dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was encouraged and strongly supported by the doctoral theses of  Paul Desmarchelier  and  Luo , under shared supervisions with  Valentina Giordano ,  Anthony Gravouil  and  Konstantinos Termentzidis. Paul Desmarchelier  performed the atomistic calculations of thermal conductivities. I thank  Yaroslav Beltukov  for his efficient contribution to our common work, and  Sébastien Aumaître  for his constant support.
Chemicals. Organophosphorus compound analogues were synthesized by Enamine Ltd. All other chemicals used for kinetic assays were ordered from Sigma-Aldrich. Design and Screening of the Combinatorial Library. Three dimensional structures of SsoPox (PDB ID 2VC5) and BdPTE (PDB ID 1DPM) were superimposed with PyMOL 41 based on their (/β)8 topology similarity. Thirteen residues were targeted for the construction of the combinatorial library, V27, L72, Y97, Y99, R154, T177, R223, L226, L228, C258, W263, W278 and I280. A randomized strategy based on degenerated oligonucleotides was applied for creating genetic diversity. The plasmid library was synthesized by GenScript, using degenerated oligonucleotides procedure to create a randomized and degenerated library. The desired mutations and the corresponding codon are described in Table  S1.
X-ray diffraction data were integrated and scaled with the XDS package (Table  S3  and S4). 46  The phases were obtained performing a molecular replacement with MOLREP or PHASER using the structure of SsoPox-wt (PDB ID 2VC5) as model. 47, 48  The models were built with Coot and refined using REFMAC  49, 50  and PHENIX  51. Structures were validated using Molprobity  52. Structure illustrations were obtained using PyMOL. 48  Structural Analysis. Active site cavities were determined using CAVER Analyst 2. 0 beta 53 and a 1. 4A probe and large probe 3. 00. For IIIC1, these settings resulted in artifactually small multiple cavities. Therefore, calculations were done by allowing a probe up to 4. 00, and the program chose a max probe of 3. 05 for this variant. Average B-factors were calculated using Baverage from the CCP4 program suite v.
In this conformation, a fragment of loop 8 is bound to the bi-metallic active site center and thereby is a nonproductive enzyme conformation (Figure  S9B ). Residue C258 interacts with the cobalt cation and C259 forms a disulfide bridge with the mutated position L72C. Other structures reveal partial loop fragments that could be modelled (Figure  S8 ); but the structures show overall low electronic density to support robust loop modelling. For the more ordered structures with discrete loop conformations, the electron density supports model building for the active site loops (Figure  S10 ). The analysis of the normalized thermal motion B-factor confirms the increased flexibility of the loops 7 and 8 (Figure  3  and Figure  S11 ) as compared to SsoPox-wt (PDB ID 2VC5). This higher mobility is consistent with the observation that all of the improved variants are destabilized compared to SsoPox-wt, as shown by their reduced melting temperature values (Table  S5 ).
FUNDING This work received support from " Investissements d'avenir" program  ( Méditerranée Infection   10-IAHU-03 ) of the  French Agence Nationale de la Recherche (ANR). P. J. received a PhD fellowship from  Direction Générale de l'Armement (DGA). The structural biology work was conducted with support from the  MnDrive Initiative , the  Biotechnology Institute seed  grant program, and a  University of Minnesota  startup funds (to MHE) and in part by the award no. R35GM133487  (to MHE) by the  National Institute of General Medical Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institutes of Health.
1a ; Supplementary Fig. 4b ; Supplementary Movie 1). Residues 30 to 1,069 (~ 70 % of the sequence) were clearly defined in the cryoEM density map. The N-terminal signal peptide (predicted to be residues 1-24) is cleaved prior to S-layer assembly  21. A few N-terminal residues and residues 1,070-1,424 at the C-terminus were not resolved by SPA due to their high flexibility (Supplementary Fig. 5 ; Supplementary Movie 2). SlaA30-1,069 is a Y-shaped protein. It consists mostly of β-strands and contains only a few short α-helices (Fig. 1b , Supplementary Fig. 4c, d ). The polypeptide chain is arranged into four domains (D130-234, D2235-660,701-746, D3661-700,747-914, D4915-1,069) defined by SWORD  26  (Fig. 1c ). Of those domains, only D4 shows significant similarity to known structures -to domain 3 of complement C5 (PDB ID: 4E0S) according to DALI  27.
