dataesr/scientific-paragraphs-categorization huggingface dataset:
Single Crystal X-ray Diffraction (XRD) Crystallographic data for single-crystal X-ray diffraction study are summarized in Table  1. These data can be obtained free of charge from The Cambridge Crystallographic Data Center via https: //www. ccdc. cam. ac. uk/structures-beta/under deposit number: 1879607. Crystals of D6R@α-CD were selected under a polarizing optical microscope and glued in paratone oil to prevent any loss of crystallization water. X-ray intensity data were collected at a low temperature (T = 230 K) on a Bruker D8 VENTURE diffractometer (Karlsruhe, Germany) equipped with a PHOTON 100 CMOS bidimensional detector using a high brilliance IµS micro-focus X-ray Mo Kα mono-chromatized radiation (λ = 0. 71073 Å). Data reduction was accomplished by using SAINT V7. 53a. The substantial redundancy in data allowed a semi-empirical absorption correction (SADABS V2.
10) to be applied based on multiple measurements of equivalent reflections. Using Olex2  [20] , the structure was solved with the ShelXT structure solution program  [21]  using Intrinsic Phasing, and refined with the ShelXL  [22]  refinement package using Least Squares minimization. Numerous water molecules located inside the cavities were disordered. Thereby, the contribution of solvent-electron density was removed using the SQUEEZE routine in PLATON  [23] , which produced a set of solvent-free diffraction intensities. The remaining non-hydrogen atoms were located from Fourier differences and were refined with anisotropic thermal parameters. Positions of the hydrogen atoms belonging to the α-CD were calculated and refined isotropically by using the gliding mode.
However, the disposition of the remaining 10 potassium atoms surrounding the D6R unit is different in the two polymorphic forms. Considering the silicate cage and their 12 potassium counter ions, no symmetry is present in the triclinic form (Figure  S4  in Supplementary Materials) while the orthorhombic form presents a C 2 symmetry axis (Figure  1c, d ). This difference between the two crystallographic systems appears due to the positions of the potassium atoms around the cyclodextrin-silicate assembly. Crystals 2018, 8, x FOR PEER REVIEW 5 of 13 SAINT V7. 53a. The substantial redundancy in data allowed a semi-empirical absorption correction (SADABS V2. 10) to be applied based on multiple measurements of equivalent reflections. Using Olex2  [20] , the structure was solved with the ShelXT structure solution program  [21]  using Intrinsic Phasing, and refined with the ShelXL  [22]  refinement package using Least Squares minimization.
CCDC XXXXXXX (4-4oH), CCDC XXXXXXX (4-13), and XXXXXXX (4-17) contain the supplementary crystallographic data for this paper. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre. 19b, 24] The ratio of open to closed isomer after several irradiation cycles and in the photostationary state (PSS) was determined by injection of irradiated samples into a UPLC and detection by the UV/vis diode array, integrated at the isosbestic point of the corresponding irradiation spectrum.
Spectrophotometric Titration in Water/Acetonitrile The spectrophotometric titrations were conducted in an aqueous, buffered solution containing (pH determined in acetonitrile/water with electrodes calibrated in the same acetonitrile/water mixture) the conversion parameter published by Bosch et. al was used. [139] 4 COMPUTATIONAL METHODS -ACID-CATALYZED CYCLOREVERSION The molecular structures (3-1o, 3-1c, 3-1o + , 3-1c + , details see appendix 7. 4. 1) were generated and pre-optimized by MM2 with PerkinElmer CambridgeSoft Chem3D Ultra 10. 0. Complete geometry optimizations and harmonic frequency calculations of the reactants, products, and transition states were performed using Gaussian 09 Rev. C. 01 program package. [140] Transition states were computed with the help of Opt=TS keyword using Berny algorithm to optimize to a transition state rather than a local minimum.
Experimental section All melting points (Yanagimoto MP-S1) are uncorrected. 1 H and  13  C NMR spectra (300 MHz) were recorded on a Nippon Denshi JEOL FT-300 NMR spectrometer in deuteriochloroform with Me4Si as an internal reference. Mass spectra were obtained on a Nippon Denshi JMS-HX110A Ultrahigh Performance Mass Spectrometer at 75 eV using a direct-inlet system. Elemental analyses were performed by Yanaco MT-5. UV-vis spectra were recorded on a Perkin Elmer Lambda 19 UV/VIS/NIR spectrometer. Here suitable single crystals were selected and mounted on a Bruker APEX 2 CCD diffractometer equipped with graphitemonochromated Mo-Kα radiation. Data were corrected for Lorentz and polarisation effects and for absorption.
