dataesr/scientific-paragraphs-categorization huggingface dataset:
In light of this, our data suggest that CorA could also be in an asymmetric, closed state at high Mg 2+ levels. available from the ATSAS package  39. In total, DAMMIF was run 100 times in slow mode with either P1 (A) or P5 (B) symmetry selected. For P1 symmetry, the normalized spatial discrepancy (NSD) values varied from 1. 2 to 1. 5, whereas for P5 symmetry, NSD values varied from 1. 4 to 1. 7. NSD values above 1 indicate that the calculated models differ systematically from one another. Changes in peak intensity are already noticeable in the 2D 1 H-15 N dipolar correlation spectra, but are amplified in the 3D correlations, where additional radio-frequency irradiation periods act as a more stringent filter, dumping the signals of the most mobile sites. 1D traces of two exemplar 3D 1 H-15 N-  13  C correlations (T65 in the ICD and F306 in the TMD) and the plot of signal intensities over the full protein sequence with and without Mg 2+ are shown in Figure  5A  and Figure  5B , respectively.
The contribution of the carbon-13 (natural abundance) to the value of the area under each [X+1] MS signal is subtracted based on the relative amount found in the protonated version. In a typical analysis we measure the C-13 natural abundance contribution by running ER-MS of the protonated version (or estimate it by ChemDraw software) and use this value in our calculation using an in-house developed method that subtracts this contribution from each MS signal constituting the isotope distribution. 1 H NMR (400 MHz),  13  C NMR (100 MHz),  31  P NMR (162 MHz) and 2 H NMR (61. 4 MHz) spectra were recorded on a Bruker 400 MHz spectrometer at 298 K. Chemical shifts, in ppm, were referenced to the residual signal of the corresponding solvent. Deuterium NMR spectroscopy was performed using the probe's lock channel for direct observation.
0044 Å -1 to 0. 42 Å -1 with the neutron wavelength of 6 Å. The scattering intensity, I(q), was brought to absolute scale in units of cm -1 by normalizing to the direct beam intensity measured with an attenuator in place. Pair distance, p(r), distributions were calculated by Bayesian indirect Fourier transformation at the bayesapp server  32  (available from the genapp server at https://somo. chem. utk. edu/bayesapp/). p(r)s and the corresponding I(q)s for PDB structures were calculated using CaPP (available at github. com/Niels-Bohr-Institute-XNS-StructBiophys/CaPP) with a water layer excess density of 6 % applied to the parts of the protein outside the membrane.
Normal mode analysis Non-linear normal mode analysis was performed using the NOn-Linear rigid Block NMA  50  (NOLB) routine (available from https://hal. inria. fr/hal-01505843v2). The NOLB routine was integrated with PEPSI-SANS 2. 2 (https://team. inria. fr/nano-d/software/pepsi-sans/) and a regularization algorithm to avoid unphysical structures. In the latter, T = c 2 + aS is minimized, where S constraints the secondary structure deviation. Scanning over different values of a, a plot of c 2 vs. S is obtained. The best compromise between conservation of structure and the best fit to the data is chosen from the "elbow"-region (Figure  4 -figure supplement 2).
The equilibration was performed according to the CHARMM-GUI equilibration protocol using a minimization step followed by 6 equilibration steps with slow decrease in the positional restraint forces on both lipids and protein in each step  53. The GROMACS-5. 1. 4 software package  54  was used to simulate with a 20 fs time step. Temperature and semi-isotropic pressure were controlled at 303. 15 K and 1 bar using the stochastic velocity rescaling thermostat  55  and Parrinello-Rahman barostat  56. Electrostatic interactions were modulated using the reaction field approach. The cutoff of short-range distance for the electrostatic interactions was 1. 1 nm. The potential shift Verlet scheme was used to cut off the Lennard-Jones potential at long ranges. Well-Tempered Metadynamics 57 simulations were performed with the PLUMED2.
The wall was defined as a harmonic restraint with a force constant = 10000, harmonic exponential power = 4, off-set = 0, and a rescaling factor of 1. Multiple wall types and sizes was attempted with lower walls causing too little dynamics for fitting with the experimental SANS data and higher walls causing individual monomers to bend unphysically and giving unfeasible large sample space. Clustering of the simulation trajectory was performed using the KMeans clustering method  59. The software program BME was used reweight the MetaD trajectory to fit the experimental SANS data  60. The hyperparameter q was determined based on a L-curve analysis (analogous to the procedure in normal mode analysis, see Figure  4 -figure supplement 2) of Srel vs c 2 , where q is chosen where a natural kink is observed and any further decrease in c 2 gives rise to an increasing larger penalty in Srel.
