dataesr/scientific-paragraphs-categorization huggingface dataset:
On another hand, the interface position is recovered a posteriori  [11]  and an extension of such a front fixing approach has been proposed in literature in order to introduce and predict the convection phenomenon within the two mushy region phases  [12] [13] [14]. El Ganaoui et al. [15] [16] [17]  improved this method by implementing a timedependent enthalpy-porosity formulation for directional solidification, in which the energy equation is introduced by using the enthalpy variable computing implicitly the thermal Stefan condition at the interface with an implicit procedure. The current work uses COMSOL Multi-physics software, to simulate solidification in Bridgman Vertical (BV) configuration during CdZnTe, which is an II-VI compound crystal growth. To the best knowledge of the authors, it seems that thermophysical properties such as thermal diffusivity, heat capacity,      From this point of view, the main idea of the current investigation is to control the shape interface, in solidification process.
Numerical tools For solving the mathematical model industrial software has been used, namely COMSOL which essentially integrated MATLAB which includes mathematics and computation; data acquisition; modeling, algorithm development; simulation, and prototyping; data analysis, exploration, and visualization. On another hand, the MATLAB language is particularly well suited to designing predictive mathematical models and developing specific algorithms  [20, 21]. Where, these algorithms contain operations for linear algebra, partial differential, matrix manipulation, or differential equation solving, data analysis and statistics. First results concerned the validation of the computational approach versus the mathematical model. For that a test validation has been defined and conducted. It refers to available experimental results with considering shaped solid/liquid interface.
Conclusion In the current investigation, solidification of the singular CdZnTe in axisymetric Vertical Bridgman (VB) cavity is studied. All numerical computations are obtained using COMSOL Multiphysics software, which is based on finite elements method (FEM) with ALE (Arbitrary Lagrangian-Euleurian) formulation allowing treating the interface displacement. The visualization of temperature fields in terms of isotherms and spatial and temporal profiles and according to the solidification of CdZnTe is presented and discussed with the help of COMSOL to provide a better understanding of phase change. Especial attention has been attributed to study the thermo-physical properties of CdZnTe in each domain (solid or liquid) such as the thermal diffusivity a, the specific heat Cp (at constant pressure), and the thermal conductivity, which are measured for both solid and liquid CdZnTe.
For positive controls, 50 ng. mL 21 of rat NGF was added in differentiation medium. Statistical analysis was performed using InStat software (Software Inc. , GraphPad, San Diego, CA). Values were expressed as mean 6 standard deviation (mean 6 SD). Neurites per cell and neurites lenght from each group were compared using nonparametric ANOVA (Kruskal-Wallis Test and Tukey post hos test). A p-value less than 0. 05 was considered to be significant. 9 DRGs extraction and cells culture DRG cells were obtained from young male Sprague Dawley rats (1-3 months old) euthanized with pentobarbital sodium. All procedures were carried out in compliance with the Ethical Committee and the Veterinary Authorities of France in accordance with the European Communities Council Directive of 22 September 2010 (2010/63/UE).
The samples were then rinsed with PBS, mounted in Mowiol and observed using a LEICA DMI 6000 microscope or a LSM 710 scanning confocal microscope which images were analyzed with 'Zen 2011' software. 12 Morphometric and statistical analysis Images were analyzed using Mathworks' Matlab software. Low signal was filtered out using a threshold based on intensity (the silk fibers and background noise present lower pixel intensities than the cell bodies and neurites). In each image, the number of cells was determined using the signal from DAPI staining. Then each neurite was isolated using the tubulin staining and its length was measured using the 'bwmorph' and 'regionprops' functions of the Matlab Image Processing Toolbox. This step was carried out by skeletinization of the neurite, and if necessary manual cropping of superfluous branches.
Artifacts and signal from cellular debris were eliminated from analysis using the 'bwareaopen' function of the Matlab Image Processing Toolbox. The results were interpreted by determining the average number of neurites per cell and the average neurite length. For the average number of neurites per cell, average neurite length and glia/neurons ratio data 6 SEM from 3 independent experiments were analyzed for each condition and at least 90 cells have been analyzed per condition. Statistical analyses were performed with 'InStat' software, using the Mann-Whitney unpaired with two-tailed P test.
