dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , dataset from  Webster et al. , 2018)  and is also in good agreement with other recent findings with about 3-5 μg/g Br in melt inclusions from arc basalts  (Kutterolf et al. , 2013; Vidal et al. , 2016). With 5 μg/g as initial average Br concentration the calculated global Br flux from arc volcanoes ranges from 23. 5 × 10 9 g/y to 72. 9 × 10 9 g/y in respect to the minimum and maximum values of our experimentally determined partition coefficients D Br fluid/glass = 1. 19 and D Br fluid/glass = 3. 92 and the water content of arc magmas. The calculated flux is higher than values from degassing data (Table  5 ), but these values could be underestimated as they assume a SO 2 fluxes of 14*10 12 g/y  (Aiuppa et al. , 2005) , 15*10 12 g/y  (Pyle and Mather, 2009) , 16. 8*10 12 g/y  (Webster et al.
Conclusion We present a new experimental dataset for Br partitioning between basalt and an aqueous fluid and between basalt and neon. To our knowledge these are the first experimental data measured in-situ for Br degassing from basalt. We calculated partition coefficients of D Br fluid/melt = 1. 19 to 3. 92 at 0. 4 to 1 GPa for magma degassing in water-rich magmas (aqueous fluids in equilibrated experiments). The values are in good agreement with partition coefficients from ex-situ experiments at lower pressures with basalt  (Cadoux et al. , 2018)  and confirm a slight negative correlation with pressure, as well as a strong positive correlation with SiO 2 content. Experiments with an inert gas analogue for CO 2 as pressure medium (neon) give partition coefficients <1 (D Br fluid/melt = 0. 38 ± 0.
Acknowledgements This project was funded by the  ANR Projet de Recherche Collaborative VOLC -HAL-CLIM (Volcanic Halogens: from Deep Earth to Atmospheric Impacts) ,  ANR-18-CE01-0018  (Hélène Bureau). Tobias Grützner  is grateful for an  EU Marie Skłodowska-Curie Fellowship "ExCliso " (Project  ID 101017762 ). We thank the  PSICH É  staff at  SOLEIL  for their assistance. Paraskevas Parisiadis  is thanked for his help with the laser heating test runs we performed at the IMPMC. We thank  Marion Louvel ,  Richard W Thomas , and the two reviewers for their helpful comments to improve the manuscript. Editor  Claudia Romano  is thanked for handling the manuscript. The authors acknowledge the facilities, and the scientific and technical assistance of  Microscopy Australia  at the  Centre of Advanced Microscopy, The Australian National University.
Acknowledgements We acknowledge the  National Key R&D Program of China  (  2021YFA1501302  ), the  National Natural Science Foundation of China  (  22121004  ,  U1862207  ,  22122808  ), the  Haihe Laboratory of Sustainable Chemical Transformations  ( CYZC202107 ) and the  Program of Introducing Talents of Discipline to Universities  (  BP0618007  ) for financial support. This work is supported by the  XPLORER PRIZE.
We resolved to find a set of global parameters to describe the relaxation processes in 1-Dy by simultaneous fitting of the three datasets to Eqn 3. As the high-field process is only observable in the 12 K dataset, we do not have sufficient information to determine the temperature depdencence of this process (which would be expected to have a linear or power-law T-dependence for the Direct or Raman-II relaxation processes, respectively  48, 50  ), and so we simply assume a linear temperature dependence. We used values from the individual fits as starting parameters, and performed the first optimisation with Q and p fixed before allowing all parameters to freely refine. This gives global best-fit parameters: Ueff = 924(17) K, τ0 =  should correlate with Ueff. 27  We hypothesized that in solution the N-Dy-N angle in 1-Dy may increase, as has been observed for the Yb congener.
complexes. 24, 25, 28, 29  Single crystal XRD data were collected on either an Oxford Diffraction Agilent Supernova profile was applied for all structures. 59  The structures were solved using SHELXT; 60 the datasets were refined by full-matrix least-squares on all unique F 2 values. 60  Anisotropic displacement parameters were used for all non-hydrogen atoms with constrained riding hydrogen geometries, with the exception of borohydride H atoms, which were located in the difference map and refined isotropically; Uiso(H) was set were close to heavy atoms and were of no chemical significance. CrysAlisPro  59  was used for control and integration, and SHELX  60, 61  was employed through OLEX2  62  for structure solution and refinement. ORTEP-3,  63  and POV-Ray  64  were used for molecular graphics.
