwell model. In order to fit the model to the experiments, we employ a multidimensional unconstrained nonlinear minimization optimization algorithm (using the Nelder-Mead Simplex search algorithm as incorporated in the MATLAB function fminsearch  58  ), to find the best-fit parameters for the model. The best-fit model we obtained (after experimenting with a range of starting values , GPa, ∆H/R = 7. 4×10 4 K, = 8. 129×10 -6 m 3 /mol  59  ) is shown in Fig. 3a  (best-fit yields G=24. 9 GPa ∆H/R= 5. 4×10 4 K which are different than the reference value of G ref =34 GPa  59  and (∆H/R) ref = 7.
4×10 4 K at 1-atm), which is rather poor. For many glass types, more than one Maxwell element is needed to describe the experimental data. 40 To improve the fitting, we applied a generalized Maxwell model with 2 Maxwell elements  59  (2-Maxwell model applied, MATLAB script available in supplemental materials), which models the rheology of glass as two parallel Maxwell bodies, which was previously found to approximate the uniaxial stress relaxation of glass successfully. 60 For the stress revolution, we introduce a weighting parameter w for 2-Maxwell model and a free parameter fac to separate the viscosities, 54 (8)   where the stresses are (9) (10)   and the viscosities are (11) (12) (13) (14) (15) (16) The best overall fit parameters and curves of the 2-Maxwell model are shown in Fig. 3b.
Acknowledgments S. D. thanks  D. Mainprice  and  M. A. Bouhifd  for informative discussions during this study. The authors thank the technicians and engineers at the SCHOTT machine-shop for the sample preparation. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant  agreement  N° 642029.
Acknowledgements This work was supported by the  Engineering and Physical Sciences Research Council  [grant number  EP/M012700/1 ]; the  European Magnetic Field Laboratory (EMFL) ; the  Engineering and Physical Sciences Research Council  via its membership to the EMFL [grant number  EP/N01085X/1 ]; the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  [grant agreement number  696656 ]; The  University of Nottingham ;  The National Academy of Sciences of Ukraine ; The  Leverhulme Trust  [ RF-2017-224 ]; The Royal Society [IE160395]; and the  Chinese Academy of Sciences. E. E. V. acknowledges support from the  Russian Science Foundation  ( 17-12-01393 ).
Acknowledgements TMH would like to acknowledge funding from  FP7-PEOPLE-2013-CIG  ' NON-EQ-SA ' (631350), ERC-2017-STG ' Life-Cycle ' (757910),  LabEx CSC  project  Controlling supramolecular assembly by "tunable anti-cooperativity ", GF received funding from  Ministère de l'Education Nationale, de l'Enseignement supérieur et de la Recherche. VM was funded by  IdEx attractivité Centrosome  centering by dissipative self-organization. D. S. and P. B. acknowledge financial support from the  University of Mainz  and the  Center for INnovative and Emerging MAterials (CINEMA).
30, 2. 49, 12. 29, 5. 57 and 5. 80 fm), and to determine low temperature magnetic structures for all the samples. Rietveld analysis was carried out using the Fullprof package  15  and BasIreps  16  was used for magnetic structure analysis. The Rietveld fit to 100 K NPD data (collected from WISH@ISIS) of Co2ScSbO6 confirms that it adopts the NTO structure shown in Fig. 1  as inset. However, structure refinement revealed 21% substitution of Sc by Co, resulting in a Sc-deficient overall composition Co2. 16Sc0. 84SbO6. The net ferroelectric polarization of PS = 19. 91 μC/cm 2 calculated from a point charge model  17  is comparable to the value for Ni2ScSbO6 of PS = 13. 27 μC/cm 2. Rietveld fits to 100 K NPD data of the x = 0, 0. 5, 1 and 1. 5 Ni2-xCoxScSbO6 samples confirm that a continuous NTO-type solid solution is formed with lattice parameters increasing with x.
The authors thank  EPSRC  and  STFC  for support of this research. We are also grateful for support from FRMII, ILL and ISIS for beamtime allocation. AMAL thanks a  Marie-Słodowska Individual Fellowship  in the  Horizon 2020 research and innovation programme  under grant agreement  750971  (" KISS ME "). Data for this study have been deposited at https://datashare. is. ed. ac. uk/handle/ 10283/838.
