dataesr/scientific-paragraphs-categorization huggingface dataset:
The highest molar extinction coefficient of the series was determined for Dye 12, peaking at 44,800 L -1. M -1. A slightly lower molar extinction coefficient (ε = 35,950 L -1. M -1 ) was determined for Dye 13, also bearing a rhodanine-based electron acceptor (see Table  1 ). Theoretical studies were also carried out to investigate the energy levels as well as the molecular orbital (M. O. ) compositions of the different dyes. DFT calculations were performed for all dyes at the wb97xd/6-311g(d,p) level of theory using the Gaussian 09 program to determine the transitions involved in the different absorption peaks. DMF was used as the solvent model with a polarizable continuum model (PCM)  [98] [99] [100] [101] [102] [103] [104]. Theoretical UV-visible absorption These results are consistent with previous works reported in the literature evidencing the remarkable electron-withdrawing ability of this group  [32, 97].
O. ) compositions of the different dyes. DFT calculations were performed for all dyes at the wb97xd/6-311g(d,p) level of theory using the Gaussian 09 program to determine the transitions involved in the different absorption peaks. DMF was used as the solvent model with a polarizable continuum model (PCM)  [98] [99] [100] [101] [102] [103] [104]. Theoretical UV-visible absorption spectra were obtained by TD-DFT calculations and the different spectra are presented in Figure  4. Optical characteristics are summarized in Table  1. The position of the absorption maxima was determined from the theoretical spectra. As shown in Table  1 , for all dyes, the positions of the theoretical absorption maxima were determined in DMF as the solvent. Noticeably, all theoretical absorption maxima were blue-shifted compared to the experimental ones.
General Information All reagents and solvents were purchased from Aldrich, Alfa Aesar or TCI Europe and used as received, without further purification. Mass spectroscopy was performed by the Spectropole of Aix Marseille University. ESI mass spectral analyses were recorded with a 3200 QTRAP (Applied Biosystems SCIEX) mass spectrometer. The HRMS mass spectral analysis was performed with a QStar Elite (Applied Biosystems SCIEX) mass spectrometer. Elemental analyses were recorded with a Thermo Finnigan EA 1112 elemental analysis apparatus driven by the Eager 300 software. 1 H and 13 C NMR spectra (More details could be found in Supplementary Materials) were determined at room temperature in 5 mm o. d. tubes on a Bruker Avance 400 spectrometer of the Spectropole: 1 H (400 MHz) and 13 C (100 MHz).
Acknowledgments: The authors thank  Aix Marseille University  and the  Centre National de la Recherche Scientifique (CNRS)  for the financial support. This work received funding from the  CY Initiative of Excellence  (grant " Investissements d'Avenir "  ANR-16-IDEX-0008 ) and was developed during Frédéric Dumur's stay at the  CY Advanced Studies , whose support is gratefully acknowledged. Nicolas Giacoletto  is acknowledged for his kind help during the TGA measurements performed during his short stay at LPPI. Severine Alfonsi  is also acknowledged for her kind help during TGA measurements.
To compare two libraries, connectivity indices are calculated for the corresponding graphs, allowing discrimination between similar versus dissimilar pairs of datasets. However, the explicit pairwise compound-to-compound similarity calculations limit the application of this approach to rather small datasets. To solve this problem, modification of the fingerprint-based similarity metrics for library comparison, avoiding calculation of the entire similarity matrix, was introduced by Miranda-Quintana et al. 5  Proposed extended similarity coefficients were then applied for the visualization of the similarity relationships between libraries via chemical library networks 6 by analogy to above-mentioned chemical space networks. The aforementioned methods however do not intuitively explain why some libraries are said to be similar.
However, the comparison of the internal diversity of libraries instead of the similarity between them is much less informative: a library can be internally highly diverse but have a very similar chemical composition to another equally diverse library. In another library representation by a Database Fingerprint (DFP), proposed by Fernández-de Gortari et al. 8  , the on-bits correspond to the most frequent fragments occurring in numerous molecules from the analyzed library. Even though DFP allows to incorporate the main structural information of the library, it ignores finer differences between the collections that might lie in the distribution of the less frequent structural fragments or mutual occurrence and rearrangements of several fragments in different groups of compounds. There is also no possibility to include property information along with the structural one into the comparison using DFPs.
