dataesr/scientific-paragraphs-categorization huggingface dataset:
Graphical presentation of the results Once the calculations needed for an umbrella review and the stratification of the evidence are completed, users can obtain a graphical presentation of the results using the forest() function. Users can improve the basic figure by using the arguments of the forest() function. For this example, we generate a forest plot that contains information on the stratification of evidence (according to the personalised classification), and in which two columns and a title for the plot have been added (). In the JAMOVI module and in the browser-based app, a basic forest plot is automatically generated and can be customized (see figures  3E  and 4E ).
DISCUSSION Umbrella reviews are an emerging type of evidence synthesis that summarises and stratifies the quality or strength of the evidence from previous systematic reviews or meta-analyses conducted on a given topic. The metaumbrella suite presented in this manuscript is the first ensemble suite including specific tools dedicated to data analysis in umbrella reviews with stratification of the evidence. This comprehensive suite includes an R package, which requires mastering a programming language, and two associated open-access, GUI-based platforms: a JAMOVI module and a browser-based application (https://metaumbrella. org/). These two GUI-based platforms allow users to access the functions of the R metaumbrella package with a few mouse clicks, thus enabling them to a prompt completion of umbrella reviews.
As umbrella reviews are a relatively new approach compared with standard systematic reviews or meta-analyses, many developments are still ongoing. For example, a growing number of umbrella reviews focus on meta-analyses of prevalence or include network meta-analyses. In addition, new tools, such as Graphical Representation of Overlap for OVErviews (GROOVE)  30  to identify the overlap of primary studies between reviews, are emerging. These developments, which are currently not available in metaumbrella, may constitute avenues for improvement in the future and could easily be incorporated in new versions of metaumbrella. Contributors CJG and AS drafted the manuscript and performed the analyses. PF-P and JR critically revised the manuscript and reviewed the analyses.
This contribution synthesises the PIXE and ICP-AES elemental analyses carried out on a large dataset, comprising of 300 copper-alloy objects dating mainly to the Late Middle Ages. The objects are drawn from heritage institutions and archaeological contexts in various regions between the Meuse and the Loire rivers (France and Belgium). This investigation focuses on the main elements that make up the alloys (Cu, Zn, Sn, Pb). Past studies have often defined the composition of an alloy as an intentional choice, however in this paper we aim to identify the constraints in alloy manufacture in a bid to understand the degree of freedom enjoyed by craftsmen during this period. We will also evaluate to what degree the constraints imposed by technique, economic conditions and social requirement, affected production in a complex multifactorial system.
Ternary diagrams help to visualise the link between object type and the relative contents of Sn, Zn and Pb in the alloy. A standard 1-way ANOVA test is performed on the data (at the confidence level alpha = 1%), to check if the variable "type of object" impacts on the average concentrations in Sn, Zn and Pb. Factorial analysis of mixed data (FAMD) is performed in R  [44] , on the reduced centred dataset, and applied to cast objects (n = 229), excluding cast items of undetermined function or for which the functional category is poorly represented (n = 26). Our aim was to examine the contents of Cu, Sn, Zn and Pb in relation to three categorical variables: functional area, production mode and dating before or after 1300. Following this FAMD, a hierarchical clustering on the main components was performed (Ward's method) and a number of classes was defined according to the gain in inertia.
Computational details First-principles calculations of structural properties and phonons are performed with the Quantum ESPRESSO package  22, 40, 41  , by combining DFT and DFPT. We use the PBE exchange-correlation functionals for all materials, with the exception of bulk wurtzite GaAs for which we use norm-conserving pseudopotentials within the local density approximation from the Original QE PP Library. For 1D-DFT/DFPT calculations, 1D periodic-boundary conditions (i. e. , the 1D cutoff and the 1D phonon Fourier-interpolation based on the analytical model) are applied to properly describe linear response to a phonon perturbation  26. The pseudopotentials (except for the ones used for bulk GaAs) are taken from the Standard Solid-State Pseudopotentials (SSSP) library (precision version 1. 1)  49  and the wave-function and charge density energy cutoff have been selected accordingly: 110 and 440 Ry for the chain, 80 and 440 Ry for nanotubes, and 90 and 720 Ry for the GaAs nanowire.
ACKNOWLEDGEMENTS This work has been inspired by the experimental results from ref. 28. We acknowledge funding from the  Swiss National Science Foundation (SNSF)  and its  National Centre of Competence in Research MARVEL on  " Computational Design and Discovery of Novel Materials " (grant number  182892 , N. R. , N. M. ). We acknowledge computational support from the  Swiss National Supercomputing Centre CSCS  under project ID mr24. Fruitful discussions with  Anna Fontcuberta i Morral  and  Francesco Libbi  are also gratefully acknowledged.