3 psi, 3 s). Sample polymers were prepared, 24 h before analysis, by dissolving the PAMAMPS at 2 g/L in water. If necessary, 0. 05% (v/v) mesityl oxide was added to the sample as a neutral marker. All separations were performed in the positive polarity mode. The temperature of the capillary cassette was maintained constant at 25 • C. Data were collected at 200 nm. For each run, electroosmotic mobility was calculated from the migration time of a neutral marker (mesityl oxide). Electropherograms were plotted in effective mobility scale by changing the x-axis using the following equation: µ ep = 1 t - 1 t eo lL V (1) where µ ep is the effective mobility, l is the effective capillary length to the detection point, L is the total capillary length, V is the applied voltage, t eo is the detection time of the neutral marker, and t is the migration time.
Taking the 150 mM ionic strength as the optimal condition of separation of the PAMAMPS mixture, the entire charge-density distribution was plotted in Figure  4C  using the data processing described in detail in Reference  [17]. In brief, the raw (time-scale) electropherogram was first converted into a mass-weighted distribution of migration times h(t), by dividing the recorded absorbance S(t) by the migration time (h(t) = S(t)/t). Then, this distribution was converted into a mass-weighted effective mobility distribution (j(µ ep ) as a function of µ ep ) by changing the x-axis from time to effective mobility, and by multiplying the time-corrected absorbance by t 2 (j(µ ep ) = h(t) × t 2 ). Finally, the mass-weighted charge density distribution (j(f ) as a function of f ) was obtained by changing the x-axis from µ ep into f using a correlation between the average effective mobility and the average f value obtained by 1 H NMR.
Acknowledgments: Part of the work presented here was funded by  ANR (French National Research Agency)  under a  MATISSE grant  ( ANR-11-BS08-001 ) as well as the  Indo-French Centre for the Promotion of Advanced Research (IFCPAR , project No. 3708-2 ) and the  CNRS  ( Centre National de la Recherche Scientifique ). The author is indebted to the following colleagues for supervision: (1) EXAFS measurements,  Jean-Louis Bantignies ; (2) C-AFM measurements,  Jérôme Faure-Vincent ; (3) nanotubes and bicopper complex syntheses,  Philippe Mésini ; (4) OPVOH synthesis,  Ayyapanpillai Ajayaghosh.
Applications of such models are generally limited by their rigorous demands for preliminary information and numerous datasets comprising high resolution data, for example, about the number of pollution sources, properties of soils from the source areas, and the comparative stability of source transport processes  [22, 23]. Principle component analysis (PCA) is a widely-used multi-statistical analysis method whose purpose is to switch multidimensional data into a couple of relevant variables that simultaneously maintain the information involved in the original variables and diminish the dimensions of the data  [24] [25] [26]. While finite mixture distribution model (FMDM) is a mathematical approach for statistical modeling of massive random data sets  [27]. It can be applied to identify whether the trace elements from soil samples originate from a natural background or anthropogenic background, without knowing the general contaminated condition of the full areal extent of the study area  [28, 29].
Auxiliary Variables Data The study area is known for its industrial prosperity, and its total industrial output value accounted for 45% of its whole economic output value in 2016. We obtained information of 48,206 industrial enterprises in the study area from Baidu Map POI (Point of Interest) data that listed all the industrial enterprises in 2016. We removed data for irrelevant non-industrial enterprises, and then classified the industrial enterprises into four categories: textiles, metal products, chemical products, and other industries. We obtained information about the soil parent material in the study area from a 1:20,000 soil map of Zhejiang Province (1990)  [32]  and the soil types from a 1:50,000 Map of Chinese Soil (1990) published by the National Soil Survey of China, respectively.
Data Analysis In this study, we did all the statistical analyses with Microsoft Excel 2016 (Office 2016, Redmond, WA, USA). We used the geostatistical analyst tool in ArcGIS 10. 2 (ESRI, ArcGIS10. 2, Redlands, CA, USA) for the kriging interpolation. Principal component analysis of the seven soil trace elements was carried out with the psych package of R3. 4. 2  [40]  and the FMDM was done in R3. 4. 2 using Mclust package  [41].