ACKNOWLEDGEMENTS We acknowledge fruitful discussions with  C. A. Angell ,  V. V. Brazhkin ,  S. V. Buldyrev ,  P. G. Debenedetti ,  M. E. Fisher ,  G. Franseze ,  J. Hrubý ,  P. Poole ,  V. N. Ryzhov ,  H. E. Stanley , and  J. V. Sengers. K. Amman-Winkel  made a valuable comment on the X-ray technique used in ref. [90]. M. A. Anisimov thanks  Swinburne University of Technology, Australia  for funding under the auspices of the Distinguished Visiting Researcher Scheme, the  Institute of Multiscale Science and Technology (Labex iMUST ) supported by the  French ANR , and by the  Russian Foundation for Basic Research  (Grant No. 16-03-00895-a ) during part of his sabbatical leave from the  University of Maryland. The research of  M. Duška  at the  University of Maryland  was supported by the  Young Scientist IAPWS  Fellowship Project " Towards an IAPWS Guideline for the Thermodynamic Properties of Supercooled Heavy Water ".
Calibration method and potential sources of uncertainties We consider a physical phenomenon appearing in several experiments, characterized by an input vector x containing specific quantities. The physical phenomenon can be described by a model containing unknown parameters, defining the vector β. This physical modeling is used in simulations and must provide an accurate description of the experiments. Specific quantities, obtained from experimental results, are summarized in a vector Y. For a given configuration x, the vector Y can be evaluated by different ways: The calibration aims at identifying the most relevant parameters vector β in order to describe at best the experimental data base. When a measurement is performed, there is some experimental error Y mes (x) = Y real (x) + mes (x).
Stability monitoring and antigen content by ELISA Product stability was studied by storing freeze-dried formulations at 5 °C, 25 °C, 37 °C and 45 °C into temperature-controlled incubators for up to 6 months. This forced degradation plan was carefully designed to obtain experimental datasets able to be fitted by the software, with a minimum of 20 data points in total obtained at three or more storage temperatures, with significant degree of reaction reached for the more drastic condition. Hence, time intervals for the forced degradation plan were chosen, as follows: 0, 30, 90 and 180 days at 5 °C; 7, 14, 30, 60, 90 and 180 days at 25 °C; 1, 3, 7, 14, 30, 60 and 180 days at 37 °C, and 1, 3, 7, 14, 30 and 90 days at 45 °C. During studies of the influence of temperature excursions, the two following temperature programs were applied to freeze-dried products before analysis by ELISA: (i) 1 month at 5 °C, followed by 9 months at 25 °C and then returned to 5 °C for 2 months; (ii) 1 month at 5 °C, followed by 6 months at 37 °C and then returned to 5 °C for 4.
Advanced kinetic analyses A general stability modeling procedure using AKTS-Thermokinetics software  [21]  (version 4. 1, AKTS AG, Advanced Kinetics and Technology Solutions, Siders, Switzerland) was already described in detail  [20]  and applied to predict stability for various products constituting vaccines  [14]. Briefly, according to a least-squares regression analysis, the software uses 'one-step' and 'two-step' kinetic models based on the truncated Šesták-Berggren equation ('two-step' in Eq. (  1 ) to determine the function(s) which best fits the experimental data. Hence, reaction rate was described as follows  [20] : = ⎛ ⎝ - ⎞ ⎠ - + ⎛ ⎝ - ⎞ ⎠ - dα dt A E RT α α A E RT α α exp (1 ) exp ( 1 ) n m n m 1 1 1 1 2 2 2 2 (1) With α: the reaction progress, A: pre-exponential factor, E: activation energy, n: reaction order, m: a parameter introduced to take into account the possible autocatalytic behavior of reaction (  1 ) and (  2 ), for the first and second step, respectively.
It was confirmed for all three serotypes, with wAIC and wBIC scores at least equal to 15% (Tables  S1-S3 ), in contrast to the other scores, close to zero, obtained with one-step and consecutive two-step models (Tables  S1-S3 ). Firstly, this means that the adjustable kinetic parameters used with one-step models were not enough to fit datasets, and secondly, that competitive models were preferred to consecutive models for two-step type kinetics. Consequently, the most appropriate kinetic models for variants A, B, and C were competitive two-step models, mathematically described as follows: Variant A: (2) = * ⎛ ⎝ - ⎞ ⎠ - + * ⎛ ⎝ - ⎞ ⎠ - dα dt E RT α E RT α 0. Variant B: (3) = * ⎛ ⎝ - ⎞ ⎠ - + * ⎛ ⎝ - ⎞ ⎠ - dα dt E RT α E RT α 0. Variant C: = * ⎛ ⎝ - ⎞ ⎠ - + * ⎛ ⎝ - ⎞ ⎠ - dα dt E RT α E RT α 0.
Acknowledgments The author would like to express his cordial thanks to  Marie-Julie Diana ,  Elisabeth Vialon  and  Romain Pizatto  for ELISA analyses,  Rayane Seba ,  Laure Cigolotti  and  Manvi Hasija  for their technical support during shipments of samples and  Aure Saulnier  for fruitful discussions and critical scientific review of the manuscript. Editorial assistance with the preparation of the manuscript was provided by  Rebecca Hornby , inScience Communications,  Springer Healthcare. Funding for this assistance was provided by  Sanofi Pasteur. Didier Clenet  is an employee of  Sanofi Pasteur.