Acknowledgements We thank the  University of Manchester  for access to the Computational Shared Facility, and the  European Research Council  ( StG-851504  and  CoG-816268 ) and the  UK EPSRC  ( EP/R002605X/1 ,  EP/P001386/1 ,  EP/S033181/1  and  EP/T011289/1 ) for funding. We acknowledge the  EPSRC UK  National https://doi. org/10. 26434/chemrxiv-2023-xv0ht ORCID: https://orcid. org/0000-0003-1575-7754 Content not peer-reviewed by ChemRxiv. License:  CC BY 4. 0.
-8. 45 (7)  s, C = 10 -4. 31 (5)  s -1 K -n , n = 4. 05 (3) , 𝜏 𝑄𝑇𝑀 = 10 -1. 09 (4)  s, Q = 10 -14 (17)  Oe -p , p = 7 (7) , A = 10 -15(4) s -1 Oe -m K -1 , m = 4(1) (Figures3, 4and S99). The field-independent QTM rate and the Orbach, Raman-I and Direct/Raman-II parameters are not significantly changed from the individual fits. However, the fielddependence of QTM process is increased and poorly defined (large errors in Q and p) relative to the 12 K parameters: this reflects the efficient quenching of QTM in the 0. 08 T dataset, which exhibits a downturn in the temperature-dependent rates at the lowest temperatures (FigureS99). However, our model does not account for any possible non-power law temperature-dependence of Raman-I relaxation at low temperatures, 48  which could explain the downturn in rates, nor does it account for any possible temperaturedependence of QTM, 47  thus we believe the field-dependence of QTM to be overestimated by the global fit.
The finite element model of the representative volume element The campaign of sensitivity analyses presented in this work aims to investigate the sensitivity of the dynamical response of the RVE geometries shown in Fig. 1  to the following parameters: The relative density of the RVE. The value of the strain rate imposed to the RVE. The geometrical imperfections resulting from the manufacturing process. Three values of relative densities are considered in all the analyses, i. e. , r ¼ [0. 2, 0. 4, 0. 6], for which three values of strain rate, i. e. , _ ε ¼ [250, 750, 1250]s À1 , are used to define the dynamic compressive loading. To evaluate the EAC of the different RVE geometries and to assess the influence of manufacturing-induced geometrical imperfections on the dynamics of the LS, a numerical framework is developed within the Abaqus® FE commercial software.
Python scripts are created and coupled with the Abaqus CAE interface to perform all the operations required to prepare the input files to be submitted to the Abaqus Implicit solver. An example of the FE model, for each RVE topology, is illustrated in Fig. 1. Specifically, for each RVE geometry, the following operations have been completely automated: (1) creation of one eighth of the RVE geometry; (2) creation of the mesh and generation of the FE model of the whole RVE by means of duplication and mirroring operations; (3) applications of the periodic boundary conditions (PBCs) to the boundary of the FE model (this operation is carried out through the HOMTOOLS 1 toolbox for Abaqus); (4) definition of the contact regions (contact zone between the struts composing the RVE, including self-contact) and of the loading case (i.
Numerical results The computation of the normalized internal energy IE is performed by means of dedicated post-processing Python scripts, which are executed after those used to generate the FE models of the RVEs and to run the simulations. Abaqus automatically computes the values of the internal energy, according to Eq. (  3 ), for each element constituting the FE model. By performing a sum over the elements constituting the FE model, the value of IE is computed as reported in Eq. (  2 ). The normalized EAC W c of Eq. (  1 ) is obtained by extracting the value of the axial force and the equivalent compressive stress s c is obtained as the ratio of the axial force to the initial cell section. To highlight the influence of geometrical imperfections on the dynamical response of RVE geometries, the results of the idealized RVE geometries are assumed as the reference ones and the results obtained with the scenarios of Table  3  are provided as percentage values with respect to the reference ones.