Acknowledgments The authors would like to thank  Patrick Villechaise  at the  Institut Pprime -UPR 3346  ( ENSMA, Poitiers, France ) for the EBSD analysis and fruitful discussions during this study. The present research work has been supported by  International Campus on Safety and Intermodality in Transportation (CISIT)  the  Nord-Pas-de-Calais Region , the  European Community , the  Ministry of Higher Education and Research , and the  National Center for Scientific Research. The authors gratefully acknowledge the support of these institutions.
Experimental Section The simulations have been done using RSOFT full-wave FDTD software, using periodic boundary condition for lateral ( X and Y ) axis and perfectly matched layer condition for vertical axis. The size of the calculation mesh was about 20 nm (round in order to have an integral number of calculation cells in a PC cell in all directions). We used broadband sources for the spectral simulation and a continuous one for spatial distribution of fi eld of the modes. Q factor analyses were performed using harmonic inversion software Harminv. The layer of QDs has been simulated assuming an equivalent layer of 27% PbSe and 73% oleate shell on all the surfaces of the PC. The PbSe QDs were synthesized by heating a mixture of PbO (4. 00 mmol), oleic acid (10. 00 mmol), and 1-octadecene (Aldrich) at 150 °C.
Acknowledgements The authors acknowledge  Stewart Burt  for the artwork, and  Josep Canet , from the  University of Valencia , for his assistance with the equipment for photoluminescence, as well as the funding from the  Engineering and Physical Sciences Research Council (EPSRC)  project " Luminescent Lanthanide Layers for Enhanced Photovoltaic Performance (LEAP) " ( EP/ I013245/1 ), the  European Community 's  Seventh Framework Program  ( FP7/2007-2013)  under grant agreement no. [246200], the  Generalitat Valenciana  and  Spanish MINECO  under Grants  PROMETEOII/2014/059  and  TEC2011-29120-C05-01.
SEM was used to investigate the morphology of cells seeded directly onto the specimens' surface. Briefly, after fixing, dehydration with the ascendant ethanol scale (70%, 80%, 90%, 100%), and hexamethyldisilazane, the specimens were mounted on aluminum stubs, using a conductive carbon tape, coated with a thin carbon layer (10 nm) and observed with a StereoScan 360 SEM (AbCam) at 10 kV with various magnifications, using secondary electrons. Furthermore, the cells morphology was visually investigated after one week of cultivation by light microscopy (Leica AF 6500, Leica Microsystems). Statistical analysis was done using Levene's test and ANOVA one-ways followed by Sheffè's post-hoc test using IBM ® SPSS ® Statistics 20 (IBM).
Acknowledgments This work was supported by  CAMPUS FRANCE-Centre  français pour l'accueilet les échanges internationaux in the frame PHC IMHOTEP No. 25639NH  and  20729TH. The authors take pleasure in acknowledging  Guy Schmerber ,  Dris Ihiawa  krim,  Jacques Faerber  for XRD, TEM, and SEM characterization respectively.
They also benefit from being implemented in some widely-used simulation softwares like GULP  44  and LAMMPS  45  codes. In this paper, we use MD simulations with the AIREBO potential to study the behaviour of single SV and DV defects. After a short methodological section, we first thoroughly evaluate the validity of the AIREBO potential to describe SV and DV defects in graphene by comparing their formation and migration energies to ESC values from the litterature and give an account of possible size effects. We then present results from MD simulations at elevated temperatures allowing charectirizing the equilibrium distribution of the different reconstructions of DVs and their dynamical properties. These simulations also show the occasionnal formation of many other reconstructions. In the last part of the paper we discuss the possibility of having some of these new structures observed in a real experiment.
At 3000 K, a bare DV (Figure  5e ) evolves to a 5-8-  We comment now on average properties computed from the different trajectories performed in this work (see Table  1 ). The self-diffusivity coefficients D s , computed from the simulations, are plotted with respect to the temperature in Figure  6. Data collected at 3000 K for DV defects were not sufficient to obtain a reasonnable estimate of D s at this temperature. Linear fit to the data were computed according to the Arrhenius equation ln(D s ) = ln(k 0 ) + E a /k B T where k 0 is the pre-exponential factor, E a the activation energy, k B the Boltzmann contant and T the temperature.
