dataesr/scientific-paragraphs-categorization huggingface dataset:
We analyzed the same proteinogenic amino acids as those mentioned above for mannitol. No acetate was observed in the supernatant, and the associated export reaction was consequently excluded from this model. Additionally, we exploited specific labeling enrichment of histidine, alanine, and ribulose-5-P and positional isotopomer data of alanine from labeling samples as described in "NMR measurements. " For MFA on methanol, the nonstationary nature of the experiment and the subsequent importance of the pools on flux distributions forced us to explicitly examine most of the reactions of the central metabolism that were lumped for the stationary models. Measurements described above were used to constrain intra-and extracellular pools (see "Sampling and MS analysis of intra-and extracellular pool sizes") and isotopologue profiles (see "Sampling and MS analysis of labeled metabolites") through the influx_si optimization process.
S3 ). On mannitol and arabitol, the precision of the fluxes through malate dehydrogenase (maldh; BMMGA3_RS12590, 1. 1. 1. 37), malic enzyme (mae; BMMGA3_RS12650, 1. 1. 1. 38 or 1. 1. 1. 40), and pyruvate carboxylase (pyc; BMMGA3_RS05255, 6. 4. 1. 1) was not satisfactory. We thus applied a flux minimization analysis (70) included in the influx_si software (i. e. , option sln) to estimate the minimal flux (i. e. , lower bound) values that could be carried through those 3 reactions. Since both the substrate uptake rates (see Table  2 ) and the content of carbon of the substrates (from 1 to 6 carbon atoms) differed widely, the estimated fluxes were normalized as follows. Analysis of arabitol phosphate dehydrogenases AtlD and AtlF. (i) Strains and culture conditions. In this study, Escherichia coli DH5␣ (71) was used as the standard cloning host, and recombinant protein production was carried out with E.
ACKNOWLEDGMENTS We thank MetaToul (www. metatoul. fr),which is part of  MetaboHub  ( ANR-11-INBS-0010 , www. metabohub. fr), for their help in collecting, processing, and interpreting 13 C NMR and MS data. We also thank  Marcus Persicke ,  Maud Heuillet ,  Edern Cahoreau ,  Lindsay Peyriga ,  Pierre Millard ,  Gilles Vieira , and  Jean-Charles Portais  for the insights they provided. This work was supported by  ERA-CoBioTech 's project  C1Pro  ( ANR-17-COBI-0003-05  and  FNR-22023617 ).
The damping property Λ depends on the induction magnitude and the excitation frequency  [20]. Similar to the static identification, the calculation of Λ is performed in each cycle using the discretized diffusion equation (Eq. 13) assuming a constant property through each measured cycle. The dynamic identification requires the knowledge of the static behavior (H s = f (B)) presented in section 4. 1. Eq. 13 calculates the local magnetic flux density vector B from which the average induction is directly derived (B av (t) = h 2 -h 2 Bdz). The identification of Λ consists in minimizing the error between the measured cycle and the numerically calculated cycle using the R-squared method. The optimization method uses a very simple technique presented by Lagarias et al. [23]  and implemented in Matlab.
5b ) and Si (Fig. 5c ). Due to the likely acidic environment existing in this region, normalization to Fig. 4  Chemical composition of corroded ISG obtained by XPS. Data in NaCl adapted from Guo et al. 16  For back side references ("Back Ref" in the figure), data were acquired from one representative area, so no error bar is presented. Error bars are defined as standard error of the mean. Si may be more reliable because other elements are soluble in such conditions. Similar trends were observed for the two normalization methods, where Na, Al, and Zr were depleted in an area that is about 2 mm within the boundary of the crevice. This led to the formation of a more silica-like gel, which is consistent with XPS results. The localized corrosion on ISG at a particular distance from the boundary of the crevice may be associated with the competition between SS dissolution, which is affected by the ohmic potential drop, and the diffusion of metal cations out of the crevice.
ACKNOWLEDGEMENTS This work was supported as part of the  Center for Performance and Design of Nuclear Waste Forms and Containers , an  Energy Frontier Research Center  funded by the  U. S. Department of Energy, Office of Science, Basic Energy Sciences  under Award #  DESC0016584. The authors would like to thank  Dr. Charles Crawford  from  Savannah River National Laboratory  for supplying the  International Simple Glass. The authors would also like to thank  Yutichai Mueanngern  and  Dr. Yehia Khalifa  from  Ohio State University  for their assistance with XPS measurement.