Control software The control of motor positions is performed via the IcePAP graphical interface (https://www. esrf. fr/Instrumentation/ DetectorsAndElectronics/icepap; Fig. 6a ). The event sequence is provided to the CITY timing module via a terminal (Fig. 6b ). Samples are visualized through the PYLON software controlling the Basler camera (Fig. 6c ). Spectra are recorded using the AvaSoft software (Avantes; Fig. 6d ).
Spectroscopic data acquisition and analysis Spectroscopic data are recorded using the AvaSoft software (version 8. 11; Avantes). Data acquisition is performed using the triggering mode of the spectrophotometer. By default, a 100 ms acquisition time is used to record the 'dark' spectrum (the background signal of the CMOS detector), the 'reference' spectrum (the light transmitted next to a crystal) and the 'sample' spectrum (the light transmitted through a crystal). A UV-Vis absorption spectrum is straightforwardly calculated using these three spectra. In-house Python scripts were written to average, smooth and/or plot (https://github. com/ncara/TRicOS;  Caramello et al. , in preparation).
The lack of bleaching of the photoactive protein can be verified by the iterative superimposition of successive ground-state spectra. The build-up and decay of the M state can be visualized by monitoring the absorbance at 415 nm. As expected, the M state is already present in the crystal 3 ms after light excitation, and its occupancy is maximal between 100 ms and 1 ms (black trace in Fig. 8b ). The build-up/ decay profile matches published data recorded on BR crystals  (Efremov et al. , 2006). This experiment was repeated at various laser-pulse energies, thus producing a power titration (or, more rigorously, a peak-fluence titration) of the M-state build-up and decay profile. The peak fluence was varied from 18 to 633 mJ cm À 2 (calculations were performed as in Nass  Kovacs et al. , 2019).
Acknowledgements Thomas Vigouroux  is acknowledged for his contribution to software writing at an early stage of the project. Kirill Kovalev ,  Manuel Maestre-Reyna ,  Po-Hsun Wang ,  Anaı ¨s Chre  ´tien,  Robin Schubert  and  Valerie Panneels  are acknowledged for help and discussion during instrument commissioning. VG highly appreciates support within the framework of Commissariat al' Energie Atomique et aux Energies Alternatives (Institut de Biologie Structurale)-Helmholtz-Gemeinschaft Deutscher Forschungszentren (Forschungszentrum Ju ¨lich)  Special Terms and Conditions 5. 1 specific agreement.
Funding information NC acknowledges a joint PhD fellowship from the ESRF and the University of Hamburg. SB acknowledges a joint PhD fellowship from the ESRF and the Forschungszentrum Ju ¨lich. The German Bundesministerium fu ¨r Bildung und Forschung is acknowledged for funding of the Tape4SSX project under grant No. 05K19FM1. This work used the icOS platforms of the Grenoble Instruct-ERIC center (ISBG; UAR 3518 CNRS-CEA-UGA-EMBL) within the Grenoble Partnership for Structural Biology (PSB), supported by FRISBI (ANR-10-INBS-0005-02) and GRAL, financed within the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS (ANR-17-EURE-0003).
4 a ). Curiously, this improved stability comes with a less ordered structure (Fig. 4 b ) with respect to an ordinary rapidly cooled MG  [38]. The influence of the ultrastability on the collective particle motion and aging has been investigated in an MG of Cu50Zr50 produced by physical vapour deposition following the procedure reported in Ref. 34  [35]. Signatures of an improved stability have been reported at the atomic scale in vapour-deposited MG. This is shown in Fig. 5a  which reports correlation functions measured with XPCS in different vapour-deposited MGs: as-prepared, and preannealed at different annealing temperatures Ta, above and below the calorimetric Tg and then cooled back to ambient temperature prior to the measurements. Data collected during an isotherm in the glassy state show that the relaxation dynamics is slower in the as-cast vapour deposited MG with respect to that measured on the same material, pre-annealed at higher temperatures (Fig.