Acknowledgements Some results described in this review were obtained thanks to a  Marie Skłodowska Curie Intra-European Fellowship  from  European Commission  (No. 623501 ) and to the  French National Research Agency  ( ANR-11-JS08-0005 ). The PhD fellowships of DV and JT are supported by the  European Union 's  Horizon 2020 research and innovation programme   under Marie Skłodowska Curie  grant agreement no. 642028  (NABBA) and by the  French National Research Agency  ( ANR-15-CE08-0019 ), respectively. CNRS and  Université Paris-Sud  are also acknowledged for financial support.
Acknowledgements Quentin Huaulmé thanks MENR  for PhD fellowship,  Nicolas Leclerc  and  Gilles Ulrich  thanks the  French National Research Agency  ( ANR   ORION  project,  ANR-13-PRGE-0001 ) and the  Interreg IV-A program  under project  C25   Rhin-Solar  for their financial support. Denis Jacquemin  thanks the  GENCI-CINES/IDRIS , the  CCIPL (Centre de Calcul Intensif des Pays de Loire)  and a local Troy cluster.
ACKNOWLEDGMENTS A. B. T. and A. D. S. gratefully acknowledge Grant No. 4. 1671. 2017/4. 6  from the  Ministry of Education and Science of the Russian Federation. D. M. P. H. is grateful to the  Science and Technology Facilities Council (United Kingdom)  for financial support. We acknowledge the provision of beamtime by  Synchrotron Soleil  (beamtime Proposal No. 20131329 ), and we thank the technical staff at Soleil for their support and for the smooth operation of the facility. We are grateful for access to the  University of Nottingham High Performance Computing Facility.
Preliminary results The capture yield analysis in the Resolved Resonance Region (RRR) is analysed and parameterized using the Bayesian R-matrix analysis code SAMMY  [9]. In the case of 171 Tm, it has been possible to analyse the RRR up to 700 eV, which allowed to extract individual resonance parameters for 28 s-wave resonances for the first time ever  [10]. For the analysis of the 203-204 Tl sample (n,γ) yield, the first step was to analyze the 203 Tl pure sample yield with the highest possible accuracy, in order to reliably account for it in the later analysis of the 204 Tl(n,γ). In 203 Tl(n,γ) more than 70 resonances, from 37 eV up to 26 keV, have been identified and analysed with SAMMY. This included the first experimental measurement for resonances at a neutron energy lower than 3 keV (fig.
The authors acknowledge financial support by the  Spanish   FPA2014-52823-C2-2-P project , by the  EC Marie Curie Action "NeutAndalus"  ( FP7-PEOPLE-2012-CIG-334315 ), by the  ARGOS scholarship of the Spanish Nuclear Safety Council (CSN)  and the  Universitat Politècnica de Catalunya , and by the  University of Sevilla  via the  VI PPIT-US program.
Chemical shifts are given in ppm (δ) and are referenced to the internal solvent signal or to TMS used as an internal standard. Coupling constants J are given in Hz. Carbon multiplicities were determined by DEPT135 experiment. Diagnostic correlations were obtained by two-dimensional COSY, HSQC and NOESY experiments. Infrared spectra (IR) were recorded on a Perkin-Elmer FT-IR system (Waltham, MA, USA) using diamond window Dura SamplIR II and the data are reported in reciprocal centimeters (cm -1 ). Optical rotations were measured on a Anton Paar MCP 300 polarimeter (Anton-Paar, Courtaboeuf, France) at 589 nm. [α] D is expressed in deg•mL•g -1 •dm -1 and c is expressed in g/100 mL. Melting points were recorded in open capillary tubes on a Buchi B-540 apparatus (Buchi, Rungis, France) and are uncorrected.
1 H-NMR (500 or 300 MHz) and13 C-NMR (125 or 75 MHz) spectra were recorded on Bruker Avance spectrometers (Bruker, Wissembourg, France) at 298 K unless otherwise stated. Chemical shifts are given in ppm (δ) and are referenced to the internal solvent signal or to TMS used as an internal standard. Coupling constants J are given in Hz. Carbon multiplicities were determined by DEPT135 experiment. Diagnostic correlations were obtained by two-dimensional COSY, HSQC and NOESY experiments. Infrared spectra (IR) were recorded on a Perkin-Elmer FT-IR system (Waltham, MA, USA) using diamond window Dura SamplIR II and the data are reported in reciprocal centimeters (cm -1 ). Optical rotations were measured on a Anton Paar MCP 300 polarimeter (Anton-Paar, Courtaboeuf, France) at 589 nm. [ ] is expressed in deg•mL•g -1 •dm -1 and c is expressed in g/100 mL.