A disulphide bond links D3 and D4 (Cys677-Cys1,017) (Supplementary Fig. 4d ), however, the density of this bond is not visible in the cryoEM map, likely due to electron beam damage  28. The structure of the missing C-terminus (SlaA914-1,424) was predicted (including D4 to aid alignment) using Alphafold  29  and revealed two additional β-domains, D5 and D6, (Supplementary Fig. 6 ). Alphafold predicted five different conformations of SlaA914-1,424, which differed with regards to the position of D5-D6 relative to D1-D4, suggesting an in-plane flexibility between these two parts of the protein around a hinge (amino acids A1,067-L1,071) between D4 and D5 (Supplementary Fig. 6  ). Similar conformations were also observed in 2D classes of our cryoEM dataset (Supplementary Fig. 5 ), substantiating the Alphafold predictions in Supplementary Fig.
Image processing Initial steps of motion correction (MotionCor 2  67  ) and CTF estimation (CTF-find 4  68  ) were performed in Relion 3. 0 34 and Relion 3. 1  25  for datasets at pH 4 and 7, whereas Warp  69  was used for the pH 10 dataset. Further steps of 2D and 3D classification, refinement, CTF refinement and polishing were performed using Relion 3. 1. For a detailed workflow of the three datasets see Supplementary Fig. 1, 9a ,b and 10. The refined maps were post-processed in Relion 3. 1 as well as using DeepEMhancer  70. The produced maps had a resolution of 3. 1 Å, 3. 9 Å and 3. 2 Å at pH 4, 7 and 10, respectively, by gold-standard FSC 0. 143. The SlaA atomic model was built de novo using the cryoEM map at pH 4 in Buccaneer  71  , refined using REFMAC5  72  and rebuilt in Coot  73. The glycans were modelled in Coot with the refinement dictionary for the unusual sugar 6-sulfoquinovose prepared using JLigand  74.
Acknowledgements We acknowledge  Ufuk Borucu  for help with data collection, and the  GW4 Facility for High-Resolution Electron Cryo-Microscopy , funded by the  Wellcome Trust  ( 202904/Z/16/Z  and  206181/Z/17/Z ) and  BBSRC  ( BB/R000484/1 ). We also acknowledge  Alexander Neuhaus  for assistance with data analysis. We thank  IDRIS  for the allocation of high-performance computing resources (allocations # 2020-AP010711998  and # 2021-A0100712343  to CH).
Data availability The SlaA atomic coordinates were deposited in the Protein Data Bank (https://www. rcsb. org/) with accession numbers 7ZCX, 8AN3, and 8AN3 for pH 4, 7 and 10, respectively. The electron density maps were deposited in the EM DataResource (https://www. emdataresource. org/) with accession numbers EMD-14635, EMD-15531 and EMD-15531 for pH 4, 7 and 10, respectively. Other structural data used in this study are: H. volcanii csg (PDB ID: 7PTR, http://dx. doi. org/10. 2210/pdb7ptr/pdb), and C. crescentus RsaA ((N-terminus PDB ID: 6T72, http://dx. doi. org/10. 2210/pdb6t72/pdb, C-terminus PDB ID: 5N8P, http://dx. doi. org/10. 2210/pdb5n8p/pdb).
Chemometric data analysis The complete operando XAS datasets were analysed by combining Principal Component Analysis (PCA) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis, see details elsewhere  [25]. The MCR-ALS analysis for XAS data set was carried out with the following constraints: non-negativity of the concentration of the components and closure (sum of the components concentrations equal to 100%) as well as single component at beginning of discharge. For Mn a less or equal to 1 closure constrain had to be used to achieve a converging fit.
Decoupled blends For the decoupled blended electrode study, the L25:N75 and L75:N25 blends were selected and each cell was subjected to subsequent charge and discharge cycles. Fig. 3  summarizes the data corresponding to a C/5 (0. 2 C) discharge, a rate comparable to the one used for the operando studies. The current recorded for each material in the blend was divided by its theoretical capacity to obtain the effective C-rate, indicative of the real current load each electrode material is bearing. As expected, due to their very different characteristic electrochemical curves and power capabilities, the effective C-rate of the two materials varies constantly depending on the state of charge (SoC) of the blend. The influence of the LMO voltage plateaus is highlighted by the fact that its discharge C-rate increases drastically when the voltage of the cell coincides with them, resulting in two characteristic peaks in the effective C-rate plot.