This approach will be explained in more detail in what follows. Define a K cross-function 𝐾 𝑂𝑁 that modifies the K-function for single populations (Eq. (  1 )). This function evaluates the expected number of points in the new dataset (i. e. ,  ) given a specific distance lag ℎ from the set of old defects (i. e. , ). Let 𝑅 𝑂 and 𝑅 𝑁 be some realizations of these populations with a number of points of 𝑛 𝑂 and 𝑛 𝑁 in a given region 𝑆, respectively. The cross-K function is defined as follows  [40] : 𝐾 𝑂𝑁 (ℎ) = 1 𝜆 𝑁 E ( 𝑛 𝑁 ∑ 𝑗=1 (𝑑 𝑂𝑁 (𝑖, 𝑗) ≤ ℎ) ) (3) where (⋅) is an indicator function, 𝑑 𝑂𝑁 (𝑖, 𝑗) is the distance from the 𝑖th point of 𝑅 𝑂 to the 𝑗th point of 𝑅 𝑁 , and 𝜆 𝑁 is the intensity of the new defects. According to Smith  [40] , the number of points 𝑛 𝑁 and the distance between the two realizations 𝑑 𝑂𝑁 (𝑖, 𝑗) would be considered random variables because the realizations have a different number of points that are randomly distributed based on the population intensities.
Main descriptors of corrosion defects Most defects are concentrated on the inner wall, which is somehow expected due to the coal-tar coating that protects the outer wall; a summary of statistics of these datasets is depicted in Table  4. Because further information about defects' shape is not available in ILI, the maximum rather than the average depth for each defect will be considered for being more conservative with the metal loss (Fig. 1 ). Following the matching approach proposed by Amaya-Gómez et al. [23]  based on a Voronoi nearest mixed criterion, it was classified each metal loss from the last inspection as old -i. e. , matched -and new -i. e. , not matched defects. Table  5  shows the summary of the corrosion extent from both sets. Note that larger and wider defects appeared in the new dataset but with shallow depths, associated with uniform corrosion that may not have initially been detected in the first inspection.
In this case, it is assumed that Deep defects have a depth 𝑑 ≥ 25%𝑡 and Shallow defects have a depth of 𝑑 ≤ 25%𝑡 (Fig. 9 ). In contrast to the earlier findings, the repulsion is predicted to occur after a separation of ℎ ≥ 20 mm in both pipe walls (instead of ℎ ≥ 5 mm). This outcome is expected since the old dataset includes several defects with depths under 25%𝑡 that cluster with both shallow and deep corrosion points. This figure also depicts a more ''random'' or expected behavior for lags larger than 20 mm, which is primarily represented by the amount and distribution of the shallow defects. Since shallow defects account for over 90% of the reported data in this case, deep defects would have a greater proportion of shallow  neighbors. The indistinguishable behavior of the inner wall is explained by the fact that these cross neighbors have shorter separations than in the case of old and new records.
CRediT authorship contribution statement Rafael Let us take the Clark & Evans statistic and its significance test for illustrative purposes. First, it is necessary to estimate the parameters E 𝑟 and 𝜎 𝑟 , considering the study area 𝐴 = 3. 36 m 2 , the perimeter is 𝑃 𝑒 = 9. 56 m and 𝑛 = 28, which follows that E 𝑟 = 0. 222 and 𝜎 𝑟 = 0. 0278. Then, using the mean nearest neighbor distance for each point in the new dataset follows that 𝐶𝐸 𝐷 = 0. 90. Based on a Monte Carlo test approach, with 99 equally-sized random point patterns under a ranking perspective, as mentioned in Section 2, it was obtained a 𝑝-value of 0. 25. This result indicates that the CSR assumption cannot be rejected with a significance level of 0. 05, which is confirmed by the statistic close to the unity and the 𝑝-value greater than the rejection zone.