Acknowledgements Financial support was provided by the  National Key Research and Development Program of China  ( 2017YFB0206101 ,  2017YFB0405601 ) and  Strategic Priority Research Program  of the  Chinese Academy of Sciences  ( XDB44010200 ). M. Zhu acknowledges support by the  Hundred Talents Program  ( Chinese Academy of Sciences ) and the  Shanghai Pujiang Talent Program  ( 18PJ1411100 ). J. Robertson  acknowledges support by  EPSRC  (No. EP/ P005152/1 ). H. Li acknowledges support by the  National Natural Science Foundation of China  ( 61704096 ,  61974082 ),  Tsinghua-IDG/McGovern Brain-X program ,  Youth Elite Scientist Sponsorship (YESS) Program of China Association for Science and Technology  (No. 2019QNRC001 ) and fruitful discussion with  Yuzheng Guo  and  Vaclav Drchal.
V. 2. Ab initio density functional approach Calculations were performed with the BigDFT software  [84]  in a supercell that guarantees negligible elastic interactions between the periodic defects  [85, 86]. Due to the large flower clusters we considered, an orthorhombic supercell with 836 atoms was needed. Geometries were considered optimized when the forces on atoms were less than 15 meV/ Å. We used a Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional  [87]  together with Hartwigsen-Goedecker-Hutter pseudopotentials  [88]  and only the Γ point. The formation energy was calculated directly with respect to bare graphene, as flower defects do not contain vacancies or interstitials.
V. 4. Phonon transport simulations Starting from the relaxed coordinates of pristine graphene, we employed Phonopy  [90]  to generate a minimal set of displaced 9 × 9 supercell configurations. We obtained the forces on all atoms in those configurations the DFT package VASP  [91]  with projector-augmented-wave datasets  [92, 93] , the PBE approximation to exchange and correlation  [87] , a plane-wave cutoff of 520 eV and a simulation box with a height of 17 Å to avoid spurious interactions between periodic images of the graphene layers. From these forces, we rebuilt the harmonic force constants needed to obtain the phonon spectrum of graphene. We followed a similar procedure using a minimal set of displaced configurations generated using thirdorder. py [76]  to obtain the third-order force constants for the supercell.
Acknowledgements This work was supported by the  European Union Horizon 2020 Research and Innovation Program  (Grant No. 724139 ). The authors thank the  Pôle de Calcul et de Données Marines (PCDM)  for providing DATARMOR storage and computational resources (http://www. ifremer. fr/pcdm),and the  Institut de Calcul Intensif (ICI)  (Grant No. 2018-P1804060 ).
Design of the Proposed MM Figure  1  shows details of the proposed MM structure. This MM consists of three layers (Figure  1a, b ). The middle layer is the FR-4 dielectric substrate with a real part of the permittivity of 4. 3, a loss tangent of 0. 025 and thickness h of 3. 2 mm. The top and bottom layers are made of copper with an electric conductivity of 5. 8 × 10 7 S/m and thickness of t = 17 μm. The bottom copper layer is a continuous film that plays the role of a perfect reflector, while the top is formed of symmetrical copper resonators with the geometry shown in Figure  1c ; it consists of 15 mm × 15 mm unit cell with a periodicity p = 0. 04 mm (Figure  1d ). Table  1  summarizes the dimensions of the Vshape resonator. The design and the absorption performance of the proposed MM array are obtained while using (CST) Microwave Studio software.
Because the unit cell is periodic in the xy-plane, Floquet port boundaries was applied in the x and y directions, whereas an open boundary is considered in the z direction. Here, the wave propagation vector k is perpendicular to the absorber plane, the magnetic field is parallel to the x-axis and the electric field is parallel to the y-axis for the TE mode, as shown in Figure  1. The design and the absorption performance of the proposed MM array are obtained while using (CST) Microwave Studio software. Because the unit cell is periodic in the xy-plane, Floquet port boundaries was applied in the x and y directions, whereas an open boundary is considered in the z direction. Here, the wave propagation vector k is perpendicular to the absorber plane, the magnetic field is parallel to the x-axis and the electric field is parallel to the y-axis for the TE mode, as shown in Figure  1.
