dataesr/scientific-paragraphs-categorization huggingface dataset:
We present CHAMPION (Chalmers hierarchical atomic, molecular, polymeric, and ionic analysis toolkit): a software developed to automatically detect time-dependent bonds between atoms based on their dynamics, classify the local graph topology around them, and analyze the physicochemical properties of these topologies by statistical physics. In stark contrast to methodologies where bonds are detected based on static conditions such as cut-off distances, CHAMPION considers pairs of atoms to be bound only if they move together and act as a bound pair over time. Furthermore, the time-dependent global bond graph is possible to split into dynamically shifting connected components or subgraphs around a certain chemical motif and thereby allow the physicochemical properties of each such topology to be analyzed by statistical physics.
| INTRODUCTION Many scientifically and technologically important materials and liquids today are complex in terms of their intermolecular structure and dynamics. Examples include electrolytes in electrochemical devices, liquid solutions, dispersions and emulsions, drug delivery systems, and most polymeric systems including gels, plastics, elastomers and fibers, and even liquid water when the dynamic network of hydrogen bonds is taken into account. One particular example, which also motivated the development of the software reported here (CHAMPION: Chalmers hierarchical atomic, molecular, polymeric and ionic analysis toolkit) and which will be used as example throughout, is electrolytes for lithium-ion batteries (LIB) as well as electrolytes for various next generation batteries. 1  enable the reader to fully understand the need for CHAMPION, and what it must be able to do-we here describe these systems to Abbreviations: ACN, acetonitrile; AIMD, ab initio MD; CHAMPION, Chalmers hierarchical atomic, molecular, polymeric, and ionic analysis toolkit; CoM, center-of-mass; DMC, dimethyl carbonate; DoF, degree of freedom; EC, ethylene carbonate; ESW, electrochemical stability window; G4, tetraglyme; GPE, gel polymer electrolyte; HCE, highly concentrated electrolyte; IID, independent, identically distributed; IL, ionic liquid; LHCE, localized HCE; LIB, lithium-ion battery; LiTDI, lithium 2-trifluoromethyl-4,5 dicyanoimidazole; LiTFSI, lithium bis(trifluoromethanesulfonyl) imide; MD, molecular dynamics; MSD, mean squared displacement; pRDF, partial RDF; RDF, radial distribution function; SIL, solvate ionic liquid; SPE, solid polymer electrolyte; vdW, van der Waals.
To resolve this CHAMPION uses a unique dynamic bond detection method based on requiring bound atoms to oscillate about a welldefined equilibrium distance of one another for an extended period of time (Figure  2 ). The bond is active for as long as this oscillatory motion F I G U R E 1 Common constituents of LIB electrolytes. LIB, lithium-ion battery persists. The dynamic nature of this method ensures that bonds are assigned only based on pairs of atoms which actually move together. While many software suites exist for post-processing of MD trajectories  35, 36, 37, 38  and are included in many MD simulation software packages,  39, 40, 41  as well as some visualization software,  42  none of these, to the best of our knowledge, implement bond detection based on dynamics. From this unique dynamic detection of local structures several properties can be extracted (for a vast range of systems) by statistical physics, such as bond graph topologies and their populations, lifetime distributions for topologies and bonds, transition rates, and, of special interest to LIB electrolytes, contributions to diffusive and structural ion transport and the effective diffusivity.
| METHODS, THEORY, AND RESULTS As outlined above CHAMPION is mainly geared toward the analysis of atomic trajectories, but it also includes a system builder for disordered condensed matter systems, to support the set-up of simulations. The simulations themselves; however, are currently outside the scope of CHAMPION, which instead is able to import trajectories in the. xyz format from any external simulation software. Subsequently, the trajectories are post-processed in three consecutive steps: to assess the structural equilibration time, to detect time-dependent bonds, and to do the topological classification of structures. After this post-processing, a number of different and mostly independent statistical physics-based analyses can be performed-herein we provide seven examples. Each provides best estimates and statistical uncertainties of the physicochemical properties targeted, including statistical inefficiency, but always assuming ergodicity-the reasonableness of this assumption has to be considered by the user.
| Post-processing step 2-time-dependent bond detection CHAMPION's main feature (pat. Pend. 43 ) is the ability to characterize the structure of any system topologically as a time-dependent set of bonds between atoms, which may form and break during the course of the trajectory (code listing S3). The algorithm works as follows: Two atoms of species i and j, which are closer than the sum of a fraction (typically 0. 8 in our analyses) of their vdW radii (Figure  3 (A)) over a particular period of time (t, t þ τÞ are considered bound if their mean distance during this time is within a tolerance of the first peak in the pRDF (Equation (  1 )) for species i and j (Figure  3(B) ) 1 À α ð Þr peak ≤ 1 τ ð tþτ t d ij t ð Þdt ≤ 1 þ α ð Þr peak , ð4Þ for tolerance α (typically a few percents of the half-width at half maximum [HWHM]).