Acknowledgement Access to the computer resources of the  Mesocentre de Calcul Intensif en Aquitaine (MCIA)  is gratefully acknowledged. This work was supported by the  Agence Nationale de la Recherche  through the  PyroMaN (Pyrocabon Matrices at the Nanoscale)  project (Grant  ANR 2010 BLAN 0929 01 ) and by the  Aerospace Valley Foundation.
Finally, all samples were incubated in a protein denaturating buffer (Tris 50 nM pH 6. 8, bromophenol blue 0. 1%, sodium dodecyl sulfate [SDS] 2%, β-mercaptoethanol 5%, glycerol 10%) for 5 minutes at 95°C, and proteins were separated by using 8% sodium dodecyl sulfate-polyacrylamide gel (SDS-PAGE). Protein bands were colored with Silver ® protein stain (Thermo Fisher Scientific). For mass spectrometry analysis, gel migration was stopped after a few millimeters after concentration of the proteins. Excised gel bands containing the mixture of proteins were analyzed on an LTQ Orbitrap Velos (Thermo Fisher Scientific) coupled online to an Ultimate 3,000 nano LC system (Dionex, Sunnyvale, CA, USA). Eluting peptides were subjected to data-dependent collision-induced dissociation fragmentation. The acquired raw data were processed with Proteome Discoverer (Thermo Fisher Scientific) and submitted to the Mascot (Matrix Science) search engine.
In contrast, paclitaxel at 100 nM and after 6 hours of treatment induced an unattached and round cell phenotype (Figure  4Bc ). In addition, microtubule bundles and pseudoasters were detectable in cells (Figure  4B ), which is typical of microtubule-stabilizing agents. 40 In parallel, H 2 O 2 levels were measured by the H 2 -DCF-DA assay. Au-NPs did not induce any increase in reactive oxygen species production in U87-MG cells (data Biological characterization of Au-NPw, Au-NPp, and Au-NPd Our previous experiments have been conducted with Au-NPs incubated in complete cell culture medium containing 10% FBS. The concentration and the nature of serum were chosen according to the protocols used in pharmacological studies. To fully characterize these promising Au-NPs in vitro, we determined their size, their charge, and the composition of the protein corona of the three types of Au-NPs in this biological medium.
HD and zeta potential Sizes and charges of Au-NPs were measured after incubation in complete cell culture medium for 1 hour, followed by three PBS washes. Data were compared with values obtained at the end of Au-NP synthesis (Table  1 ). HD of Au-NPs was increased by 1. 3-to 2. 1-fold, due to the formation of hard protein corona. Au-NPd showed the largest HD (86±9 nm), whereas the HD of Au-NPw and Au-NPp was around 60 nm (57±6 nm and 62±3 nm, respectively). This statistically significant difference (P0. 05) suggested that Au-NPd bound a higher number of proteins or larger proteins. The surface charge of the three Au-NPs became less negative after incubation in biological medium, with values between -12. 6±1. 1 mV and -14. 5±0. 6 mV (P0. 001). These data indicated that proteins bound to the Au surface of NPs to reduce their free energy and to realize a neutralizing effect.
Formation of protein corona and its composition on Au-NPs Protein composition of the corona of the three Au-NPs was analyzed by electrophoresis and mass spectrometry. SDS-PAGE results indicated that a protein corona was formed at 30 minutes of incubation and it did not distinctly change after 1-hour and 24-hour incubation (Figure  5A ). Indeed, the amount and profile of proteins look similar, as shown by ImageJ software analysis (Figure  5B ). In order to anticipate their future uses as nanocarriers, we identify the proteins of corona formed on each Au-NP. NanoLC-ESI-Orbitrap analysis and identification of proteins from the Mascot algorithm showed some differences between Au-NPs (Table  S1 ). About 100 different proteins were adsorbed on Au-NPd, against 80 for Au-NPw and Au-NPp (Figure  5C ).