Acknowledgements We thank  Elliot Gilbert  for his assistance with SANS experiments at QUOKKA at  ANSTO  and  Marta Brennich  for her assistance with SAXS experiments at BM29 at the  ESRF. Thomas Boesen  is acknowledged for his help with EM experiments conducted at  Aarhus University  and  Michael Gajhede  for his assistance in EM data processing. We thank  Ida Louise Jørgensen  for helping with functional reconstitution of CorA in large unilamellar vesicles and  Michael Maguire  for providing a plasmid encoding CorA.
Data availability The SANS data have been deposited to the Small Angle Scattering Biological Data Bank (https://www. sasbdb. org/, access codes SASDM42, SASDM52, SASDM62, SASDM72). The NMR resonance assignments have been deposited in the Biological Magnetic Resonance Data Bank (https://bmrb. io, access code 50959). The EM maps have been deposited to the Electron Microscopy Data Bank (https://www. ebi. ac. uk/emdb, access codes EMD-13326 and EMD-13327). Fluorescence data on CorA activity are available at GitHub https://github. com/Niels-Bohr-Institute-XNS-StructBiophys/CorAData/. The metadynamics.
Parameters of the p-PBPK Model Overall, our model consists of two sets of parameters. The first set of parameters describe the physiological properties in each compartment including the volume, blood flow rate; while the second set of parameters define drug-specific parameters including tissue-to-blood partition coefficients, metabolic and clearance rates. The second set, i. e. nicotine/cotinine related parameters including their respective data sources are provided in Table  1. The first set i. e. , physiological parameters are provide in the Supplementary Material. There are total 32 differential equations for the p-PBPK model shown in Figure  1. The equations are not listed here but provided in the Supplementary Material for interested reader's reference. The program was implemented in Matlab, with ODE45 as the differential equation solver.
ECONOMIC STUDY To assess the measurement costs of the ten metals/metalloids studied by grab sampling or DGT passive sampling, the following financial simulations were performed: -A three-station measurement network requiring 50 km of travel during 1 working day; -A ten-station measurement network (large scale) requiring 800 km of travel during 2 working days. It was considered for each network one grab sampling or one 2-weeks DGT sampling (2 DGT-Ch and 2 DGT-Zr per site) every two months (6 sampling campaigns). The costs were grouped into 4 categories: -Materials: devices, sampling equipment; -Field operations: installation/removal of equipment, travel, catering, and overnight stays; -Analysis: treatment of DGT samples, analysis, ICP-MS depreciation; -Data processing: quality control, CDGT calculation, result analysis.
Depending on the tasks, the staff qualification was adapted for either a higher-level technician (€ 24/h) or engineer (€ 37/h). The duration of each task was estimated for the staff cost estimation. The supervision of an engineer is involved for 5% of the total time (data processing). Field operations require two agents for safety. The 2016 French administration packages are used for the travel prices (0. 47€/km; 15. 25€/meal; and 70€/hotel night). The DGT cost is 15€ when buying from DGT Research Ltd (Lancaster, UK). All the reagents and disposable material costs were taken from the UGAP supplier (the French organization for public purchase) catalogue prices. Reusable materials such as temperature loggers are considered according to a fixed cost per campaign (1€). More details of the costs can be found in Table  S5.
FIELD LOSS RATE Among the 408 DGTs exposed to the field, only 3 DGTs were damaged (with damaged protective membranes), and 10 were found out of the water after the 14 days of river exposure, primarily during the first field campaign, due to a large decrease in the water level linked to the end of a flood event. After their analysis, the 3 damaged DGTs were consistent with their undamaged duplicate and were consequently considered workable. On this basis, a 3% loss rate can be established (10 out of 408 DGTs). It is noticeable that, for 6 out of the 10 DGTs recovered out of water, an exposure concentration (CDGT) can still be calculated. Thanks to the temperature data recorded every 10 minutes, their exposure time is obtained (typically one week versus two for the other DGTs), enabling calculation of CDGT.
Acknowledgements The authors appreciate the effort of the management of  Tizi-Ouzou University of Algeria , for providing the Laboratory of applied chemistry and chemical engineering with the necessary equipment to carry out this research work. In addition, we would like to thank  Pr GUÉNIN Erwann  for his contribution to quantitative analytical performances and the techniques for analyzing quantitative and qualitative data (Laboratory of Integrated Transformations of renewable matter, University of technology of Compiègne 60200 France).