S C H E M E 1 Full workflow for performing a CHAMPION analysis. All but the gray step are done using CHAMPION. Numbered steps are user initiated, bullet points are executed automatically by the software. CHAMPION, Chalmers hierarchical atomic, molecular, polymeric, and ionic analysis toolkit interval is subsequently expanded in both directions to the entry into/ exit from the furthest turning distance between the starting guesses. This scheme for birth and death is designed to avoid spurious exits and re-entries to be registered by the algorithm. A prerequisite for this algorithm to work is that the typical lifetime of a bond must stretch over many timesteps in order for the average distance to approach the pRDF peak. Indeed, the greater the average number of sampled timesteps for a bond, the tighter the bond length tolerance can be set.
| CONCLUDING REMARKS We have here presented the design and methodology of our novel software CHAMPION, which allows analyses of atomic trajectories to be based on dynamic structure discovery. This unique approach gives a time-resolved view of the studied system at the intermolecular level where many technologically relevant properties arise. The combination of automated dynamic structure discovery to find both reversible and irreversible bonds, graph theory to classify all topologically distinct connected components and local structures, and statistical physics to characterize the properties of the discovered structures, has the potential to elucidate much of the complexity in dynamic and disordered materials and liquids. This is necessary in order to understand many dynamical processes such as transport, self-organization, phase transitions, and so on.
ACKNOWLEDGMENTS The research presented has received funding through the  HELIS  project (European Union's  Horizon 2020 research and innovation program  under Grant Agreement No. 666221 ) and the  Energimyndigheten  grants (# P43525-1  and # P39909-1 ). Patrik Johansson  would also like to acknowledge several of  Chalmers Areas of Advance: Materials Science, Energy , and Transport, for continuous support, and specifically the Theory and  Modeling scheme of Advanced User Support  to  Rasmus Andersson. Alejandro A. Franco  acknowledges the  Institut Universitaire de France  for the support.
Spéciation and lability To be sure of the stability of inorganic I species in deployment solution, speciation was checked with IC-ICP-MS measurements during DGT experiment. The concentration of inorganic I species was relatively stable over time in each deployment solution with RSD < 5 %, confirming that there is no redox interconversion in these DGT experiments. Concerning the lability of inorganic I species, speciation calculations were performed with PHREEQC version 3 (https://www. usgs. gov/software/phreeqc) with the thermodynamic database ThermoChimie version 9b0 developed by Andra (http://www. thermochimie-tdb. com/) [49, 50]. Input data were the concentrations of each component in deployment solution. Atmosphere-water equilibrium with CO2 was also assumed. pH and temperature was also introduced.
Time-series experiment in natural and synthetic waters After carrying out numerous experiments in 10 mmol L-1 NaCl solutions, the performances of AgdCl-DGT method were studied in Vittel® and Volvic® water spiked with T with a concentration close to 20 pg L-1. The concentration of IO3-was negligible in these deployment solutions with values lower than 1 pg L-1. The ionic strength for these two drinking waters are 22. 6 and 3. 2 mmol L-1, respectively (calculations performed by using data in Table  1  and PHREEQC software). The mass of T accumulated by AgdCl-DGT samplers was monitored over time, and compared to Mth3 (black line in Figure  5 ). Linear accumulation of total I mass by AgdCl-DGT samplers was observed up to 24 h for Vittel® and Volvic® waters (R > 0. 9; Figure  5  (A) and (B)). However, the amount of I-measured by AgdCl-DGT samplers was 33 ± 8 and 27 ± 5 % in Vittel® and Volvic® waters, respectively.
Acknowledgements This work has been supported by the  ANR (French National Research Agency) , under the " Investissement d'Avenir" framework program  [Number  ANR-11-RSNR-0002 ]. Véronique Hénin  ( Metrohm France ) is gratefully thanked for the loan of the Metrosep Asupp 10 guard and analytical columns, but also  Gilles Alcalde  for the granulometric measurements of AgdCl-resin beads. All experiments were performed at LUTECE (the SEDRE's experimental platform), and elemental and speciation measurements at PATERSON (the  IRSN 's mass spectrometry platform). This is PATERSON contribution n°9. Support from the  Spanish Ministry of Science and Innovation  is gratefully acknowledged (Project  PID2019-107033GB-C21 ).