Results and Discussion The crystal structures of 1 and 2 were determined from singlecrystal X-ray diffraction data. Details about data collection and structure refinements are summarized in Table  1. The crystal structure of 1 and 2 are built up from [{Mo 6 Br i 6 Se i 2 }(CN) a 6 ] 4À molecular cluster unit (Figure  1 ) wherein the octahedral Mo 6 clusters are face-capped by six bromine ligands and two selenium ligands randomly distributed on the inner positions. It is worth noting that the presence of both bromine and selenium elements could not be distinguished by X-ray diffraction analyses but has been clearly observed and quantified by EDS analyses. Thus, the value of the Br/Se ratio was fixed at 0. 75 : 0. 25 during the refinement according to chemical analyses. The average MoÀ Mo distances are 2.
Crystal structure determinations Crystallographic data, details on data collections and refinement parameters of the crystal structures are summarized in Table  1. Singlecrystal X-ray diffraction data were collected at 296 K on an Agilent Xcalibur diffractometer (Nikolaev Institute of Inorganic Chemistry of Novosibirsk) and processed with the CrysAlisPro program package  [18]  for 1. For 2, data were collected at 150 K on a D8 VENTURE Bruker AXS diffractometer and processed with the APEX 3  [19]  program suite (CDIFX, Rennes). For both compounds the X-ray wavelength used was the MoÀ Kα (λ= 0. 71073 Å). Frame integration, data reduction and absorption corrections were performed using the CrysAlisPro program package for 1. For 2, data reduction were carried out with the program SAINT. [20] he program SADABS  [21]  was then employed for multiscan-type absorption corrections.
For both compounds, the structures was determined by dual-space method using the SHELXT program,  [22]  and refined with full-matrix least-square methods based on F 2 (SHELXL-2014)  [23]  with the aid of the WinGX plateform. [24] The final refinements included anisotropic displacement parameters for the non-hydrogen atoms. For both compounds the positions of the hydrogen atoms on the salen groups were idealized by HFIX command. The drawing of the crystal structures were made with Diamond software. [25] Crystallographic data (excluding structure factors) for the structures of 1 and 2 reported in this paper have been deposited with the Cambridge Crystallographic Data Centre as supplementary publication no. CCDC 2242274 and CCDC 2242275 respectively. Copies of the data can be obtained free of charge on application to CCDC, 12 Union Road, Cambridge CB2 1EZ, UK [fax.
Acknowledgements The research was supported by the  Ministry of Science and Higher Education of the Russian Federation. Gilles Daigre  thanks the  International Project Laboratory CLUSPOM  ( 2014-2023 ) as well as "L' Ecole des Docteurs de l'Université Bretagne Loire" and the "Conseil Regional de Bretagne"  for the scholarship mobility at the  Nikolaev Institute of Inorganic Chemistry of Novosibirsk. The authors also thank the  "Centre de diffractométrie X" of the Institute of Chemical Science of Rennes  for single-crystal X-ray diffraction data collection.
In addition, the emission spectra were further corrected for the internal filter effect. The spectral changes were interpreted using the HypSpec 2014 program  [29]. Time-resolved fluorescence intensity decays were obtained using the single-photon timing method with laser excitation and microchannel plate detection, with the set-up already described  [30]. The excitation wavelength used was at the maximum absorption of the calixarene and the emission wavelength at the maximum emission, using a right-angle geometry. Decay data analysis with a sum of exponentials was achieved by means of a Microsoft Excel spreadsheet specially designed for lifetime analysis that considers the convolution with the IRF.
Acknowledgments: We thank  Vienna Drosophila Resource Center  for providing fly stocks. We thank  Jie Shen  for his gracious gifts of Drosophila stocks. We are grateful to the  Core Facility of Drosophila Resource and Technology  at the  Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences  for fly stocks. We also thank the  Scientific Instrument Center of Shanxi University  for confocal microscope technical assistance.
Acknowledgements The authors thank the  QUEFORMAL  project for the financial support. The  QUEFORMAL  project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under the  Marie Skłodowska-Curie  grant agreement no. 829035. We thank the  ISCRA programme at the   Cineca supercomputer center (Bologna, Italy)  for providing computational resources.