97 1. 01 1 0. 99 0. 85 1. 25 1. 02 1. 08 1. 26 1. 26 1. 01 0. 98 1. 41 0. 58 1. 17 1. 03 0. 74 0. 83 0. 94 0. 95 0. 68 0. 58 0. 83 0. 78 0. 53 0. 55 0. 78 0. 70 0. 60 0. 61 0. 87 0. 86 NR NR 0. 87 0. 81 0. 86 0. 66 0. 87 0. 76 0. 59 0. 67 0. 87 0. 85 0. 66 0. 58 0. 84 0. 76 0. 93 0. 50 0. 79 0. 75 0. 55 0. 69 0. 81 0. 79 0. 42 0. 37 ILI1Int 0. 73 0. 71 0. 69 0. 77 0. 74 0. 78 0. 67 0. 73 0. 72 0. 58 0. 57 0. 55 0. 61 0. 57 0. 63 0. 53 0. 60 0. 56 Welds ILI2Int 0. 84 0. 89 0. 83 0. 9 0. 85 0. 78 0. 85 0. 91 0. 78 0. 65 0. 68 0. 67 0. 69 0. 64 0. 62 0. 64 0. 73 0. 62 up to 1m ILI1Ext 0. 64 0. 68 NR 0. 88 1. 25 0. 15 0. 45 0. 72 0. 57 0. 46 0. 47 NR 0. 53 0. 75 0. 12 0. 35 0. 61 0. 40 ILI2Ext 0. 76 0. 65 NR 0. 87 0. 77 1. 32 0. 39 0. 81 0. 76 0. 56 0. 46 NR 0. 58 0. 54 0. 91 0. 32 0. 71 0. 52 S1: Soil 1, S2: Soil 2, S3: Sol 3, S4: Soil 4, S5: Soil 5, S6: Soil 6, S7: Soil 7, UZ: Urban Zone ILI1Int and ILI2Int are the first and second ILI runs datasets at the inner wall dataset, respectively.
ILI1Ext and ILI2Ext are the first and second ILI runs datasets at the outer wall dataset, respectively. *NR: No defects reported. S1: Soil 1, S2: Soil 2, S3: Soil 3, S4: Soil 4, S5: Soil 5, S6: Soil 6, S7: Soil 7, S8: Near urban zone.
Algal and human ECM proteic sequence homology prospective analysis The Eukaryotic Ortholog Groups (KOG) database, which phylogenetically classi es proteins encoded by complete eukaryotic genomes, has been recently supplemented with the protein sequences deduced from the V. carteri genome  [23]. The computer classi cation resulting from this entry (Fig. 4 ) highlighted the presence in this alga of orthologs likely to shed light on our observations concerning cell adhesion. As possible cell adhesion ligands diffusing or being exposed at the algal surface, predicted extracellular structures of interest were found within the cellular processes and signalling category. These proteins are involved in glycoprotein modi cation (e. g. , exostosin 1 and 2), extracellular matrix building (e. g. , collagen IV and XVIII and laminin alpha and gamma), cell adhesion (e.
Acknowledgements: The authors thank  Vanessa Bleicher- and  Ulysse Peirera  for technical help, the  Service Analyses Physico-Chimiques (SAPC)  for the imaging , and all their colleagues for helpful comments throughout this effort. Christophe Egles  thank the  RIN program  of the  Région Normandie ,  Evreux Portes de Normandie  and the  FEDER european  fund for their support.
3 Å in a privileged direction of the crystals, as often observed for membrane proteins  (Martin et al. , 2023; Robert et al. , 2017). Despite the improvement of the crystal quality, the anisotropy precluded resolution without a good model for molecular replacement. However, the soluble SpNOXDH domain represents more than 50% of the fulllength enzyme, making it a suitable model for use in molecular replacement. We obtained some crystals of both SpNOXDH WT and F397W, co-crystallized with FAD. Unfortunately, crystals of the SpNOX F397S mutant with or without NADPH could not be obtained. Structure resolution at 1. 94 Å of SpNOXDH F397W de novo was possible thanks to cocrystallization in the presence of sodium bromide and collection of a bromide-SAD dataset. We used this structure as a model to solve SpNOXDH WT by molecular replacement at 2.