In-solution small-angle X-ray and neutron scattering (SAXS/SANS) have become popular methods to characterize the structure of membrane proteins, solubilized by either detergents or nanodiscs. SANS studies of protein-detergent complexes usually require deuterium-labeled proteins or detergents, which in turn often lead to problems in their expression or purification. Here, we report an approach whose novelty is the combined analysis of SAXS and SANS data from an unlabeled membrane protein complex in solution in two complementary ways. First, an explicit atomic analysis, including both protein and detergent molecules, using the program WAXSiS, which has been adapted to predict SANS data. Second, the use of MONSA which allows one to discriminate between detergent head-and tail-groups in an ab initio approach.
Size exclusion chromatography in-line with multiangle light scattering (SEC-MALS) analysis showed that the AmtB-detergent complex comprises 285 ± 12 DDM molecules (Figure  S1  and Table  S1 ). Independently conducted analytical ultracentrifugation (AUC) experiments revealed a detergent shell of 321 ± 1 DDM molecules (Figure  S2  and Table  S1 ). Taken together, these independent findings indicate that the detergent corona around AmtB is likely to include between 260 and 320 DDM molecules. We next exploited atomistic molecular dynamics (MD) simulations of the AmtB-DDM complex and scored the models against SAXS data to resolve the experimental uncertainty regarding the size of the detergent corona. AmtB in the physiologically functional trimeric form (PDB ID: 1U7G)  8  was simulated surrounded by DDM coronas of 260, 280, 300, 320, 340, and 360 molecules.
It has previously been shown that single structures extracted from MD trajectories do not fully capture the characteristics of the solution ensemble. 9 We therefore calculated the predicted SAXS curves from conformational ensembles comprising 9000 individual configurations as observed in 70-160 ns simulations of each differently sized complex. The SAXS curves were obtained using explicit-solvent calculations as implemented in the WAXSiS method, thereby taking into account accurate atomic models for both the hydration layer and the excluded solvent, and consequently avoiding any solvent-related fitting parameters (Figure  2 ). 11, 12 AS experiments are very demanding in terms of requirements of sample quality,  13, 14  therefore, before recording SAS data, we ascertained that our samples were monodisperse and that AmtB was pure, stable, and critically active in detergent (Supporting Information, Figure  S1-S3 ).
We subsequently collected experimental SAXS data following size-exclusion chromatography of the AmtB-DDM complex. The radius of gyration (R g ) was found to be constant across the elution peak (Figure  S1 ), indicating the monodispersity of the complex and good data quality. Importantly, the scattering curves predicted for the models containing 260, 280, 300, 340, and 360 DDM molecules deviate slightly from the experimental data (Figures  2  and S4 ). By co trast, the curve computed for the MD model containing 320 DDM molecules was nearly indistinguishable from the experimental SAXS data (Figure  2  and S4). Furthermore, the values for R g obtained by the Guinier approximation from the experimental data and from for the MD model containing 320 DDM molecules were in quantitative agreement (Table  S3  and Figure  S5 ).
This suggests that the overall dimension of the simulated protein-detergent complex containing 320 molecules of DDM is identical to that in solution. It is important to note that the overall information content of SAXS is relatively low, and thus agreement between experimental and back-calculated curves may be insufficient to serve as unambiguous evidence for a structural model. 15  Specifically, in the context of a protein-detergent complex, SAXS data reports on the overall shape of the complex, whereas they do not provide independent information on the individual contributions from the protein and the detergent corona. Therefore, we employed SANS together with contrast variation to more firmly validate our computational model. We collected SANS data at four contrast points (0%, 22%, 42% and 60% (v/v) D 2 O) to differentiate between the individual components of the protein-detergent complex.
The Journal of Physical Chemistry Letters Letter protein during the SANS experiment (Figure  S6 ). To ascertain the reproducibility and the quality of our measurements, two independent sets of SANS data were acquired, using two batches of AmtB purified independently. The two data sets were found to be identical within the limits of the observed experimental noise (Figure  S7 ). It has previously been shown that in the absence of D 2 O in the buffer, neutron scattering from DDM micelles originates primarily from the hydrophilic head groups. 16 We calculated (Supporting Information) the overall contrast match point of DDM to be at 22% D 2 O, while the contrast match point for typical proteins is around 42% D 2 O. 4, 17  Consequently, the scattering contribution is dominated by the protein and the DDM hydrophilic headgroup in a buffer containing 0% D 2 O, by the protein at 22% D 2 O and by the complete detergent corona at 42% D 2 O.