For the ADF-STEM measurement, the pixel dwell time was set to 20 μs and the chosen camera length was 8 cm, corresponding to a collection semiangle of 35-180 mrad. During ADF-STEM characterizations, the beam current density on the fluorescence screen was about 0. 2 pA cm -1. The SWCNT samples were loaded onto the lacey TEM grid by drop-casting. The TEM grids were baked in an vacuum oven at 100-150 °C for 12 h prior to characterization in order to remove contaminants such as hydrocarbon molecules. Image analyses were carried out in the Gatan Microscopy Suite (GMS) software. AFM Imaging. The sample height and morphology of the material were studied by a Bruker Dimension Icon atomic force microscope. Measurements were carried out under peak force tapping mode at a tapping rate of 2 kHz, with an Al-coated SiN cantilever (tip radius = 2-12 nm, spring constant = 40 N m -1 , resonant frequency = ∼70 kHz, length = ∼115 μm, and width = ∼25 μm) applied as the probe.
The data were analyzed in the CasaXPS package using Shirley backgrounds and mixed Gaussian-Lorentzian (Voigt) line shapes, with asymmetry parameters where appropriate. Due to surface charging during the experiment, the samples had to be flooded with a beam of low-energy electrons in order to keep the surface from becoming positively charged. This necessitated subsequent referencing of the binding energy scale to the C-C/C-H adventitious C component at 284. 6 eV. For compositional analysis, the analyzer transmission function was determined using clean metallic foils to determine the detection efficiency across the full binding energy range. Steady-State Spectroscopy. UV-vis-NIR (260-1800 nm or 4. 77-0. 69 eV) and far-infrared (0. 2-3 THz or 0. 8-12. 4 meV) absorbance of the material was characterized by a PerkinElmer Lambda1050 spectrometer and a THz-TDS setup where photoconductive antenna and ZnTe were served as the emitter and detection crystal, respectively.
■ ACKNOWLEDGMENTS J. S. is indebted to the  Engineering and Physical Sciences Research Council  ( EPSRC; Swindon, U. K. ) for support from Grant  EP/R019428/1 , while M. W. and B. B. acknowledge financial support from the  EPSRC-funded Warwick Analytical Science Centre  ( EP/V007688/1 ). The authors acknowledge use of the  Warwick Centre for Ultrafast Spectroscopy Research Technology Platform (RTP) facility , the  Electron Microscopy RTP , and the  Spectroscopy RTP at the University of Warwick.
Small-angle X-ray scattering (SAXS). The structural properties of the gel samples were investigated using small-angle X-ray scattering measurements carried out on the ID02 beamline at the European Synchrotron Radiation Facility (ESRF, Grenoble, France)  48  where an incident X-ray beam wavelength of 0. 1 nm collimated to a vertical size of 50 µm and an horizontal size of 100 µm. The sample in the liquid state was introduced in a borosilicate capillary tube of diameter 2 mm and left to cool down to T = 20 °C. SAXS measurements were conducted on the gel state at T = 20°C. The two-dimensional scattering patterns were collected using an Eiger2 4M pixel array detector. The following data reduction procedure is described elsewhere. 49  The 2D scattering pattern is isotropic. We therefore carried out an azymutal averaging of the 2D scattering pattern and worked on the one-dimensional spectrum I(q) where q is defined as the modulus of scattering vector.
Microscopy, AFM, and SAXS. The gel structure was also determined using a combination of bright-field microscopy, AFM, and SAXS measurements. One key conclusion drawn from the data shown in Figure  6  is that the investigated gels display a very well-defined hierarchical structure. At the macroscopic level, microscope images recorded over time for a hot dispersion subjected to cooling from 90°C to 20°C at a rate of 0. 6°C/s (Figure  6A ) revealed the nucleation and growth spheroidal domains with dendrit-like internal structure at the micrometer scale (Figure  6A ). 50, 51  As the temperature is decreased, the size of these spheroidal domains increases up to the point where they interpenetrate, thus forming a percolating network. Such a percolation most likely corresponds to the point where the sample becomes a gel.
This value of R2 = 6000 Ω was also confirmed by electrochemical measurements (see Figure  S11 ) from the slope of the i= f(η) curves at η ~ 0 V. Taken together, these impedance data (Figures 3 and Figure  8 ) thus reveal that the investigated gels are very efficient ionic conductors, as good as reference electrolyte solutions. They also suggest that the presence of redox-active viologen units provides the gels with electron-conducting capabilities. Additional studies are currently under way to further support this conclusion. These conductive gels have also been characterized by electrochemical methods. Cyclic voltammetry measurements were carried out in a home-made three electrodes cell on gels formed in situ after cooling a mixture of 1(Br)2, TBAI and LiTFSI in pentanol. The CV curve recorded at a platinum grid working electrode (Figure  9 ) shows two reduction waves at [E1/2] 1 = -0.