Data 1. ChEMBL The ChEMBL dataset (version 28) was used here as a reference library. It was downloaded and standardized in our previous work  10  according to the approach implemented on the Virtual Screening Web Server of the Laboratory of Chemoinformatics at the University of Strasbourg, using the ChemAxon Standardizer  11. This procedure included dearomatization and final aromatization (heterocycles like pyridone are not aromatized), dealkalization, conversion to canonical SMILES, removal of salts and mixtures, neutralization of all species, except nitrogen(IV), generation of the major tautomer according to ChemAxon. It resulted in 1 853 565 unique ChEMBL compounds. This set is extremely diverse: for example, molecular mass spans a range between 7 g/mol and 2255 g/mol. In principle, there is no limitation in size or complexity for molecules in DELs.
Chemical Library Space (CLS) The conventional way of library analysis consists in a detailed investigation of its compound space where each compound is defined by molecular descriptors -in our case ISIDA fragment counts  18. These fragments are composing elements of the molecule and their combinations define molecular properties. However, the structural fragment level is too detailed for characterizing the whole library as its composing elements that define properties of a mixture are individual compounds themselves. It also makes little sense to build a cumulated count of all fragments seen in the members of a library, because this vector loses the key information on how those fragments were initially distributed in individual compounds. In order to generalize the structural information of the library, one way would be to somehow encode the "chemotype" counts -the number of compounds of a particular "chemotype" present in a library.
direction and diffuse transmittance) factors minimizing the root mean square deviation from the measured spectra. The algorithm utilizes the fmincon function from Matlab® with a sequential quadratic programming "sqp" algorithm in order to minimize the root mean square difference between the prediction and the measurement of the directional and diffuse reflectance (resp. transmittance) factors for each wavelength. The boundaries for the optimization were 0 and 10000 m -1 for the absorption and scattering coefficients, while the asymmetry parameter g(λ) was restricted between -0. 25 and 0. 999 (as in Rozé's tables) and n(λ) was restricted between 1. 01 and 2. First guesses are fed as starting points to the algorithm: n 0 = 1. 5, g 0 = 0. 7, = ( ) k In h T r 0 1 (1 ) tot 0 2 and = ( ) s In h T r 0 1 (1 ) cc 0 2.
The algorithm is fast and stable regardless of the initial parameters. However, to ensure that the local optimization algorithm converges towards the best solution possible, a global optimization algorithm was also implemented, using the GlobalSearch Matlab® function. This algorithm is way more robust in finding the best solution, but is very time-consuming (up to 10 s for each wavelength, versus 0. 01 s for the local algorithm). In every test carried out, the global optimization and local optimization algorithms were found to converge towards the same solutions (deviation below 10 -4 %), which confirms that the local optimization algorithm can be safely used. This optimization algorithm enables to extract the optical parameters k(λ), s(λ), g(λ), n(λ), ζ(λ) and ε(λ) of a material, which is referred to as the calibration process.
Acknowledgements We are indebted to all our collaborators whose names appear in the references for their intellectual and experimental contributions to this work. This work was partially supported by  Normandie Université (NU) , the  Région Normandie , the  Centre National de la Recherche Scientifique (CNRS) ,  Université de Rouen Normandie (URN) ,  INSA Rouen Normandie ,  Labex SynOrg  ( ANR-11-LABX0029 ), the graduate school for research  XL-Chem  ( ANR-18-EURE-0020 XL CHEM ),  Innovation Chimie Carnot  ( I2 C ) and the  Agence National pour la Recherche  ( ANR-CE07-0004-1 ). T. B. thanks the  Labex SynOrg  ( ANR-11-LABX-0029 ) and the  Région Normandie  for a doctoral fellowship ( RIN 50 % program ). M. R. thanks the  Region Normandy (RIN ZIRCO_PHOTO_ COP)  for a postdoctoral fellowship.
Acknowledgments Financial support from the  China Scholarship Council (CSC , PhD grant to  Yue Liu ), the  MAT2017-88752-R Retos  Project from the  Ministerio de Economía, Industria y Competitividad, gobierno de España  ( Dr Sergio Moya ), the  Universities of Bordeaux and Rennes  and the  Centre National de la Recherche Scientifique (CNRS)  is gratefully acknowledged.