To compare the experimental neutron scattering data with the MD-generated models, SANS curves were calculated using WAXSiS for 9000 individual configurations observed during 70-160 ns MD trajectories of each of the complexes. To this end, we extended the WAXSiS method, originally developed for SAXS predictions, to also allow SANS predictions with explicit-solvent models at various D 2 O concentrations (Supporting Information). The experimental curves were fitted to the calculated curves following I fit = f•I exp + c, thereby accounting for scattering contributions from the incoherent background with the fitting parameter c. However, neither the hydration layer nor the excluded volume were adjusted. Congruent with the analysis of the SAXS data, all SANS data sets were best fitted by the curves calculated for the model incorporating 320 molecules of DDM (Figure  3  and S8 ).
Hence, the SANS and SAXS data consistently validate our MD model with 320 DDM molecules. Second, the excellent agreement we observe between the experimental and calculated SAXS curves shows that the overall organization of the complex is accurately reflected by the atomistic model. Finally, the good agreement between experimental and computed SANS curves indicates that the MD model describes accurately the hydrophobic and hydrophilic phase of the detergent ring as well as the position of AmtB inside the corona. Importantly, the crystal structure of AmtB was used to produce our MD trajectories, which precludes the possibility of applying this combined MD/SAXS/SANS approach to membrane proteins of unknown structure. We therefore applied, in the final step, an independent "MD-free" approach to obtain a full ab initio model that captures detailed structural information on the complex without using the crystal structure of AmtB.
The protein envelope is a good representation of the crystallographic structure of AmtB and is, furthermore, confined inside the detergent corona. Importantly, the joint use of both SAXS and multiple SANS data sets allowed us to distinguish the head-and tail-groups of the detergent corona and place them correctly with respect to the protein surface and solvent. Such detailed insight is usually not achieved with ab initio models unless additional contact restraints are applied:  20  the detergent ring fits the contours of the protein and the positions of the two detergent phases (head-and tail-groups) are particularly clear. The hydrophobic phase is strictly contained between AmtB and the hydrophilic ring, with only the tails of DDM being in contact with the hydrophobic surface of the transmembrane domain.
Hence, without using deuterated protein or detergent, and without information about the 3D structure of AmtB, the combination of SAXS and SANS data capture the essential structural details contained in membraneprotein detergent complexes in solution. In summary, there is considerable interest in developing SAS methodology further to allow routine investigation of membrane proteins. We have adapted WAXSiS to account for SANS data and therefore open up this software package for future projects including both types of scattering data. Using our methodology, based upon a combination of SAXS/SANS measurements and MD simulations, we have been able to propose an atomic model of a protein-detergent complex. Our integrative approach demonstrates that combining SAXS, SANS, and iterative simulations provides much more detailed structural information than each of the methods alone.
It is widely recognized that cryo-electron microscopy (cryo-EM) will revolutionize the structural analysis of membrane proteins in the near future. 21, 22 It is our belief that a hybrid approach, combining in solution SAS techniques, in silico modeling, and cryo-EM will allow for better tracking and description of conformational changes of membrane proteins in solution, induced by ligand or cofactor binding. In this context, it was important to account accurately for the bound detergent molecules, which is greatly improved by combining SAXS and SANS data at various contrasts. Second, our multiphase analysis, which merges SAXS and SANS data, without using deuterated protein or detergent, allowed us to obtain unprecedented structural information on the phase density of the detergent, in particular to distinguish head-and tail-groups in the assembled membrane protein-detergent complexes.
This is particularly relevant as deuterated media/detergents are often expensive and/or toxic for bacteria, leading to decreased protein yields. 23 rucially, the multiphase analysis does not require information on the 3D structure of the protein, which opens up the possibility of applying this methodology to a wide range of important membrane proteins that have so far remained inaccessible to high resolution structural analysis. While SAS has become a popular technique among structural biologists, combinations of SANS, SAXS and MD simulations have remained underexploited by the community. In this context, our work represents a significant advancement in data acquisition, model validation, development of new software, and multiphase volumetric analysis to firmly establish SAS technology as a standard method for membrane protein structural biology.