Acknowledgments: This work was supported by the  European Union  through the  European Regional Development Fund (ERDF)  and by the  Ministry of Higher Education and Research, Brittany Région, Côtes d'Armor Département and Saint Brieuc Armor Agglomération  through the  CPER Projects MATECOM and SOPHIE-STICC. This work was also supported by the  Syndicat de Gestion du Pole Universitaire de Saint Brieuc (France). The authors want to thank  J. Sol  for its technical support for the prototypes measurement.
Acknowledgements This work was supported by the  MEXT Program for Promoting the Enhancement of Research Universities in NAIST  and also partly supported by the  JSPS KAKENHI  Grant ( JP26107006 ) in  Scientic Research on Innovative Areas "Photosynergetics". RA thanks the  NAIST Foundation  for nancial support and CJM thanks the  JSPS KAKENHI   Grant-in-Aid for Early-Career Scientists  ( 19K15312 ).
Proteomics The 2D gel based proteomic experiments were essentially carried out as previously described,  26, 27  on independent biological triplicates, except for the acutely exposed cells for which a quadruplicate was obtained. The isoelectric focusing dimension used a linear 4-8 pH gradient, and the second dimension a homogeneous 10% acrylamide gel spanning a molecular mass window of 14-200 kDa. Protein detection was carried out by silver staining. Two-dimensional gel images were analyzed by the Delta2D software, and the spot intensities are expressed as a percentage of the sum of all detected spots, thereby normalizing the intensities between gels. Statistical analysis was carried out by a heteroschedastic Student T-test (also known as Welch T test). For the general analysis a first pass filter of p < 0.
25 was carried out. This allowed decreasing the noise brought by proteins that do not show a consistent variation in the biological phenomena under investigation. For the detailed analysis only proteins showing a p ≤ 0. 05 in one T-test (acutely exposed cells vs. acute control or chronicallyexposed cells vs. chronic control) were selected. Protein identification was carried out by mass spectrometry as described previously. 27 The MS/MS data were interpreted using a local Mascot server with MASCOT 2. 5. 1 algorithm (Matrix Science, London, UK) against UniProtKB/ SwissProt (version 2017_09, 555594 sequences). The research was carried out in all species. Spectra were searched with a mass tolerance of 15 ppm for MS and 0. 05 Da for MS/MS data, allowing a maximum of one trypsin missed cleavage.
Global analysis of the proteomic results From the total protein abundances data obtained from the gel analysis software, a subset of putatively variable proteins (T-test p < 0. 25 in either the acute exposure vs. control or the chronic exposure vs. control comparisons) was extracted. This subset was then tested using the PAST statistical suite. 43 As a first test, a hierarchical clustering was performed, and the results are shown in Fig. 2. This test clearly showed that the cells chronically exposed to silver stood apart from all the other groups, and that the second most important variable was the cell aging, as the chronic control group also stood apart from the two acute groups (control and treated).
Detailed analysis of the proteomic results We extracted the intensity values for the spots that were significantly altered (T-test p ≤ 0. 05) between one of the silver-treated groups and its corresponding control. We thus obtained two subsets, one for the spots changed in the chronic exposure scheme and one for the spots changed in the acute exposure scheme. The two subsets, accounting in total for 152 spots, were then merged into a single table of 85 variable and identified proteins (Table  1 ). The corresponding 2D gel images are shown on Fig. 3  and Fig. S1  and S2. † The mass spectrometry identification parameters are detailed in Table  S2  † and the mass spectrometry data are deposited in Pride under the identifier PXD016379. 44  order to cope with the multiple testing issue, we used several approaches such as the Benjamini-Hochberg approach,  45  the sequential goodness of fit approach  46  and the sequential Fisher test approach.
Acknowledgements This work was funded by the  CNRS, The University of Grenoble , the  University of Strasbourg Unistra , the  Région Alsace , the  French National Research Program for Environmental and Occupational Health of ANSES  ( PNREST 2011/025 ,  Innimunotox Grant ) and the toxicology project of the  CEA  ( Nanostress grant ). This work is a contribution to the  Labex Serenade  (no. ANR-11-LABX-0064 ) funded by the " Investissements d'Avenir" French Government program  of the  French National Research Agency (ANR)  through the A* MIDEX  project (no. ANR-11-IDEX-0001-02 ). This work used the platforms of the  French Proteomic Infrastructure (ProFI)  project (grant  ANR-10-INBS-08-03 ). The authors would like to thank  K. Pernet-Gallay  for help in TEM image acquisition. Lastly, BD thanks the  CNRS  for a handicap PhD fellowship.