6  in S. I. The results obtained suggest that the full spectral data set of Ni K-edge can be reproduced with a linear combination of three components. This is well in line with the expected reduction of Ni +4 to Ni +2 via the intermediate Ni +3 phase upon NMC-532 discharge, see Fig. 7 (top). For Mn K-edge the spectral changes can be reproduced analogously using two components which is in agreement with the expected reduction of Mn +4 to Mn +3. 5 during LMO lithiation. It should be noted that the statistical approach used for the concentration profile plotting only takes into consideration the spectral changes in the data set. Absorbing atoms that do not change their coordination geometry and /or chemical state leading to spectral alterations are hence not considered. Interestingly, the closure condition (sum of components equal to one) could not be achieved for the Mn spectral data set unlike for Ni.
Acknowledgements Operando experiments were performed at  MSPD & CLAESS beamlines  (proposal 2021035098 and  2021095303 ) at  ALBA Synchrotron  with the collaboration of ALBA staff. Authors are grateful to  ALISTORE ERI  and to  H. Ehrenberg  and his team at  KIT  for providing access to the 8 coin cell carousel holder. ICMAB-CSIC members thank the  Spanish Agencia Estatal de Investigación Severo Ochoa Programme for Centres of Excellence in R&D, Spain  ( CEX2019-000917-S ) and funding through grant  PID2020-113805GB-I00. This work has been done in the framework of the doctorate in Materials Science of the  Universitat Autònoma de Barcelona  and D. C. wants to acknowledge  DESTINY  MSCA PhD Programme. Authors acknowledge support of the publication fee by the  CSIC Open Access Publication Support Initiative  through its  Unit of Information Resources for Research (URICI ).
Results and discussion The results of peel tests, including the average peel load values and failure mechanisms observed for each type of specimens, are presented in Table  5. The average peel load values are shown as the average ± standard deviation of the three specimens tested for each adhesive type. Two distinct failure mechanism were observed: cohesive failure (CF) occurring within the adhesive layer, and adhesive failure (AF) at the interface between adhesive and adherends. The percentage area of each failure modes was measured based on the visual observations of the specimens' fracture surfaces, using the Fiji    image processing package for scientific image analysis. Fig. 6  gives a representation of the realized measurements on one of the tested specimens. The average failure peel load was determined over a displacement of 100 mm, excluding the first 50 mm.
Acknowledgements This research was financially supported by  CESI LINEACT and COST Action  ( CA18120 ) Association under the grant award:  E-COST-GRANT-CA18120-e629dae7. The authors would like to acknowledge the support of the  GeM Institute of research in Mechanics and Civil Engineering , and the  ASM Department of TU Delft (Aeronautics Structures and Materials). This work was supported by  FCT , through  IDMEC , under  LAETA , project  UIDB/50022/2020. We would also like to show our sincere appreciation to  Sika®  for providing the adhesives, and to  Sika® Operator Mr. Jean-Pierre Jugé , for his advice and for successfully realizing the bonding of one of the joints used in this study.
Acknowledgment This work was financially supported by the  National Natural Science Foundation of China  (No. 12002258 ), and the  China Postdoctoral Science Foundation  (No. 2022M712540 ). Wei Ding  is grateful to the support of  China Scholarship Council  (Grant No. 202206280170 ). Wei Ding  thanks  Huan Lu  ( Xi'an Jiaotong University ) for his help in the analysis of the study problem.
The crack regions in the absorption images were segmented by machine learning with the U-net function using ORS Dragonfly Pro software. After meshing the segmented crack region, the thickness between the boundary points was calculated as the diameter of a hypothetical sphere fitting into each boundary point. In the uncharged specimen (Figure  4  (a)), the threedimensional crack morphology is not continuous and there are a lot of macroscopically isolated noncracked regions (so called un-cracked ligaments). This indicates that the crack propagated discontinuously and that the crack was locally arrested. As reported previously, the discontinuous crack propagation could be attributed to the high crack arrestability of low-angle boundary segments 31). In contrast, both the number and size of macroscopically un-cracked ligaments are small in the hydrogen-charged specimen (Figure  4 (c, e )), indicating that the hydrogen-related intergranular cracks propagated more continuously.
, 2012). We used the weighted back projection (WBP) algorithm  (Herman et al. , 1976 ) for this study. The resulting 3D volume is interpolated within the acquisition angular range and extrapolated in the missing-wedge, resulting in ribbon-shaped reconstructions of dislocations. To extract linear contrast of dislocations, dislocation lines are redrawn directly into the reconstructed volume using the UCSF Chimera free software  (Pettersen et al. , 2004 ).