ACKNOWLEDGMENT The authors thank the  Ecole Normale Supérieure de Lyon (ENSL)  and the  Centre National de la Recherche Scientifique (CNRS)  for financial, logistical, and administrative supports. The authors thank the  ESRF  for beamtime at the beamline ID02 and  Theyencheri Narayanan  for discussions and technical support for USAXS measurements. This work was supported by the  LABEX iMUST  ( ANR-10-LABX-0064 ) of  Université de Lyon  within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR) , by the  ANR  ( ANR-CE06-0020-01 ) and by the " Mission pour les initiatives transverses et interdisciplinaires "  MITI CNRS  (Auto-organisation  AAP 2020 ). V. A. thanks  ENSL  for the  CDSN PhD Grant.
Size Exclusion Chromatography (SEC) SEC analysis of copolymers (except P(MTC-co-GalVE) copolymers) was performed on a Tosoh EcoSEC HLC-8320 GPC with two columns from Agilent (PL-gel MIXED-D, 300 × 7. 5 mm, beads diameter: 5 µm, linear part: 400-4 × 10 5 g. mol -1 ). Analyses were performed at 35°C in CHCl3 (HPLC grade) at a flow-rate of 1 mL. min -1. Toluene (0. 3 vol %) was used as flow-rate marker. Samples were filtered with a 0. 22-µm PTFE filter before analysis. The calibration curve was based on poly(methyl methacrylate) (PMMA) standards (Mp = 1 850-1 916 000 g. mol -1 ) from Agilent. The EcoSEC Analysis software enabled the determination of the number-average molar mass Mn, the weight-average molar mass Mw and the dispersity (Ð = Mw/Mn).
SEC analysis of P(MTC-co-GalVE) copolymers was performed on a Viscotek TDA/GPCmax from Malvern with two columns from Agilent (PL PolarGel-M, 300 × 7. 5 mm, bead diameter: 8 µm, linear part: 1 000-500 000 g. mol -1 ). Analyses were performed at 60 °C in DMSO (HPLC grade) with 10 mM LiBr (filtered over 0. 22-µm PTFE filters) at a flow rate of 0. 7 mL. min -1. 2,6-Di-tert-butyl-4-methylphenol (BHT) (0. 3 wt %) was used as flow rate marker. Samples were filtered with a 0. 22-µm PTFE filter before analysis. The calibration curve was based on PMMA standards (Mp = 540-342 900 g. mol -1 ) from Agilent. The OmniSEC software enabled the determination of Mn, Mw and Ð. SEC analysis of the degraded copolymers was performed at 30°C in CHCl3 with trifluoroacetic acid (TFA, 0. 1 vol %) as eluent on a system equipped with two columns from Agilent (PL-gel MIXED-D, 300 × 7.
5 mm, beads diameter: 5 µm, linear part: 400-4 × 10 5 g. mol - 1 ), a differential refractive index detector (Spectrasystem RI-150 from Thermo Electron Corp. ) and a Waters 515 HPLC Pump at a flow rate of 1 mL. min -1. Toluene (0. 3 vol %) was used as flow rate marker. Samples were filtered with a 0. 22-µm PTFE filter before analysis. The calibration curve was based on PMMA standards (Mp = 540-342 900 g. mol -1 ) from Agilent. The OmniSEC software enabled the determination of Mn, Mw and Ð.
Isothermal titration calorimetry (ITC) Recombinant lyophilized LecA was dissolved in water in the presence of 100 µM CaCl2 and degassed. The LecA concentration was determined by measuring the optical density (Nanodrop) by using a theoretical absorbance extinction coefficient of 27 960 M -1. cm -. The nanoparticles were diluted in the same solution and degassed. Reverse ITC experiments (nanoparticles in the cell) were performed at 25 °C, with a VP-ITC MicroCalorimeter from MicroCal Incorporated. Titration was performed with 10 µL injections of LecA solution (1. 74 mM) in the 1. 466 mL cell with nanoparticles solution ([glycoside] = 0. 1 mM) every 300 s. Data were fitted with MicroCal PEAQ ITC analysis software according to standard procedures. Fitted data yielded the stoichiometry (n), the association constant (Ka) and the enthalpy of binding (ΔH).