Characterization Tools The crystalline structures of all the particles produced were checked by X-ray diffraction (XRD) using a Panalytical X'pert Pro diffractometer, working in the Bragg-Brentano reflection geometry and equipped with a multichannel X'celerator detector and a cobalt X-ray tube operating at 40 kV and 40 mA. The data were analyzed by the Rietveld method, using HighScore software (Panalytical) to determine the cell parameters and the average crystal sizes of the investigated crystalline phases. Detailed microstructural analysis was performed on aqueous suspensions sonicated for few minutes before deposition of a few drops on specific grids for transmission electron microscopy (TEM) or scanning electron microscopy (SEM) observations. TEM was carried out on a JEOL-100-CX II microscope operating at 100 kV, and SEM was carried out on a Zeiss Supra 40 microscope operating at 5 to 10 kV and equipped with an energy dispersive spectrometer (EDS).
The recorded micrographs were analyzed using the ImageJ software, and a lognormal statistical law was applied to the inferred size distribution to determine the average particle size. Using the same suspensions, the hydrodynamic size distribution was determined by dynamic light scattering (DLS) using a Zeta Nanosizer Malvern instrument operating with a laser of 633 nm. This equipment also allows the determination of the zeta potentials (ζ) of the particles. The isoelectric point of these particles was determined through zeta potential measurements while varying the pH between 1 to 13, by adding HCl or NaOH to distilled water. The pH value was exactly measured by a JENCO Electronics Microcomputer pH-VISION 6071 pH-meter. UV-Visible absorption spectroscopy was also used to determine the plasmonic resonance band of all the produced gold-containing particles.
Contributions: This work is conceived by T. T. N. , F. M. and S. A. , prepared by T. T. N. The structural and microstructural characterizations were carried out by T. T. N. SERS experiments were performed by T. T. N. and S. L. -T. The analysis and discussion of all the obtained results were achieved by T. T. N. , F. M. and S. A. The manuscript was written through contributions of all authors. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the French Ministry of Research and National Centre for Scientific Research. ANR (Agence Nationale de la Recherche) and CGI (Commissariat à l'Investissement d'Avenir) are gratefully acknowledged for their financial support of this work through the grant number [ANR-10-LABX-096 and ANR-18-IDEX-0001].
A representation of the experimental setup is shown in Figure  8. The X-ray powder diffraction (XRD) analysis was employed to gain information on the reaction products structure and to understand the kinetic evolution of the synthesis. To ensure the maintenance of an inert atmosphere during the XRD data collection, the samples were placed in an air-tight sample holder, sealed with a thin Kapton foil under argon atmosphere. The XRD analyses were carried out using a Bragg-Brentano rotating anode diffractometer (Smartlab Rigaku) equipped with a copper source (CuKα1 = 1. 5406 Ǻ, CuKα2 = 1. 5443 Ǻ). The patterns were collected step-wise in a 2θ range from 20° into 100°, with a step size of 0. 05 and with a fixed time of 4 s. The XRD patterns were numerically refined by the Rietveld technique using the MAUD software  [40].
The thermal analysis was carried out with a Setaram Labsys TG/DSC under Argon flux at a scanning rate equal to 10 °C min -1 , during a heating scan from 20 to 500 °C. The morphological and elemental analyses of the powders were carried out by scanning electron microscopy (FEI Quanta 200). The X-ray powder diffraction (XRD) analysis was employed to gain information on the reaction products structure and to understand the kinetic evolution of the synthesis. To ensure the maintenance of an inert atmosphere during the XRD data collection, the samples were placed in an air-tight sample holder, sealed with a thin Kapton foil under argon atmosphere. The XRD analyses were carried out using a Bragg-Brentano rotating anode diffractometer (Smartlab Rigaku) equipped with a copper source (Cu Kα1 = 1. 5406 Å, Cu Kα2 = 1.
5443 Å). The patterns were collected step-wise in a 2θ range from 20 • into 100 • , with a step size of 0. 05 and with a fixed time of 4 s. The XRD patterns were numerically refined by the Rietveld technique using the MAUD software  [40]. The thermal analysis was carried out with a Setaram Labsys TG/DSC under Argon flux at a scanning rate equal to 10 • C min -1 , during a heating scan from 20 to 500 • C. The morphological and elemental analyses of the powders were carried out by scanning electron microscopy (FEI Quanta 200).
