dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This study was conducted in the frame of research project  AP09057889  financed by the  Ministry of Science and Education of Republic of Kazakhstan , and the  French-Kazakh scholarship Abaï-Verne  supporting this PhD project. French carbonaceous materials were acquired in the frame of the PIEGEACHLOR ( 1672C0042 -ADEME ) project and pesticide analyses and characterization of carbonaceous matrices were funded by  PYROSAR  ( 20MAC0011 -ADEME ). Analyses were carried out on the  Bio-DA platform (Université de Lorraine, Vandoeuvre-les-Nancy France)  and  Antigen analytical chemistry department (Almaty, Kazakhstan). The authors would like to thank  Mr. Bernard Faye  for his help with statistical treatment of the obtained results.
7, are then proved by showing that our DG-enhanced versions of Hecke algebras Hd and H d on one side, and the DG-enhanced versions of KLR algebras Rpνq on the other satisfy the properties that are required for them to be isomorphic (after completion). We then use the fact that the DG-algebras Rpνq are quasi-isomophic to cyclotomic KLR algebras together with the DG-enhanced version of the BKR isomorphism to show in Corollary 4. 10 that the algebras Hd and H d are quasi-isomorphic to degenerate cyclotomic Hecke algebras and cyclotomic q-Hecke algebras respectively. Acknowledgments. We thank Jonathan Grant for useful discussions. PV was supported by the Fonds de la Recherche Scientifique -FNRS under Grant no. MIS-F. 4536. 19.
Radial Line Intensity Profile Generation We computed radial profiles using the radial_profile function in the GoFish python package (Teague 2019a) to deproject the zeroth moment maps. During deprojection, we incorporated the derived emission surfaces listed in Table  3. This is particularly important for highly elevated surfaces, e. g. , CO, in order to derive accurate radial locations of line emission substructures (e. g. ,  Law et al. 2021b; Rosotti et al. 2021 ). We generated a set of radial profiles from azimuthally averaged profiles to those only including 15°, 30°, and 45°wedges along the disk major axis. Azimuthally averaged profiles are most sensitive to weaker emission at large radii, while profiles extracted from narrow wedges possess a higher effective spatial resolution, given adequate S/N.
In this sample, we also included the HD 163296 and GM Aur disks, which have CO and 13 CO J = 2-1 surfaces, respectively, derived as part of the MAPS program  (Law et al. 2021a) , as well as 13 CO J = 3-2 in GM Aur  (Schwarz et al. 2021). We also extracted the CO J = 3-2 surface of HD 163296 from ALMA science verification data (e. g. ,  Rosenfeld et al. 2013; de Gregorio-Monsalvo et al. 2013 ) using the same methods described in Section 3. 1. While the ALMA science verification data of CO J = 3-2 in the HD 163296 disk and the 13 CO J = 3-2 data in the GM Aur disk have considerably coarser beams than used in this work, we do not expect these larger beam sizes to significantly influence the derived surfaces for these disks (see Appendix D in  Law et al. 2021a ). In all disks and in either CO or 13 CO, the J = 3-2 and J = 2-1 lines show consistent line emission heights.
Both stellar mass and mean temperature trends remain highly scattered and the weak trend suggested by scaling laws means that, even with additional sources, it is difficult to assess the reality of such trends. Observations of disks with more diverse properties, such as substantially warmer gas temperatures and around either lower-mass (<0. 5 M e ) or higher-mass (>3 M e ) stars, are required to provide meaningful anchor points for these potential trends. The correlation between R CO and z/r remains strong and the addition of several sources with large CO disks allows us to better quantify the R CO -z/r correlation. To do so, we used the Bayesian linear regression method of  Kelly (2007)  using the linmix Python implementation. 25  We find a best-fit relation of z/r = (2. 3 ± 0. 6 × 10 -4 )R CO + (0.
The  National Radio Astronomy Observatory  is a facility of the  National Science Foundation  operated under cooperative agreement by  Associated Universities, Inc. C. J. L. acknowledges funding from the  National Science Foundation Graduate Research Fellowship  under grant No. DGE1745303. R. T. and F. L. acknowledge support from the  Smithsonian Institution  as a  Submillimeter Array (SMA)  Fellow. K. I. Ö. acknowledges support from the  Simons Foundation  ( SCOL  # 686302 ) and the  National Science Foundation  under grant No. AST-1907832. E. A. R acknowledges support from  NSF   AST 1830728. S. M. A. and J. H. acknowledge funding support from the  National Aeronautics and Space Administration  under grant No. 17-XRP17 2-0012  issued through the  Exoplanets Research Program. Support for J.