Acknowledgments We acknowledge funding from  CNRS-INSU  though their program  TELLUS-Syster  (project  DOMINO 2021-2022 ) to SD; the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  under grant agreement No  787198 -TimeMan  to PC. SD thank  C. Nevado  and  D. Delmas  for providing highquality thin sections for TEM and SEM. S. Erdmann  is thanked for her contribution for SEM images. The  Helios SEM-STEM-FIB at CarMA facility at Université Clermont Auvergne  is supported by the  Conseil Régional Auvergne-Rhône-Alpes (France) , and the  TEM national  facilities in  Lille University  is supported by the  Conseil Régional du Hauts-de-France (France). Both microscopes are supported by the  Institut National de Sciences de l'Univers (INSU)  from  Centre National de la Recherche Scientifique (CNRS, France).
, 2018). As a result, the instrument offers a somewhat unique size and time scales for XPCS investigations. Fast processing of XPCS data is facilitated by the Dynamix Python software package  (Paleo et al. , 2021). Additionally, a Python software named XPCSUtilities (Chevremont, 2023a) was developed as a user-friendly graphical interface to access, visualize and analyze the processed data. The performance of XPCS at the TRUSAXS instrument is demonstrated using dilute colloidal suspensions and powder samples. The speckle contrast is characterized as a function of sample-to-detector distance at the standard working energy of 12. 230 keV, and as a function of energy at the maximum sample-to-detector distance (31 m). The results are compared with theoretical estimates  (Sandy et al. , 1999). The stability of the beamline, and thus its ability to perform long-time XPCS, has been characterized using a fixed alumina powder sample.
, 1997)   where t 1 and t 2 are the time of frames 1 and 2, respectively. = t 2 À t 1 is the lag time, t = (t 1 + t 2 )/2 is the age, h. i p is the average over all pixels in the corresponding q-bin, and I(t, p) is the intensity of pixel p at time t. TTCF can be used to study out-of-equilibrium dynamics  (Malik et al. , 1998). When the system is at (pseudo-)steady state, this TTCF can be timeaveraged to obtain the standard g 2 (q, )  (Brown et al. , 1997) , g 2 ðq; Þ ¼ g tt ðq; ; tÞ t ð3Þ where h. i t is the average over all ages. Dynamix software also computes the standard deviation of g 2 (q, ) and the normalized variance is directly related to the dynamical susceptibility, 4 (, q), that characterizes the steadiness of the system over the measurement  (Duri & Cipelletti, 2006)  (see also SI).
To ensure consistency of the results file, all results and computation parameters are saved into one HDF5 file, compliant with NeXus format  (Ko ¨nnecke et al. , 2015). For online data processing, a lightweight server detects the new XPCS acquisition files and launches the data processing with a predefined list of INI configuration files. For most of the acquisitions, the correlation functions are available in less than one minute after measurement, which is a huge advantage compared with the previous XPCS data reduction procedure involving manual operations. Typical timing to perform the correlation on 35 narrow bins on a 10 000 frames dataset in 8bit mode is 13 s to read the data from the central storage, 1. 2 s to perform SAXS azimuthal regrouping and other reduction steps, 3 s to initialize the correlator on the GPU, 16 s to compute all the TTCF and g 2 (q, t) functions with logarithmic distribution of lag time, and 5 s to save the results to disk.
Visualization tool In addition to the processing software, a display tool, named XPCSUtilities (Chevremont, 2023a), has been developed to ease the visualization of the results immediately after the measurement. It is publicly available on the Git repository, and can be installed by any user on a Linux, Mac or Windows computer using the pip utility. The first function of this software is to visualize XPCS data on the fly. It has the ability to display the 2D SAXS pattern and the azimuthally regrouped curve, g tt (q, , t) and g 2 (q, ), from the results file. This software can also perform basic data processing, such as exporting g 2 (q, ) as ASCII files. Some basic data analysis procedures are also implemented to evaluate the data and decide on the next measurements. There is a basic fitting tool, which fits the data with predefined functions.
The last tool is a model-free relaxation time-Laplace analyzer, based on the multiscale CONTIN method (Lienard et al. , 2022), and adapted to work with multiple q values. Additionally, this software can be used as a Python package to open and manipulate the XPCS results files, and used to script all of the tools described above. The online visualization and analysis tools hopefully aid non-expert users in more effective exploitation of the XPCS data.
The use of higher X-ray energy helps to reduce radiation damage with biological samples and soft matter  (Mo ¨ller et al. , 2019a). If needed, XPCS measurements can be performed even at higher energies, $ 20 keV, with a speckle contrast of 20%. This opens new perspectives for XPCS studies of highly absorbing samples and sample environments, such as a high-pressure cell that has thick diamond windows  (Moron et al. , 2022; Zhang et al. , 2023). To better exploit the instrumental developments, a new software suite that enables online processing of XPCS data acquisition as well as the visualization of processed data has been developed. This significantly improves the user experience when performing XPCS using the TRUSAXS instrument, and hopefully helps in promoting the application of XPCS to new scientific cases.