λ max (nm) (ε(M -1 cm -1 )) E ox (Fe II /Fe III ) (V SCE -1 ) E Red (V SCE -1 ) ΔE C0 To provide a quantitative framework to the observed fs-TAS results and given the concerted nature of temporal evolution of different transient spectral features, we performed a global analysis  39  using GloTarAn software  40  assuming a sequential decay model convoluted with a Gaussian instrument response function (IRF). The data could be satisfactorily described by a sum of 4 exponential functions (see the ESI † for the discussion on the relevance of the 4 th exponential function). The corresponding decay time constants are reported in Table  3  and the evolution associated difference spectra (EADS) for CTh3 are shown in Fig. 6. In accordance with the qualitative analysis of the experimental data, the first sub-100 fs component revealed by the global analysis accounts for the initial blue-shift of both high and low-energy ESA bands.
General Solvents and commercially available reagents were used as received. Thin layer chromatography (TLC) was performed by using silica gel 60 F-254 (Merck) plates and visualized under UV light. Chromatographic purification was performed by using silica gel 60 (0. 063-0. 2 mm per 70-230 mesh). 1 H (400 MHz) and  13  C NMR (100 MHz) spectra were taken on a DRX400 Bruker spectrometer at ambient temperature. The chemical shifts (δ), were calibrated by using either tetramethylsilane (TMS) or signals from the residual protons of the deuterated solvents and are reported in parts per million ( ppm) from low to high field, NMR signal assignments for L2, L3, CTh, CTh2 and CTh3 are detailed in the ESI. † High-resolution mass spectrometry (HRMS) data was obtained by using Bruker micrOTOF-Q spectrometer.
Acknowledgements The Strasbourg team is grateful to  Dr O. Crégut  for expert help in laser maintenance and development of data acquisition software, and for the funding provided by the  ITI 2021-28 program  of the  University of Strasbourg ,  CNRS  and  Inserm , supported by  IdEx Unistra  ( ANR 10 IDEX 0002 ), by  SFRI STRAT ' US  project ( ANR 20 SFRI 0012 ) and  EUR QMAT ANR-17-EURE-0024  under the framework of the  French Investments for the Future Program. The L2CM thanks the  French Agence Nationale de Recherche  ( ANR-16-CE07-0013-02 ),  Lorraine Université d'Excellence  ( IMPACT N4S ) and the  European Regional Development Funds  ( Programme opérationnel FEDER-FSE Lorraine et Massif des Vosges 2014-2020/"Fire Light " project: " Photo-bio-active molecules and nanoparticles"  for financial support and grants to A.
As can be seen from Fig. 1 , we have synthesised 11 cations, 10 of which are direct structural isomers for [C 11 C 1 im] + , by re-arranging the alkyl chain; [C 7 CycloC 5 ] + is not strictly a structural isomer of [C 11 C 1 im] + , as it has one less hydrogen atom. The studied dataset consists of 8 cations with linear chains, 2 of which have a methylated C 2 position, 3 cations with one branched alkyl chain with different lengths and 1 cation with a cyclic chain. It is important to emphasise here that this dataset does not represent all the possible structural isomers of the model cation, as according to the cation isomer enumeration performed by Koutsoukos et al. ,  3  for a dialkylimidazolium cation with 12 total alkyl chain carbons with no heteroatoms present, there are more than 70 000 structural isomers.
Linear fits were performed to the density measurements, with an R 2 4 0. 9999 in all cases. The raw density data, as well as the fitted lines, are shown in the ESI, † Tables  S1  and S2. The microviscometer is based on Hoeppler's falling ball principle that measures the rolling time of a ball through transparent and opaque liquids confined in a glass microcapillary. The accuracy of this module is 0. 05% in the viscosity and 0. 02 1C in the temperature. A 1. 8 mm diameter capillary was calibrated in order to cover the whole range of the sample viscosities in the temperature range of the analysis. The calibration was performed with a substance of precisely known viscosities, APN26, and a ball with specific and known material, geometry and density. The viscosity data were successfully fitted to the Vogel -Fulcher -Tammann (VFT) equation (eqn (S4), ESI †).
The samples were loaded into polycarbonate capillaries. All measurements were performed at room temperature (25 1C). The DAWNDiamond (v2. 26. 0) software  21  was used to integrate the scattering patterns to give 1D scattering plots of intensity against scattering vector, q (where q is the scattering vector defined as q = 2p/d, with d representing a real space distance) and subtract the background scattering from an empty polycarbonate capillary tube.