H. was provided by  NASA  through the  NASA   Hubble Fellowship  grant No. HST-HF2-51460. 001- A awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , for  NASA , under contract  NAS5-26555. J. B. acknowledges support by  NASA  through the  NASA   Hubble Fellowship  grant No. HST-HF2-51427. 001- A awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555. L. M. P. gratefully acknowledges support by the  ANID BASAL  project  FB210003 , and by  ANID , -Millennium Science Initiative Program -NCN19_171. T. T. is supported by  JSPS KAKENHI  grant Nos. JP17K14244  and  JP20K04017. S. J. acknowledges support from  National Natural Science Foundation of China  under grant No.
Here, for reasons of time economy, potential scan rates of 10 mV s -1 , higher than those routinely used in this type of measurements, are employed. The discussion is limited to the general information provided by this technique around the corrosion potential. Eventually, the discussion of the Tafel regions and the calculation of the corrosion resistance are made. The second part of the session is devoted to record the EIS data on eurocent coins in contact with the above electrolytes. (2D) topotactic solid-to-solid transformation involving the ingress/issue of K + ions into/from the solid lattice (MR) most solids are permeable to the diffusion of ions and electrons through their lattice (PD) from the model of Scholz, the peak potentials should vary with the concentration of supporting electrolyte Voltammetry upon varying the concentration of the supporting electrolyte Deviations from linearity in the E p vs.
Annex Writing "final test" It consists of three questions within the general types exemplified above. 1(A). -We obtain the following data corresponding to the variation of peak current with potential scan rate for the oxidation of ferrocene at Pt electrode (data recorded for a 2. 0 mM ferrocene solution in DMF using 0,10 M Bu 4 NPF 6 as the supporting electrolyte): Discuss if the process can be considered as diffusioncontrolled. 1(C). -We dispose of a Cu 2+ solution of unknown concentration. An aliquot of the same is spiked with different volumes of a standard solution of Cu 2+ and the peak current for the stripping peak recorded in a conventional experiment are recorded. Based in the following experimental data, calculate the concentration of Cu 2+ in the problem solution.
Acknowledgements The authors wish to thank  Prof. Fritz Scholz  by his constructive comments and suggestions during the preparation of the manuscript. Financial support from Project  PID2020-113022GB-I00  supported by  MCIN/AEI/10/13039/501100011033 ,  Fondo Europeo de Desarrollo Regional (ERDF) , and  Agencia Estatal de Investigación (AEI) , is gratefully acknowledged.
Acknowledgements We thank  Richard Kenyon  for suggesting investigating the generalized cluster modular group, and  Niklas Affolter ,  Tomas Berggren ,  Sunita Chepuri  and  Richard Kenyon  for fruitful discussions. We are also grateful to both referees for several comments which helped clarify the exposition. SR was partially supported by the  Agence Nationale de la Recherche , Grant Number  ANR-18-CE40-0033  ( ANR DIMERS ).
Statistical analyses The performance and robustness of the signal processing pipeline was evaluated by varying the signal-to-noise ratio (SNR) of optical mapping recordings during tachyarrhythmia induction (N = 5, see Supplementary Figures  S4-S8  for examples at baseline conditions). Non-parametric analyses of variance Friedman tests were used to identify statistical differences of signal characteristics and signal processing outputs across SNR populations. A multiple comparison assessment was also performed to compare results for individual SNR populations versus the baseline SNR population (established using a cut-off of 60Hz for a butterworth lowpass filter). Spatial correlations by pixel-to-pixel linear regression analyses were used for simulation data to compare eAT with uAT and pAT maps.
Funding This study received financial support from the  French Government  as part of the  "Investments of the Future" program  managed by the  National Research Agency (ANR) , Grant reference  ANR-10-IAHU-04 , funding from the  European Research Area in Cardiovascular Diseases (ERA-CVD) , grant reference  H2020-HCO-2015_680969  (MultiFib), funding from the  French Region Nouvelle Aquitaine , grant references  2016-1R 30113 0000 7550/ 2016-1R 30113 0000 7553 , and  ANR-19-ECVD-0006-01 , and funding from the  Leducq Foundation , grant reference  16CVD02  (RHYTHM).