Acknowledgments This work has been carried out thanks to the support of the  French National Research Agency (ANR)  Lasernanobio  10Blan 919 03 ,  DGOS (SIRIC label) INCa-DGOS-Inserm 6038 , and  A*MIDEX  project (No. ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Government program , managed by the  ANR. Transmission electron microscopy analysis, in solution and in cells, was performed in the  Department of Electron Microscopy (health campus Timone)  under the supervision of Joël Courageot. The MS analyses were carried out in the Proteomic Platform (PIT2) of UMR INSERM 911 (MaP platform). The authors wish to thank  Nathalie Ferté  for preparation of samples and preliminary DLS measurements at CINaM and  Caroline Orneto (Laboratory of Industrial Galenic Pharmacy and Cosmetology, Faculty of Pharmacy)  for help in the DLS and zeta potential measurements.
A non-linear identification method has been used out to obtain the parameters. The determination of model parameters is based on the use of test samples under a simplified strain state. The unknown model parameters are obtained using curve fittings from experimental data. The corresponding identification errors are also taken into account to analyze the results. Numerous compression tests were performed to obtain a sufficient number of experimental results for parameter identification. The determination of these parameters through foam testing is important for the provision of data for the simulation of their service performance, product design and quality control.
For the sake of minimization of the noise contribution, the maximum experimental response force of foam had to be slightly less than the load cell maximum capacity. The test conditions including the strain rate, the maximum compression level, the number of cycles, the sampling period and the test mechanical parameters exported were controlled using the BLUEHILL software configuration window. At the beginning of the experiment, the samples were set into position on the bottom plate. The test started when the upper plate began to affect the foams and then the upper block moved down to compress the samples to the final level. The final compression ratio was 80% of the original thickness. At the end of the loading process, the upper block took a converse compression direction and returned to its initial position.
The software BLUEHILL recorded all test data such as time, stress, strain, force and displacement. Each specimen had been quasi-statically compressed and then decompressed at a constant speed during all the test process. At the beginning of the test, 15 test samples for each foam at three strain rates were taken for the preliminary test. Then, equation  (22)  helped to determine the minimum number of test samples which were to ensure the statistical quality of the parameters. The quasi-static compression test conditions and the minimum number of tests are given in Table  2 ; more details will be given in the discussion section.
Parameter optimization approach Optimization methods are used as basic tools to identify both viscoelastic parameters and elastic parameters. The stress-strain experimental data can be obtained from the uniaxial compression tests. The mean square error between the experimental data and model data can be calculated using a function :  (21)  where is the model stress and is the experimental stress. The objective of the optimization is to find the best combination of model parameters which minimize function. MATLAB's optimization toolbox is considered here for minimization. There are deterministic and random methods for optimization  [40]. The trust region reflective, Levenberg-Marquardt and Gradient methods are three examples of deterministic methods which are effective when the objective function (function to optimize) changes rapidly.
Ceil(x) is a function to round to the smallest integer not less than x in MATLAB. The Student law is used for all parameters in this paper. In the identification method section, a difference-stress method is used to identify the viscoelastic parameters based on the symmetry of the elastic stress. So, it is necessary to check this assumption. Figure  9  indicates that the model elastic stress verifies the elastic symmetry. Except for the standard deviation analysis, the model results also show a good agreement with the experimental data. According to Fig. 10 , the Ogden-hyperelastic model curves lie between the minimum and maximum experimental envelopes. This is another criterion for the validation of the parameters.
AtPSS1 is required for full synapsis and bivalent formation in meiosis A previous report showed that mutation of the rice class I kinesin I (named OsPSS1) leads to meiotic defects  [38]. Reciprocal BLAST analysis and comprehensive sequence analysis of plant kinesins  [39]  unambiguously identified the product encoded by the Arabidopsis At3g63480 gene as the only putative orthologue of OsPSS1. The two proteins share high amino acid sequence identity (59%). We identified three T-DNA insertion lines from the public collections: Atpss1-1, Atpss1-2 and Atpss1-3. Insertion of the TDNA in these loci was confirmed by sequencing the flanking sequences (Figure  1 ). Homozygous plants for all three lines have the same phenotype: normal vegetative growth but decreased fertility, as shown by reduced seed set (5566 seeds per silique for wild type versus 2765 for Atpss1-1) and reduced pollen viability (Alexander staining, Figure  S1 ).