Data collection and structural solution of SpNOX DH F397W A bromide-SAD dataset was collected at the macromolecular crystallography beamline ID30B of the European Synchrotron radiation facility, equipped with Pilatus3 6M detector  (McCarthy et al. , 2018). The wavelength was set above the Br K-edge at 0. 9184 Å in order to maximize the anomalous signal, and a highly redundant data set at a resolution of 1. 94 Å, including 7200 images with 0. 05 degrees oscillation, was collected. Data processing was performed by the GrenADES parallelproc pipeline  (Monaco et al. , 2013)  that is based on XDS  (Kabsch, 2010)  and scaled using aimless  (Evans and Murshudov, 2013)  from the CCP4-suite  (Winn et al. , 2011). Attempts to solve the structure with the molecular replacement method using homologous models failed.
Crystallization, Data Collection, and Processing of full length SpNOX SpNOX F397W at 4. 04 mg. ml -1 in 50 mM TRIS pH 7, 300 mM NaCl, 0. 025 mM LMNG, 0. 01 mM FAD was crystallized in 30. 5% PEG 300, 0. 15 M Li2SO4, 0. 15 M NaCl and 0. 1 M MES pH 6, using the vapor diffusion method in a 6 µL protein sample plus 6 µL of well hanging drop at 20°C. Hexagonal brick-red crystals were obtained, mounted on mesh litho-loops and flash frozen in native conditions in liquid nitrogen. X-ray diffraction data were collected at ID30A-1/MASSIF-1 beamline at ESRF Grenoble  (Bowler et al. , 2015). A dataset of 1800 images was collected with an oscillation range of 0. 2° per image and an exposure time of 1. 699 s and a crystal to detector distance refined to 500. 9 mm. As observed for many membrane protein crystals  (Martin et al.
, 2023; Robert et al. , 2017) , the data collected were very anisotropic going to 3. 2 Å resolution in the strongest diffracting direction. After data processing the dataset was finally cut at 3. 6 Å to ensure sufficient completeness in the highest resolution shell. Inclusion of further data only resulted in more noise without obvious gain in 2mFo-DFc electron density. Data were initially processed using the program XDS as spherical to the highest resolution possible (3. 2 Å) even though spherical statistics were not usable. Staraniso analysis for diffraction anisotropy (staraniso@globalphasing. org) revealed that completeness was 85. 4% in the highest resolution shell, therefore revealing that all the data collectable for this crystal had been collected. Data was cut at the diffraction limits suggested by the Staraniso server using only the first 1000 images.
, 2010)  and LORESTR pipeline  (Kovalevskiy et al. , 2016) , using TLS (5-183 for TM domain and 184-399 for DH domain) allowed large repositioning of some a-helixes of transmembrane domain. A final comparison of a SpNox model generated with AlphaFold  (Jumper et al. , 2021)  drove the repositioning of some loops and some sequence shifts in transmembrane helixes not associated with hemes. Final refinement was made with the help of ISOLDE  (Croll, 2018)  and LORESTR, ramachandran and rotamers outliers were corrected, yielding a final model with R = 26. 2% and Rfree = 32. 0% that was deposited in the Protein Data Bank under the PDB accession code 8qq7. Crystallographic data statistics are presented in Table  S1. The PDB deposition code for the SpNOXDH F397W is 8qq7. support from FRISBI (ANR-10-INSB-0502) and GRAL (ANR-10-LABX-49-01) within the Grenoble Partnership for Structural Biology.
Acknowledgment and funding Funding for the automated crystallography pipelines at  EMBL Grenoble  was provided by the grants and  iNEXT Discovery  (grant agreement  ID 871037 ) and  Fragment Screen  (grant agreement ID:  101094131 ) funded by the  European Commission  and through Instruct-ERIC. We want to thank the  EMBL-ESRF Joint Structural Biology Group (JSBG)  and in particular  Andrew McCarthy  for access and support in the use of MX beam lines at the  ESRF. This work used the platforms of the Grenoble Instruct center (UMS 3518  CNRS-CEA-UJF-EMBL ) with.