All authors have read and agreed to the published version of the manuscript. Funding: This work and the activity of L. C. has been supported by a PhD program, MIUR special scholarship, within the joint agreement UNICA-UNISS for the PhD program in Chemical and Technological in Sciences. That PhD has been financed by PON-Ri scholarship (Programma Operativo Nazionale Ricerca e Innovazione 2014-2020, Fondo Sociale Europeo Regione Autonoma Sardegna). S. G. , S. E. , and G. M. acknowledge UNISS for the financial support received within the program "Fondo di Ateneo per la ricerca 2019".
Acknowledgments: The authors acknowledge the support of the  CeSAR UNISS, Centro Servizi di Ateneo per la Ricerca of the University of Sassari , for making available several instrumental techniques to carry out materials characterization. Part of this work has been carried out in the framework of the  ENABLES  Project (http://www. enables-project. eu/),which received funding from the  EU 's  Horizon 2020 research and innovation program  under  GA 730957.
The OECD Nuclear Energy Agency (NEA) Working Party on International Nuclear Data Evaluation Cooperation (WPEC) was established in 1989 to facilitate collaboration in nuclear data activities. Over its thirty year history, different Subgroups have been created to address topics in nearly every aspect of nuclear data, including: experimental measurements, evaluation, validation, model development, quality assurance of databases and the development of software tools. WPEC has recently completed activities on fission yield evaluation, the general nuclear database structure (GNDS) to replace the ENDF-6 format, methods to provide feedback to evaluation, studies of specific capture cross sections, new methods in thermal scattering kernel evaluation and the Collaborative International Evaluated Library Organisation (CIELO) Pilot Project.
Ongoing activities in GNDS application programming interface (API) development, methods for covariance evaluation and quality assurance in nuclear data validation using the International Criticality Safety Benchmark Evaluation Project (ICSBEP) database are complemented by the work of two Expert Groups that oversee the High-Priority Request List (HPRL) for Nuclear Data and the continuous development of the GNDS. New activities on the use of integral experiments for nuclear data validation and adjustment, as well as the use of the Shielding Integral Benchmark Archive and Database (SINBAD) for validation have begun and will be coordinated alongside future Subgroups. After three decades we will review the status of WPEC, how it integrates other collections and activities organised by the NEA and how it dovetails with the initiatives of the IAEA and other bodies to effectively coordinate international activities in nuclear data.
The High Priority Request List The OECD-NEA High Priority Request List (HPRL) for Nuclear Data is a a reference that stimulates the improvement of nuclear data and bridges the gap between data users and producers. A panel of experts reviews and maintains the list under the WPEC Expert Group on the HPRL. Once approved, requests are classified into high-priority, general or special-purpose requests and entered into a relational database that is publicly searchable on the NEA website. The HPRL entries are annually reviewed and further classified based on the progress that has been made in satisfying the request. New publications on evaluations and experiments are recorded within each entry and used to justify the classification. Numerous new requests have been entered into the HPRL since 2017 and others have been identified as 'completed' following the 2018 EG-HPRL review.
9 release. To use the new formats, it was realised that a conduit between the data and codes that would use them was required and Subgroup 43 was established to explore this and prepare designs and implementations. In software terminology, this is known as an Application Programming Interface (API) and defines objects that can be used by multiple different programming languages without focusing on the details of the data formats. By designing this API as an international project the mass duplication of effort to perform this for the many nuclear simulation codes around the world would be avoided (as well as the introduction of many errors along the way). Already, multiple APIs have been desgined and implemented, including tools such as GIDI  [9]  and FUDGE  [10, 11]  that are available with open-source licenses.
In total, more new evaluations were made in SG42 than in all countries in the past 30 years. The new releases of ENDF/B-VIII. 0 [3]  and JEFF-3. 3 [4]  contain more new evaluations from Subgroup 42 than from any other source. In addition to these new evaluations, new software tools such as FLASSH  [12]  have been developed to overcome approximations within the well-known NJOY LEAPR  [13]  module and fully utilise new inputs from Density Functional Theory and Molecular Dynamics calculations.