Acknowledgements The authors profusely thank  Y. Chushkin ,  J. Kieffer  and  P. Paleo  for development of the Dynamix software package and  A. Homs  for critical detector software support. ID02 staff,  M. Kocsis  and  P. -A. Douissard  are acknowledged for technical support. The authors also thank  Y. Chushkin  for fruitful discussions and  ESRF  for provision of synchrotron beam time.
William Chevremont et al. Improvement of UA-XPCS with the EBS J. Synchrotron Rad. (2024). 31, 65-76Figure 2Schematic representation of the XPCS data processing workflow implemented. The processing starts from the raw data file, which contains the recorded patterns as well as metadata required for the calculation. The processing software id02xpcs_correlate performs the correlation according to the specifications in the INI file using the Dynamix package and writes the results in HDF5 format. The result file can be displayed using XPCSUtilities software or post-processed with custom scripts.
Acknowledgements The work was nancially supported by the  Russian Science Foundation  (grant no. 22-73-00072 , spectroscopic mechanistic studies) and by the  CNRS. E. S. G. is grateful to the  French Embassy in Moscow  for a joint PhD fellowship ( Vernadski program ). Computational studies were performed using HPC resources from CALMIP (grant no. P18038 ). Spectroscopic data were partially collected using the equipment of the  Center for Molecular Composition Studies of INEOS  RAS with the support from the  Ministry of Science and Higher Education of the Russian Federation  (contract no. 075-03-2023-642 ). We thank  Dr Noël Lugan  and  Dr Olivier Baslé  for helpful discussions.
The cyclometalated gold(III) compound [Au(bnpy)Cl2] is used for promoting C-Se coupling across a wide variety of substrates, including under biocompatible conditions. Previous works on metal-promoted C-Se coupling mostly explore copper. [5, 10]  Here, diphenyldiselenide (Ph-Se)2 and selenocystine (Sec)2 were selected as small molecule models. The reaction was also demonstrated with the diselenide analog of the cyclic peptide vasopressin, (Se-Se)-AVP, and the GPx1 selenoprotein from bovine erythrocytes. The GPx1 structure (PDB ID 1GP1) shown highlights the Sec-containing catalytic site (note that in the crystal structure, the Se atom is present in an oxidized state). [23]  Herein, we demonstrate the promising utility of [Au(bnpy)Cl2] for the efficient arylation of small molecule diselenides and Sec-containing biomolecules.
LC-MS gave further evidence that the 1:0. 5 [Au(bnpy)Cl2]:(Ph-Se)2 ratio was not enough to lead to full conversion even after 24h incubation, as some unreacted cyclometalated compound is still left (in agreement with 1 H NMR). Meanwhile, at either 1:1 or 1:5 ratios the Se-arylated species appeared as the only species in the chromatogram after 24h and longer incubation times. MS/MS spectra of the m/z 326. 0449 (Figure  3 , bottom left) and the m/z 522. 0029 (Figure  3 , bottom right) ions confirmed the identity of these two species as the Searylated product [Ph-Se-bnpy + H] + and its gold(I) adduct [Ph-Sebnpy + Au(I)] + , respectively. The full LC-MS dataset, with additional ions observed throughout the experiments, is presented in Figures  S21 -S24   Selenocystine, (Sec)2, was studied as a biologically relevant small molecule model.
Acknowledgements This project has received funding from  "la Caixa" Foundation  ( ID 100010434 ) through the  Junior Leader fellowship  to RdP ( LCF/BQ/PI22/11910033 ). The  São Paulo Research Foundation  (FAPESP; www. fapesp. br) is acknowledged -grants  CEPID-Redoxoma 2013/07937-8  to SM and fellowship  2017/13804-1  to AI. The authors thank  Dr. José Ignacio Miranda  ( UPV/EHU, SGIker ) for the aid and fruitful discussions with the 77 Se NMR measurements. Technical and human support provided by  SGIker (UPV/EHU , MICINN, GV/EJ,  ERDF  and  ESF ) is also acknowledged. The  Donostia International Physics Center  receives support from the  "Severo Ochoa" Program for Centers of Excellence in R&D  (Grants  CEX2018-000867-S ) run by the  Spanish State Research Agency. This research has also received Funding from the  European Union 's  Horizon H2020 Research and Innovation  under the  Marie Skłodowska-Curie Grant Agreement  N°  945416  through the  PhD fellowship  of IK.