Acknowledgements This work has received funding from the  JSPS   KAKENHI grant in aid for Scientific Research on Innovative Areas "Molecular Engine  (No. 8006 )"  18H05419 , the  JSPS KAKENHI   Grant-in-Aid for Challenging Research  ( 20K21131 ) and the  JSPS KAKENHI   Grant-in-Aid for Basic Research A  ( 22H00325 ). Dr. Toshio Nishino  is warmly thanked for providing the Figure 1d and  Mr. Shohei Katao  for his help in the single crystal XRD analysis. The  University Paul Sabatier (Toulouse)  and  NAIST  are also warmly thanked for providing a crossed position to GR.
Gold was sputtered on both sides of the pellet to guarantee good contact. A BioLogic MTZ-35 impedance analyzer was used to collect data in a frequency range of 3. 5 × 10 7 Hz to 1 Hz, under Ar atmosphere. The resulting data were fitted using the equivalent circuit model proposed in Ref. [94]. X-Ray Diffraction X-ray diffraction was performed using a Bruker D8 Advance powder diffractometer, with a copper anode (Cu -K α = 1. 540 59 Å). The powder XRD pattern was fitted using the Rietveld method as implemented in the Fullprof program  [95] , with a splitpseudo-Voigt function to model the peaks  [96]. 119 Sn Mössbauer Spectroscopy A lab-made constant acceleration Halder-type spectrometer operating in transmission geometry was used to carry out the Mössbauer analyses. The spectrometer was equipped with a radioactive source of 119m Sn (370 MBq) embedded in a CaSnO 3 matrix and maintained at room-temperature.
Experiments were performed with 50 mg to 70 mg of sample ([Sn] = 5 mg cm -2 to 8 mg cm -2 ) at room temperature (∼ 293 K) and 77 K using a liquid nitrogen bath cryostat. The Mössbauer hyperfine parameters (δ isomer shift, ∆ quadrupole splitting, Γ signal linewidth, G 11 Goldanskii-Karyagin factor and relative areas) were refined using the WinNormos software [97]. Isomer-shift values are reported relative to CaSnO 3 at room temperature. 19 F Solid-State NMR Quantitative 19 F Magic Angle Spinning (MAS) NMR spectra were recorded on Bruker Avance III spectrometers operating at B 0 = 7. 0 T ( 19 F Larmor frequency of 282. 4 MHz), using a 1. 3 mm CP-MAS probe head, and, for variable temperature experiments, using a 2. 5 mm double resonance ( 1 H/ 19 F-X) CP-MAS probe and a Bruker Cooling Unit (BCU-II).
Molecular-Dynamics Simulations To model the equilibrium structure and dynamics of c-BaSnF 4 , we performed ab initio molecular dynamics (AIMD) using the Vienna ab initio simulation package (VASP)  [103] [104] [105]. We used the revised Perdew-Burke-Ernzerhof generalized gradient approximation PBEsol exchangecorrelation function  [106]. Interactions between core and valence electrons were described within the projectoraugmented-wave (PAW) method  [107] , with cores of [Kr] 4d 10 for Ba, [Kr] for Sn, and  [He]  for F. We simulated a 6 × 6 × 6 supercell, starting from a cation-disordered fluorite structure with a special-quasi-random configuration of Ba and Sn over the Wyckoff 4a cation sites, that we generated using the icet package  [108]. This 6 × 6 × 6 special-quasi-random structure best approximates the Ba/Sn correlations for an infinite lattice with a fully-random arrangement of cations  [109, 110].
Our molecular dynamics simulation used a plane-wave cutoff of 350 eV with only the gamma point used for kspace sampling, and without spin-polarisation. The simulation was performed at 600 K and used a time-step of 2 fs. Before our production run, we obtained the 600 K equilibrium volume by running a preliminary series of simulations with different cell volumes for 8 ps each, and fitting the Birch-Murnaghan equation to the resulting energy-volume dataset. The simulation was run in the N V T ensemble using a Nosé-Hoover thermostat. Thermal equilibration was performed by running a 2 ps N V E run with temperature rescaling every 50 steps. The production run was 159 ps in length. To calculate fluoride-ion site occupancies and site-site transition frequencies, at each simulation timestep we assigned every fluoride ion to a distinct tetrahedral or octahedral site by projecting the instantaneous fluoride-ion positions onto polyhedral "sites" defined by the Wyckoff 4a positions as fixed vertex positions  (1).