Genetic analyses The six intervals tested in this study correspond to intervals I1b and I1c (both located at the top of chromosome 1), I2a and I2b (both located at the bottom of chromosome 2), and I5c and I5d (both located at the top of chromosome 5) described in  [64]. Tetrad analyses were carried out as described in  [46]. The resulting tetrad data (Table  S1a ) were analyzed using the Perkins mapping equation. All double mutants were obtained by crossing plants, which were heterozygous for each mutation. The resulting hybrids were self-pollinated. PCR screening was then used to identify plants in the F2 progeny that were homozygous for both mutations.
Microscopy Chromosome spreads of male meiocytes were prepared and stained with DAPI as described in  [40]. Chromosome spreads for immunocytology was performed according to  [42]. Observations were made using a Leica (http://www. leica. com) DM RXA2 microscope or a Zeiss (http://www. zeiss. fr) Axio Imager 2 microscope; photographs were taken using a CoolSNAP HQ (Roper, http://www. roperscientific. com) camera driven by OpenLAB 4. 0. 4 software or a Zeiss camera AxioCam MR driven by Axiovision 4. 7. All images were further processed with OpenLAB 4. 0. 4, Axiovision 4. 7, or AdobePhotoshop 7. 0 (http://www. adobe. com).
Complementation tests A 5 kb AtPSS1 genomic fragment containing 1. 5 kb of promoter region and the complete AtPSS1 gene was amplified using specific primers flanked by AttB1 and AttB2 sites (Table  S2 ), cloned into Gateway vector pDONR207 using BP recombination (Invitrogen), and sequenced. Directed mutagenesis was performed using the Quickchange Site-Directed Mutagenesis Kit (Stratagene). The mutagenic primers used to generate the AtPSS1-R293H (Arg codon cgcRHis codon cac) are shown in Table  S2. A LR reaction between the resulting vectors and the pGWB1 destination binary vector was performed. Table  S1  Tetrad analysis. (A) Tetrad raw data set. The FTL system relies on transgenic markers conferring cyan, yellow or red fluorescence of pollen grains within tetrads. Drawings above each column represent the different distribution possibilities of markers among the four chromatids and the corresponding distribution of colors in the tetrad, according to the nomenclature of Berchowitz and Copenhaver  [46].
For each pair of intervals (e. g. I1b and I1c are two adjacent intervals on chromosome 1) and each genotype the observed number of each type of tetrad is given. (B) Interference analysis. Inter-interval interference was measured by comparing the genetic size of an interval (d, Perkins equation, cM) when a crossover occurs in an adjacent interval to the genetic size of the same interval when no crossover occurs in the adjacent interval. The ratio of these two distances, called the interference ratio (IR), gives a measurement of the strength of interference between two intervals  [46]  (e. g. IR I2bI2a = (d(I2b) with CO in I2a)/ (d(I2b) without CO in I2a)). The more this interference ratio is inferior to 1, the stronger interference is. Using the raw data from table  S1A , calculations and statistical analyses have been performed according to Berchowitz and Copenhaver  [46]  and Stahl Lab Online tools (http://www.
Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based on remote sensing: (1) an empirical relationship method with a growing season-integrated Normalized Difference Vegetation Index NDVI, (2) the Kumar-Monteith efficiency model, and (3) a forced-coupling method with a sugarcane crop model (MOSICAS) and satellite-derived fraction of absorbed photosynthetically active radiation. These models were compared with the crop model alone and discussed to provide recommendations for a satellite-based system for the estimation of yield at the field scale. Results showed that the linear empirical model produced the best results (RMSE = 10.
Coupling methods that consist of data from remote sensing integrated into crop models provide a solution for crop development monitoring and biomass estimation. They combine actual and exhaustive observations with the mathematical conceptualization of the model. Different coupling methods have been reported  [15, 18, 19]  and can be summarized into recalibration methods and forcing methods. To our knowledge, few attempts have been made to use coupling methods with a sugarcane dedicated model. In  [20] , researchers used a forcing method with the MOSICAS sugarcane model to improve the accuracy of yield estimation. Although numerous studies have described how remote sensing data can be used to estimate a crop's ecophysiological variables, including biomass production, to our knowledge, no attempt has been made to directly compare the remote sensing methods on a unique dataset.