This work clearly demonstrates that the many-body interactions inherent for the ionic liquids significantly affect their physical properties, even when minor alterations are introduced on the structure. Not all properties are equally affected by the symmetry though, as for example although the SAXS/ WAXS data show clear differentiations in the nanostructuring even for the linear analogues, macroscopic viscosities and densities are not significantly affected. Expanding the studied dataset to include more examples, possibly with introducing alkylations of the C 4/5 positions of the imidazolium ring or by studying ionic liquids with different sizes of alkyl chains, could help understanding better the effects of symmetry on the physical properties of ionic liquids and even create models which can help structure-property relationship predictions.
Acknowledgements The authors thank  Dr Lisa Haigh  for the mass spectrometry measurements and  Dr Peter Haycock  and  Imperial College's NMR Suite  for the PFGSTE NMR measurements. We acknowledge the  Diamond Light Source  for the time on the Beamline I22 under proposal  SM29165  and thank  Dr Andy Smith  for his assistance during our experiments. J. A. and M. C. G. thank  IDEX Lyon  ( ANR-16-IDEX-0005 ) for financial support.
Acknowledgements The authors are grateful to  Prof. Dr. R. Ludwig ,  BOKU -University of Natural Resources and Life Sciences, Vienna ; for the provision of the FAD-GDH. We acknowledge funding from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) in the framework of the SPP2240 (e-biotech) [445820469] and the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie MSCA-ITN "ImplantSens " [813006].
[9]  Achieving independent alignment of a molecule in several media is however a major challenge, and in particular cases no media with independent alignments can be found. [10]  Obtaining a sufficient number of independent datasets is often the major bottleneck faced in the usage of RDCs for structural and dynamic characterization of biomolecules. Type 1 pili from Escherichia coli are filamentous, supramolecular protein complexes extending from the surface of the cell into the extracellular space and consisting of five different structural subunits: FimH, FimG, FimF, FimA and FimI (Figure  1A ). A single copy of FimH is located at the tip of the pilus and is followed by the two adaptor proteins FimG and FimF, thus forming a short, linear tip fibrillum. [12]  At its proximal end, the tip fibrillum connects to the pilus rod, a rigid and helical quaternary structure that has a diameter of 7 nm, a length of up to � 2 μm and is formed through assembly of hundreds to thousands of copies of the main structural subunit FimA.
Acknowledgements We are grateful to  Prof. Gerhard Wider  and  Dr. Fred Damberger  for discussions;  Dr. Landry Gayet  for technical support and advices for RNA production and purification;  Dr. Marina Rubini  for providing plasmid pQE-31Ub;  Dr. Serge Chesnov  from the  Functional Genomics Center Zurich  for performing the MS analysis;  Helene Fäh-Rechsteiner  for excellent technical support; the  Scientific Center for Optical and Electron Microscopy (ScopeM) of ETH Zurich  and  Peter Tittmann  for instrument access and maintenance; and  ETH Zurich  for financial support of this research. L. S. acknowledges support from the  European Union   Horizon 2020 Research and Innovation Program  under  Marie Sklodowska-Curie  (grant agreement  707635 ) and from the  IDEX de l'Université de Lyon (Impulsion).
, 2008 ; Underhill et al. , 2011 ; Thiessen et al. , 2013 ; Janve et al. , 2013 ; Turati et al. , 2015 ; Hakkarainen et al. , 2016 ; Khodanovich et al. , 2017 ; Khodanovich et al. , 2019 ; Soustelle et al. , 2019 )  were performed. In a second step, taking advantage of the volumetric nature of the MRI datasets, ROI-based analyses along the rostro-caudal mCC and voxel-based analyses were carried out for ihMT, MPF and R 1 in order to identify the spatial extent and the significant signal variations over the time course of the acute cuprizone model.
MRI post-processing The derivation of the MRI metrics was accomplished following the same procedure thoroughly described in previous work (see Fig. 1  in reference  ( Hertanu et al. , 2022b ) ). Briefly, ihMTsat (i. e. , an ihMT metric unbiased for B 1 + /T 1 effects)  ( Hertanu et al. , 2022b ; Munsch et al. , 2021 ; Rowley et al. , 2021 )  was calculated from MT-weighted images acquired with single-frequency and dual-frequency off-resonance saturation using a home-built pipeline available at https://github. com/ lsoustelle/SP _ qMT _ proc (hash f3f49e0). Increasing switching time values ( Δt = 0. 0; 0. 8; 3. 2 ms) used for the dual offset saturation led to the derivation of ihMT high-pass T 1D -filters with increasing T 1D -cutoff values  ( Hertanu et al. , 2022a ). Note that Δt of 0. 0 ms (simultaneous dual frequency offset saturation) was obtained by cosine-modulating the RF saturation pulses.