Acknowledgments We thank the  ANR NanoCardioROP  ( ANR-15-CE08-0019 ) and the  French Ministry of Research , respectively for the financial support of the Master 2 internship and the PhD thesis of TP. AI and EG acknowledge support from  Glyco@Alps  ( ANR-15-IDEX-02 ) and  Labex Arcane/CBH-EUR - GS  ( ANR-17-EURE-0003 ). Stéphanie Denis  (IGPS,  Univ Paris-Saclay)  is acknowledged for her technical assistance in cell culture.
Acknowledgements For financial support of this work, we acknowledge the  CEA ,  CNRS ,  CHARMMMAT Laboratory of Excellence , and the  European Research Council  ( ERC Consolidator Grant  Agreement no. 818260 ). N. L. and L. P. were supported by a fellowship from the  European Union   Horizon 2020 research and innovation program  under grant agreement no. 768919  ( Carbon4PUR  project). T. C. thanks the  Fondation Louis D. -Institut de France  for its support. We thank  Thierry Bernard  ( CEA ) for help in the conception and realization of the autoclaves.
Acknowledgements The authors acknowledge the  Brazilian agencies CNPq/MCTIC ,  CT-INFRA/FINEP/MCTIC , and  CAPES/MEC  (finance code  001 ) for the financial support. André Menezes  would also like to thank  CNPq  (Grant  232680/2014-0 ),  PNPD/CAPES  (Grant  88882. 317938/2019-01 )and  FAPESQ/PB -SSECT  ( 17/2022 ) for the awarded fellowships. Brendan J. Kennedy  acknowledges the support of the  Australian Research Council ,  Australia. Alexandra Ciorita  and  Arpad M. Rostas  greatly acknowledge the  Romanian Ministry of Research, Innovation and Digitalization, Core Program  Project, Romania  PN19 35 02 03.
However, the computational cost of DFT typically scales as O(N 3 ) for metallic systems, which severely limits its direct applicability to the study of extended defects (section 2. 1). As a result, dislocationimpurity studies remain challenging or approximate, requiring resources at or beyond the limits of many practitioners, essentially ruling out the systematic studies required for rational design approaches. Email addresses: petr. grigorev@univ-amu. fr (Petr Grigorev), thomas. swinburne@cnrs. fr (Thomas D  Swinburne)  In this paper, we present and apply a new hybrid simulation method (section 2) that embeds ab initio simulations in much more general simulation geometries than previously possible, using custom-fit machine learning interatomic potentials  [11]  (section 2. 2 ). An open source implementation employing the popular atomic simulation environment (ASE) package  [12]  is freely available online  [13].
The results are presented in supplementary materials  [58]. It can be seen that both dislocation retrained (Θ) as well as segregation retrained (Θ ) potentials maintain perfect description of elastic properties as well as vacancy behaviour in the bulk. Thus we see no signs of overfitting in the constrained domain. We note that similar functionality should be achievable with highly flexible (large capacity) approaches employing e. g. neural networks  [44, 49]  or kernels  [66]  such as GAP  [43]. Kernel methods can have theoretically infinite capacity, which is controlled during the refitting procedure by sparsification and providing the relevant information from the database  [67]. However, this procedure requires special design of the database, some prior knowledge of relevant atomic configurations for a specific physical problems pointed up with the appropriate theoretical tools  [11, 68, 69].
Acknowledgements PG and TDS gratefully recognize support from the  Agence Nationale de Recherche , via the  MeMoPAS  project  ANR-19-CE46-0006-1  and the  Centre Nationale de Recherce Scientifique , via a Jeunes Entrants grant of the  Institut de Physique. This work was granted access to the  HPC resources of IDRIS  under the allocations  A0090910965  and  A0120913455  attributed by  GENCI  and the  Computational Simulation Centre  of the  International Fusion Energy Research Centre in the Rokkasho Fusion Institute of QST (Aomori, Japan) , under the  Broader Approach grant AbIn-Seg. This work has been carried out within the framework of the  EUROfusion consortium  and has received funding from the  Euratom  research and training programme  2019-2020  under grant agreement No  633053. AMG, MCM and TDS acknowledge the support from  TGCC-CCRT computer centres under the allocation 'Grand Challenge ' no.
502 on Topaze. JRK and PG acknowledge support from the  UK Engineering and Physical Sciences Research Council (EPSRC)  under grant numbers  EP/R012474/1  and  EP/R043612/1. Additional support was provided by the  Leverhulme Trust  under grant  RPG-2017-191. We are grateful for computational support from the  UK  national high performance computing service,  ARCHER , for which access was obtained via the  UKCP consortium  and funded by  EPSRC  grant reference  EP/P022065/1. Additional.