Data and Code availability A complete dataset for the computational modelling and analysis described in this paper is available from the University of Bath Research Data Archive  [140]. This dataset contains inputs and outputs for all DFT calculations, plus scripts for analysis of the DFT data and for plotting Figs. 4, 5, 6, 7, 9, 10, 11 and 12(b). A subsidiary dataset containing only the figure-plotting scripts and relevant input data is available on GitHub  [141].
ACKNOWLEDGEMENTS B. M. , C. M. , and D. D. thank the  French National Research Agency  ( STORE-EX Labex  Project  ANR-10-LABX-76-01 ) for financial support. D. D. wishes to thank  A. Abouserie  (funded by the  INC-CNRS   Emergence program ) for conducting preliminary experiments. The work done at the  Advanced Photon Source , an  Office of Science User Facility operated  for the  U. S. Department of Energy (DOE) Office of Science  by  Argonne National Laboratory , was supported by the  U. S. DOE  under Contract No. DE-AC02-06CH11357. S. W. C. and B. J. M. thank the  Faraday Institution CATMAT  project ( EP/S003053/1 ,  FIRG016 ) for financial support. This work used the Michael computing (FIRG030), the  ARCHER2 UK National Supercomputer Service , with access provided by our membership of the  UK 's  HPC Materials Modelling Consortium  ( EP/R029431 ), and the  Isambard 2 UK  National Tier-2 HPC Service operated by GW4 and the  UK Met Office , and funded by  EPSRC  ( EP/T022078/1 ).
Acknowledgements We are grateful to  Dr. C. Yuan  for providing us with their shell-model Hamiltonian. The present work was in part supported by the  World Class University Project  ( R32-2008-000-10155-0 ),  NRF  grants ( NRF-2011-0006492 ,  NRF-2018R1A5A1025563 ) and  IBS  ( IBS-R031-D1 ,  2013M7A1A1075764 :RISP) in Korea, and  JSPS   KAK-ENHI  Grant Numbers  JP16H02179 ,  JP18H05404 ,  JP21H04465  and  MEXT KAKENHI  Grant Number  24105005  in Japan. N. L. A. , F. D. , J. G. , F. M. M. and N. A. O. acknowledge partial support from the  French-Japanese LIA-International   Associated Laboratory for Nuclear Structure Problems  as well as the French  ANR-14-CE33-0022-02 EX-PAND. A. N. and J. G. would like to acknowledge the  JSPS Invitation fellowship   program  for long term research in Japan at the  Tokyo Institute of Technology and RIKEN , respectively.
85 to 0. 59. 12, 19  The present data extend these observations to confirm that the photoconversion quantum yield of 1GM does not show a significant solvent polarity dependence (0. 6 ± 0. 1) in all of the solvents studied. The data for 1GM CT are very different. In nonpolar cyclohexane, the photoconversion yield is enhanced to a value near unity, the highest value reported (or possible) for any photomolecular motor. In sharp contrast, in polar methanol, the photoconversion quantum yield is reduced more than 10-fold. Even in chloroform (which has a much lower dielectric constant than methanol), the yield is similarly greatly reduced. In addition to its moderate polarity, CHCl 3 is also highly polarizable. To distinguish the effects of these solvent properties, the photoconversion yield was measured in the polarizable but weakly polar toluene.
In this case, a high photoconversion yield (0. 9) was observed. Thus, these data demonstrate that the isomerization yield of firstgeneration motors can be modified by CT substituents, introducing a remarkable solvent polarity dependence. These stationary state data show that the introduction of electron donor-acceptor substituents dramatically alters the photoconversion yield in the motor photocycle. The origin of this behavior was investigated by transient absorption (TA) spectroscopy applied to 1GM and 1GM CT. TA spectra were measured in methanol and cyclohexane in a probe window of 380-1250 nm. The data for 1GM in cyclohexane and methanol are presented in Figure  3a  and Figure  3c , respectively. The evolution follows the mechanism set out in Figure  1b. The intense narrow excited state absorption (ESA) feature near 800 nm and the stimulated emission near 500 nm are associated with the Franck-Condon (FC) bright state, and both decay in a few hundred femtoseconds to form the dark state, which has a broader, red-shifted ESA rising at wavelengths above 900 nm, no stimulated emission, and an additional absorption near 450 nm.