The device allows the measure of the absorbance and the reectance by the external mode using an integrating sphere and a xenon lamp. The barium sulfate powder (BaSO 4 ) is used to record the reference signal. Thermal stability in air of the as-prepared powder was studied by temperature-controlled X-ray powder diffraction. Prior to data collection, a dehydration of the sample was carried out at 373 K for 110 min in the reactor chamber. Then, temperature-controlled XRPD patterns were recorded on the same diffractometer between 298 and 383 K (heating rate of 10 K min À1 , temperature stabilisation for 20 min, cooling rate of 60 K min À1 , air ow of 40 mL min À1 ) by using an XRK 900 Anton Paar reactor chamber. The sample was deposited on the sieve (pore size F 0. 2 mm) of the open sample holder cup, both made of glass ceramic Macor®, thus allowing air to ow through the sample.
The Raman spectra were collected between 70 and 3100 cm À1 over the temperature range 294-443 K using a T-64000 (Jobin-Yvon, Horiba group, Kyoto, Japan) spectrometer. Prior to data collection, the sample was heated up to 373 K for 10 min then cooled to room temperature. We used a BX41 Olympus microscope equipped with a MSPlan 50x (N. A. 0. 55) objective to focus the incident wavelength radiation at 647. 1 nm, provided by an argon-krypton ion laser (Innova, Coherent, France). During the experiment, the laser power was less than 2 mW on the sample and the Raman spectra were systematically recorded twice with an integration time of 60 s. The scattered light was then analyzed by a spectrometer with a single monochromator (600 gratings per mm), coupled to a nitrogen cooled front-illuminated charge-coupled device (CCD) detector.
, 2013 (Bensby et al. , 2017 (Bensby et al. , 2020)). On these occasions, from 2 to 8 ∼1800 s UVES  (Dekker et al. 2000)  exposures were recorded with a 0. 7 wide slit with wavelength coverage in the intervals 376-498 nm, 568-750nm and 766-946 nm. During the same observational night, one rapidly rotating B star for each target was observed to evaluate the effect due to the Earth atmosphere. We retrieved the UVES reduced (UVES pipeline version 5. 10. 13 1 ) data from the ESO data archive 2. The reduced spectra are characterized by R = 42310 and S/N ∼ 19 -183 at ∼ 921 nm. The coordinates and the S/N of the data are listed in Table  1. In order to minimize systematic errors, a homogeneous comparison between bulge and disk stars with atmospheric parameters estimated in a consistent way is required.
Stellar sample analysis The sample of stars considered in this work was already studied by  Bensby et al. (2009 Bensby et al. ( , 2010a Bensby et al. ( ,b, 2011 Bensby et al. ( , 2013 Bensby et al. ( , 2014 Bensby et al. ( , 2017 Bensby et al. ( , 2020)). They estimated atmospheric parameters, chemical abundances of 13 species  (Fe, O, Na, Mg, Al, Si, Ca, Ti, Cr, Ni, Zn, Y, Ba) , ages and radial velocities of the targets. Using the atmospheric parameters measured by Bensby and collaborators, we created log(g) vs log(T ef f ) diagrams (Figure  1 ). We divided the stellar sample in sub-samples with similar metallicities, dividing the metallicity range -2 <[Fe/H]< 1 in bins of 0. 5 dex. In Figure  1  the bulge, thick and thin disk stars are shown in black, cyan and magenta, respectively. In each panel are reported different isochrones from the Dartmouth Stellar Evolution Database  (Dotter et al.
Sulfur abundances The spectral range covered by the data allowed us to measure A(S) from lines of Mult. 1, 6 and 8 (Table  4 ). In order to identify the lines, we computed synthetic spectra with the code SYNTHE  (Kurucz 1993b (Kurucz ,2005)) , using ATLAS9 α-enhanced model atmospheres  (Kurucz 1993a)  based on ODF by  Castelli & Kurucz (2003)  with the parameters in Table  1. To assess the contamination due to telluric lines, we compared the observed spectra of bulge targets with those of B stars observed on the same night. Similarly, we used UVES POP B stars spectra observed on the closer observation night of the disk stars. Thanks to the simple superimposition of these spectra, we evaluated the line suitability for the estimation of A(S). We rejected lines of Mult. 1 contaminated by telluric ones and Mult.