Acknowledgements We thank  B. Ruta ,  M. Z. Li ,  H. P. Zhang ,  P. F. Guan  and  R. Blumenfeld  for useful discussions. This work is supported by the  National Basic Research Program of China  ( 973 Program , Grant No. 2015CB856801 ),  National Natural Science Foundation of China  (Grant No. 11525520  and  11804027 ),  China Postdoctoral Science Foundation  (Grant No. 2017M610687 ), and  ANR-15-CE30-0003-02  (France).
5 m; for middle Q data, 6 Å with the detector at 12. 5 m; for high Q data, 6 Å and a 2 m detector distance. The data were fitted using Sasview (Doucet, M. et al. SasView version 4. 1. 2. Zenodo. (2017)). Cryogenic-transmission electron microscopy. Cryo-TEM imaging was performed using a FEI Tecnai 12 TWIN TEM, operating at 100 kV. The TEM grids were treated with plasma air to render the lacey carbon film hydrophilic. A thin film of the sample solution was produced using the Vitrobot with a controlled humidity chamber (FEI). After loading of the sample solution, the lacey carbon grid was blotted using pre-set parameters and plunged instantly into a liquid ethane reservoir precooled by liquid nitrogen. The vitrified samples were transferred to a cryo-holder using a cryo-transfer stage which was cooled by liquid nitrogen.
The cryo-holder temperature was maintained below -170 °C during the imaging process to prevent sublimation of vitreous water. All images were recorded by a SIS Megaview III wide-angle CCD camera. Fiber width measurements were collected using ImageJ software. The average was generated from 70 measurements and the standard deviation used for the error. Histograms were generated using SigmaPlot software. UV-Vis absorption. UV-Vis absorption spectra were collected using a Cary 60 UV-Vis spectrophotometer from Agilent Technologies. Solutions or gels were measured in a 2 mm pathlength quartz cuvette (Hellma Analytics). Spectra were collected from 380-1100 nm at a scan rate of 2 nm/s. For irradiated samples, the cuvettes were irradiated using a 365 nm 700 mA LED, giving around 70 mW intensity of light on the samples, which was measured using a photometer (Thor Labs).
Samples were irradiated for 5 min before the spectra were collected. Samples were allowed to relax back to the original color before another spectrum was collected. Rheology. All rheological measurements were performed using an Anton Paar Physica 301 rheometer, fitted with a chiller to help with the cold temperature measurements. Temperature calibrations were performed between -30 and 80 °C before starting the temperature measurements to ensure the correct temperature was being recorded. All data was collected using a vane (ST10-4V-8. 8/97. 5) and cup geometry (H-24-D) so samples could be prepared in aluminum cups or Sterilin vials to remove any loading issues. There was a gap distance of 1. 5 mm between the bottom of the gel and the cup. A zero force of 0 N was maintained throughout the experiments.
Fourier-transform infrared spectroscopy. Data was recorded using a Thermo Nicolet Is5 (Diamond ATR attachment). The spectra were recorded at 64 scans and a resolution of 4 cm -1. The background was of the empty ATR crystal and spectra were recorded of D 2 O, NDI in D 2 O and the NDI in D 2 O irradiated for 1 min at 365 nm. Spectra subtraction was done using OMNIC 8 software. Cyclic voltammetry. CVs were collected using a PalmSens4 potentiostat with a glassy carbon working electrode, platinum counter electrode and an Ag/AgCl reference. Voltammograms were measured from 0 to -1. 5 to +1 V at various scan rates from 0. 02 to 0. 8 V/s. Voltammetry could be also used on the FTO windows to change the color of the windows, with the reference and counter electrode on one end and the working electrode on the other.
The working electrode was clipped to one end of the cell and the reference and counter electrodes to the other to make it a twoelectrode experiment. The potential could be set to change the color of the cell. Electron paramagnetic resonance. Data were recorded at X-band frequency on a Bruker ELEXSYS E500 spectrometer equipped with an ER 4102ST-O optical transmission resonator. Samples were transferred into soda glass capillary tubes with a diameter of 2 mm. Tubes were sealed at one end and samples filled 20 mm of the capillary tube. Samples were irradiated with a 365 nm LED from LedEngin with an IsoTech CD laboratory power supply at a constant current of 0. 7 A for 2 min. Field-swept spectra represent 5 scan averages collected over a 5 mT sweep width centered at 344. 8 mT, with modulation frequency = 100 kHz, modulation amplitude = 0.