In addition a band-pass T 1D -filter was generated from the difference of ihMTsat images obtained with Δt 0. 0 and Δt 0. 8 as described in  ( Hertanu et al. , 2022a ). The macromolecular proton fraction (MPF) metric was derived from the binary spin-bath model for MT  ( Yarnykh, 2002 ; Yarnykh, 2012 ; Yarnykh et al. , 2020 ) , using the single-point measurement strategy with optimally constrained parameters  ( Soustelle et al. , 2020 ). The longitudinal relaxation rate R 1 was estimated using the Ernst equation  ( Chang et al. , 2008 )  applied to the VFA dataset. MPF and R 1 were both corrected for B 1 + inhomogeneities  ( Yarnykh et al. , 2020 )  estimated from the AFI dataset using a home-built pipeline available at https://github. com/lsoustelle/SP _ qMT _ proc (hash 05f43a4). Following the pipeline presented in  ( Hertanu et al.
Comparison of MRI metrics with histology metrics The individual native MRI images of mice in the time point groups were considered for the comparison with histology. Mean values were quantified in mCC ANT , mCC POST and CTX regions of interest for each mouse. Due to experimental acquisition issues (motion and respiratory instabilities during the acquisition protocol), one mouse at W 0 and one mouse at W 5 had to be discarded from the analysis. The normality of the data was assessed in JMP (v. 16. 0. 0; SAS Institute) using the Shapiro-Wilk test. The homogeneity of variance was examined using Bartlett's test. A one-way analysis of variance (ANOVA) for each phase of the study (demyelination and remyelination) was followed by post-hoc Tukey's HSD two-by-two tests to determine significant differences in the signal dynamics of all MRI and histology metrics for the three abovementioned ROIs.
Pearson's correlations were performed between MRI metrics and the LFB and GFP signals for the demyelination phase, the remyelination phase, as well as for the entire temporal dynamics (demyelination + remyelination). Statistical significance was defined as a p-value below the 0. 05 threshold. Pairs of significant correlation coefficients were tested for differences using the Steiger's modification of Dunn and Clark's z approach  ( Steiger, 1980 )  and the Zou's confidence interval  ( Zou, 2007 )  from the cocor package  ( Diedenhofen and Musch, 2015 )  for overlapping correlations based on dependent groups in  R (v. 1. 4. 1106; RStudio, PBC)  in order to determine to which histology reference the MRI metrics correlated best.
, 2014 )  and 10,000 permutations  ( Wakana et al. , 2007 ). From the family-wise error corrected (1 -p-value ) maps, a binary mask was generated by thresholding all p-values below 0. 01. The mask was afterwards applied on the F-contrast maps to obtain the significant F-statistics values. The AimsMesh function from the BrainVISA software  ( Rivière et al. , 2003 )  (v. 5. 0. 4, IFR49) was used with a smoothing option of 500 iterations in order to generate meshes from the slice-wise binary masks of the brain and of the corpus callosum. The significant F-maps were bi-narized and the same approach was used to generate the corresponding meshes. The Anatomist interface from BrainVISA was used to visualize all the resulting renderings, which were overlapped in order to obtain the 3D localization of significant clusters in the brain.
Acknowledgements This work was funded by  ANR-17-CE18-0030 , the  VERISMO  project, and partly funded by  France Life Imaging  (Grant No. ANR-11-INBS-0006  ), ARSEP 2020 , and CARNOT STAR 2020. We thank  Nathalie Cuge  for animal handling,  Martine Armand  and  Christophe Vilmen  for fruitful discussions, advice and help with the logistics of the cuprizone experiments.
Electrochemical measurements To perform electrochemical measurements, a classical setup consisting of a single compartment glass cell and three electrodes system was used. The system composed of a reference Scheme 1 A flowchart of the preparation procedure of NE extract inhibitor. electrode (saturated calomel (SCE)), a counter electrode made of a platinum grid with a large surface area and a working electrode made of mild steel employing AUTOLABÒ potentiostat/galvaostat (PGSTAT30, Ecochemie, Netherlands) controlled by NovaÒ software. The intensity-potential curves were obtained in potentiodynamic mode. The potential applied to the sample varied continuously, with a scan rate of 1 mV s À1 in the potential range of À1000 to À500 mV. The inhibition efficiency IE(%) is calculated using the following formula  (Singh et al.