2013). EWs were measured with the IRAF 4  task splot and the contribution from the telluric and the S line were taken into account using the splot deblend option. To evaluate the A(S) uncertainty due to the atmospheric parameters errors, we assumed the mean errors on temperature, gravity, microturbulence velocity and metallicity estimated by  Bensby et al. (2017)  with the method outlined in  Epstein et al. (2010). A σ T ∼94 K, σ log(g) ∼0. 15 and Fig. 1. log(g) vs log(T ef f ) diagrams of the stellar sample considered in this work. The bulge, thick and thin disk stars are shown in black, cyan and magenta respectively. The black filled circle and open circles are bulge dwarfs and giants. In each panels are reported the metallicities and the ages adopted to obtain isochrones from the Dartmouth Stellar Evolution Database  (Dotter et al.
Acknowledgements. The authors are grateful to the anonymus referee for providing helpful comments and suggestions, which have improved the content of this paper. Support for the author FL is provided by  CONICYT-PFCHA /Doctorado Nacional año 2020-folio 21200677. EC and PB acknowledge support from the  French National Research Agency (ANR)  funded project " Pristine"  ( ANR-18-CE31-0017 ).
While benchmarking the ProCare method by comparing protein structures disclosed for the first time to 31,570 subpockets from the sc-PDB archive  [8]  (Figure  1 ), we noticed unexpected high similarities between the core pocket at the interface of an inhibitor-bound asymmetric human TNF-α trimer (PDB ID 6OOY)  [22] , and several non-nucleoside binding sites of inhibitor-bound HIV-1 RT (Supporting Table  S1 ). Computing the area under the receiveroperating characteristic (ROCAUC) curves (Supporting Figure  S1 ) from pairwise ProCare similarity score distributions, the selected non-nucleoside HIV-1 RT subpockets are statistically similar to the inner core of the TNF-α trimer with ROCAUC values higher than 0. 80, a remote similarity not being observed by five other randomly selected targets (Table  1 ).
Fig. 1  Virtual screening of sc-PDB subpockets for similarity to the core cavity TNF-α. The inner pocket of TNF-α (PDB ID 6OOY) is converted as a cloud of points with pharmacophoric properties (orange: hydrophobic, green: aromatic, blue: H-bond donor and positive ionisable, red: h-bond acceptor and negative ionisable, white: dummy) and compared to the corresponding point clouds originating from fragment-bound subpockets of sc-PDB ligands. To further investigate that hypothesis, exclude a potential bias in the ProCare alignment/scoring method and give a stronger statistical support to our prediction, we systematically compared all binding sites from available asymmetric human TNF-α X-ray structures  [22]  to that of 122 HIV-1 RT structures bound to nonnucleoside inhibitors. Exhaustive comparison of TNF-α trimer and HIV-1 reverse transcriptase binding sites.
A ProCare similarity matrix was built by comparing cavities of three asymmetric TNF-α structures (PDB identifiers 6OOY, 6OOZ and 6OP0) co-crystallized with benzimidazole inhibitors to 122 cavities (Supporting Table  S2 ) of non-nucleoside HIV-1 RT inhibitors (Figure  2 ). We observed that 76% of all pairwise comparisons were scored higher than the previously statistically determined ProCare similarity score threshold of 0. 47 in a previous study  [17]  (Figure  2A ). To exclude the possibility that the predicted similarity is caused by peculiar mutations of the HIV-1 RT non-nucleoside biding site, we also compared pairwise similarities for both wild type and mutated HIV-1 RT pockets, but did not observe significant differences in the percentage of HIV-1 RT pockets predicted similar to that of TNF-α (74% and 82% of similar pockets for wild type and mutants, respectively).
In absence of X-ray structures of TNF-α bound to efavirenz and delavirdine, we cannot rule out the possibility that both inhibitors bind to a different pocket than that highlighted in the current computational study. This hypothesis is however very unlikely for two reasons: (i) no other cavity than that occurring at the inner core of the multimeric TNF-α could be detected among the currently existing 33 structures available in the Protein Data Bank; (ii) all non-covalent small molecular weight inhibitors co-crystallized with TNF-α dimeric or trimeric forms  [32] [33] [34] [35]  are exactly bound at the central pocket examined in this study. We should recall here that none of the HIV-1 RT inhibitors has been optimized for binding to TNF-α and directly repurposable for treating TNF-α -dependent autoimmune disorders.