Methods Acquired were from the Human Urinary Proteome Database the urinary peptide data of 1850 healthy control (HC) and CKD (diabetic kidney disease-DKD, IgA nephropathy-IgAN, vasculitis) participants. The uniform manifold approximation and projection (UMAP) method was coupled to a support vector machine (SVM) algorithm. Binary (DKD, HC) and multiclass (DKD, HC, IgAN, vasculitis) classifications were performed, including or skipping the UMAP step. Last, the pipeline was compared to the current state-of-the-art singleaetiology CKD urinary models.
Applying urinary peptidomics combined with the support vector machine (SVM) algorithm demonstrated superior performance in comparison to the state-of-the-art  (16). Several specific SVM-based peptide panels have been established in the field of chronic kidney disease (CKD), such as the IgAN237  (17)  or the CKD273(18) panels, the latter being recognized with a letter of support from the U. S. Food and Drug Administration (FDA)  (19)  and implemented in a clinical trial for early detection of diabetic kidney disease  (20). Building on available 1850 urine peptidomics datasets obtained from the Human Urinary Proteome Database  (10) , our aim was to establish a pipeline for the non-invasive differential diagnosis of CKD aetiologies in a novel approach, harnessing the dimensionality reduction and visualization capabilities of UMAP in a proof-of-concept study.
Methods Anonymized peptidomics data of 1850 urine samples corresponding to healthy controls (HC) and CKD participants of various aetiologies were acquired from the Human Urinary Proteome Database  (10). The CKD aetiology selection criterion was based on a minimum of 70 samples. The HC samples were derived from participants without either signs of CKD or significant loss of kidney function (eGFR ≥ 60 mL/min/1. 73m 2 ) (n = 504). The CKD samples were derived from participants diagnosed with one of the following CKD aetiologies: IgAN (n = 737), DKD (n = 534), and vasculitis (n = 75). The study design is depicted in Figure  1. All datasets were from previously published studies and fully anonymized. The studies respected the regulations for protecting participants in medical research and the Declaration of Helsinki (2013).
This study was approved by the ethics committee of the Friedrich-Alexander Universität Erlangen-Nürnberg, Germany (ethic approval code 264_20 B for the nephrological biobank and ethic approval code 221_20 B for the urinary proteomics analysis). The methods used in this study are described in detail in the appendix. In brief, initially a urinary peptidomics protocol involving CE-MS, peptide sequencing and data evaluation was performed (appendix p. 1). UMAP  (5, 6)  was applied to embed the data into a lower dimension (3D), as also described in several sources  (21, 22)  and, for the multiclass classification only, oversampling  (23)  was used to account for the class imbalance (appendix p. 2). The entire peptidomics dataset was randomly split in train and test sets based on sample groups in a 75:25 ratio for classification purposes (appendix pp.
2-3). The train set features with the disease labels, were fed to a SVM classifier to generate a model able to distinguish participants based on their disease state. To this end, models were trained within a three-times repeated, four-fold cross validation (CV), in which Bayesian optimization  (24)  was used as an iterative search method in the context of hyperparameter tuning. The machine learning pipeline was based on R statistical software (appendix p. 3).
Results Urinary peptidomics data of 1850 individuals were extracted from the Human Urinary Proteome Database  (10). This set included 504 HC participants as well as 534 patients with DKD, 737 with IgAN, and 75 with vasculitis for which the available clinical information is presented in Table  1. A machine learning pipeline was implemented to discriminate DKD and HC (binary classification) as well as DKD, HC, IgAN, and vasculitis (multiclass classification) with the main goal to maximize the separation of the participants based on their CKD aetiology diagnosis (class). The binary differentiation was used as an initial evaluation to explore the UMAP capabilities before moving on to the more complex challenge of multiple CKD aetiologies differentiation. In this first step, DKD was selected, because it is the most common CKD aetiology; in addition, in our dataset the DKD and HC classes were balanced in terms of sample sizes.
In that particular classification (including the UMAP step), during predictions on the independent test set, the selected model had the lowest performance distinguishing DKD, assigning a substantial part of its participants to the former majority class, IgAN. Nevertheless, although DKD and IgAN are not that clinically similar, their routine treatment involves several common aspects, especially the anti-hypertension treatment involving angiotensin-converting-enzyme inhibitors and angiotensin II receptor blockers, as well as the recently implemented, SGLT-2 inhibitors. As a dimensionality reduction method, UMAP is able to remove, at least to a degree, the noise of a dataset and thus increase the model performance. Considering the SVM-alone pipeline, this was not observed here, which we speculate may be linked to the noise reduction already achieved via applying the 30% peptide frequency threshold considering only the sequenced peptides as part of the routine analysis in the peptidomics field.