The binding site perception, comparison algorithm and scoring function is specific to each method. Some methods (FuzCav, SiteAlign) consider entire cavities while some others utilize either fragment-bound subpockets (KRIPO, Shaper) or local protein descriptors (G-LoSA). To make the comparison consistent, the same set of atomic coordinates were compared, a binding site being defined by the protein PDB identifier, the ligand HET code, chain identifiers and list of amino acids lining the cavity. The only exception was for the KRIPO method, which used all the chains available in the PDB entry, but still corresponding to the same tuple (PDB, HET) as for the other methods. For each method, the distribution (Figure  5 ) and percentage of pairwise comparisons scored above the developer's recommended similarity threshold (Table  3 ) were reported.
Strikingly, only the G-LoSA method relying on a graph-based local alignment of cavity-lining amino acids, managed to find some similarity between the two sets of binding sites, however with reduced success rate (35. 2%) when compared to the ProCare algorithm (76. 6 % success rate; Table  3 ). We acknowledge that the developer's recommended thresholds may be biased toward peculiar datasets. However, all methods compared herein were subjected to the same protocol and we do think that the threshold scores are appropriate indicators in a virtual screening setting where there is no room for a one-by-one case study of each pairwise comparison.
METHODS TNF-α structures. The recently described asymmetric structures of the human TNF-α trimer bound to different inhibitors were retrieved from the RCSB Protein Data Bank (PDB) homepage (https://www. rcsb. org) [44]  using the following identifiers: 6OOY, 6OP0, 6OOZ  [22]. The PDB structures were protonated with Protoss  [45]  v. 4. 0, then split into protein, ligands and water molecules and finally converted into mol2 format with Sybyl-X v. 2. 1. 1 (Certara USA, Inc. , Princeton, NJ 08540). The binding sites ('SITE') were defined as any protein residue with at least one heavy atom closer than 6. 5 Å from any ligand heavy atom and saved in mol2 and pdb formats. The ligands were converted into sdf format with OpenEye Python toolkits v. 2020. 0. 4 (OpenEye Scientific Software, Santa Fe, U.
The same ROC AUC calculation was repeated, each time considering the following human targets as positives, any other entry being the negatives: beta-2 adrenergic receptor, carbonic anhydrase 2, cyclin-dependent kinase 2, thrombin and heat shock protein 90-alpha. Docking. TNF-α x-ray structure 6OOZ was prepared as described above (see TNF-α structures). 6OOZ cocrystallized ligand on the one hand, delavirdine, efavirenz and nevirapine as well as their main fragments on the other hand were drawn with MarvinSketch v. 16. 10. 17 (ChemAxon Ltd, 1031 Budapest, Hungary) and saved into 2D sdf format. They were ionized with Filter v. 2. 5. 1. 4 (OpenEye Scientific Software, Santa Fe, U. S. A. ) using customized parameters (Supporting Table  S6 ). Then Corina v. 3. 40 (Molecular Networks GmbH, 90411 Nürnberg, Germany) was used to generate a starting 3D conformation for each inhibitor.
The prepared molecules were docked into the target 6OOZ with PLANTS v. 1. 2 [26]  using the following configuration: the grid was set at 13 Å from the binding site center; poses were searched 'speed1' settings to generate a maximum of 10 poses per ligand using a clustering rmsd of 2 Å. Solutions were scored with the default ChemPLP scoring function  [26]. The docking protocol was validated by computing the rmsd between of the docked 6OOZ ligand coordinates and the x-ray coordinates. Results were processed and rescored by computing the interaction fingerprint (IFP) similarity (Tanimoto metric)  [48]  between X-ray and docking poses. The IFPs were computed with IChem v. 5. 2. 9 IFP module. All poses were visually inspected using Maestro v. 2019-3 (Schrödinger, New York, NY 10036-4041). HIV-1 reverse transcriptase PDB structures.
Starting from the PDB structure 1VRT as a reference, a search was performed in the RCSB PDB (https://www. rcsb. org) [44]  to retrieve all structures with strict matching ("Structure Similarity" query in the PDB). After visual check, 122 entries already available in the sc-PDB repository (http://bioinfo-pharma. u-strasbg. fr/scPDB) [8]  and for which the ligand is a non-nucleoside inhibitor were kept. The remaining PDB structures were protonated with Protoss  [45]  v4. 0. The list of the of aligned points of the same pharmacophoric features. In agreement with our previous study  [17]  where the similarity threshold of 0. 47 (p-value of 0. 05) was statistically determined, pockets scoring above 0. 47 were considered similar. FuzCav. FuzCav  [36] , an alignment-free method, was used to compare the binding site 'SITE' (mol2 format) entries of TNF-α dataset to the binding sites of HIV-1 RT sc-PDB dataset.