Uranium concentrations were measured in olfactory bulbs (A), olfactory tubercles (B) and brain residue (C). Data are given as mean AE SD of 8 animals. Significant difference from the respective timepoint t = 10 min group is indicated as 1 p < 0. 05, 11 p < 0. 01 and 111 p < 0. 001; significant difference from the respective timepoint t = 250 min group is indicated as D p < 0. 05; significant difference in between micro and nanoparticles group is indicated as $ p < 0. 05 and $$$ p < 0. 001 using two-way ANOVA (treatment x time) with the LSD post hoc test. reported that only the High [UO 4 ] group shows an increase in the U concentration in the nasal turbinates compared to the Low [UO 4 ] group, thus showing a dose effect of the aerosol concentration. Conversely, the absence of a difference with the Low [UO 2 ] and Repeated low [UO 4 ] groups tends to show that the inhaled quantity per exposure are comparable, which is in line with the concentrations measured in the generated aerosols.
Acknowledgements We thank  Bruno Vitorge  from the  Institut Pasteur Biological NMR Technological Platform  for helping with NMR experiments,  Frédéric Bonhomme  of the  CNRS-Institut Pasteur UMR3523 Organic Chemistry Unit  for assisting with HRMS analysis. Sophie Vichier-Guerre of the  CNRS-Institut Pasteur UMR3523 Organic Chemistry Unit  for assisting with LC/MS analysis. The authors are thankful to  Alain Guy  and  Clotilde Ferroud ,  CNAM, Paris , for their teaching on the use of the nosyl as protecting group. Région Ile de France  supported the research with a Ph. D. fellowship to  CB (ARDoC) , la Ligue contre le cancer,  Comité de Paris , to PBA ( RS20/ 75-82  and  RS21/75-31 ).
The MALS detector was a DAWN Heleos-II (Wyatt Technology Inc. , CA, USA) fitted with a 50 μL K5 cell and 18 photodiodes (normalized relative to the 90 • detector using bovine serum albumin). The viscometer detector (VD) was a Vis-coStar II (Wyatt Technology Inc. , CA, USA). The collected data were analysed using the Astra® 6. 1. 7. 16 software package. Molar masses were obtained with the Zimm order 1 method. The concentration of each eluted fraction was determined with DRI (RID 10A Shimadzu, Japan) according to the values of dn/dC (0. 150 mL g -1 for HA and 0. 158 mL g -1 for PMeOx), which were determined through DRI measurements using polymer solutions in 0. 1 M LiNO 3 at different concentrations. The dn/dC of the HA-g-PMeOx samples were then calculated as the average of the dn/dC of HA and PMeOx according to the weight content of the respective polymers in the copolymer.
Rheology Rheological measurements were performed on a Discovery HR-2 hybrid rheometer from TA Instruments (UK). HA 990k -g-(PiPrOx 20 -co-PnBuOx 7 )-0. 10 was dissolved in PBS (0. 01 M phosphate buffer, 0. 15 M NaCl, pH: 7. 4) at a concentration of 15 g L -1. The measurements were performed with a double concentric cylinder measuring system and the rheometer was operated using the TRIOS v4 software.
Conclusion PMeOx was successfully grafted on HA through a direct termination route between the oxazolinium terminal group of PMeOx chain and carboxylate moieties of HA without using any activating agent. The successful grafting as well as the purity of the resulting HA-g-PMeOx have been confirmed with SEC/MALS/VD/DRI and DOSY NMR  MHS coefficient (a) has been determined by integrating the most linear part of bilogarithmic representation of intrinsic viscosity vs molar mass. SEC/MALS/VD/DRI data accuracy: M n ± 9%; M w ± 6%; Đ ± 10%; [η] w ± 0. 5%; Rh w ± 2%; Rg w ± 6%. a Out of the range of the MALS detector (isotropic diffusion for lower masses fractions).
N O 2 N N N N Br X N O 2 N N N N Br Y O O N R C Fig. 1A : Binding mode of the first generation compound X in BCL-xL, as suggested by previous molecular simulations. 6 Several important residues are labelled, and PUMA binding crevice is illustrated as white dotted line. Fig. 1B  NMR-based structure of BCL-xL/PUMA complex, in the same orientation B (PDB:2M04). 8  PUMA is shown as a yellow a-helix. Fig. 1C  Proposed transition from the first generation compound X to the second series Y. The purpose of this paper is first to report the synthesis of a focused library (with 12 members) of such type-Y molecules. Next, these compounds will be evaluated on cytotoxic studies, using a representative panel of seven cancer cell lines. Finally, for the best compound, the precise involvement of BCL-xL/PUMA interaction will be checked by BRET and molecular modelling experiments.
Acknowledgements We thank  Chemveda Life Sciences for providing laboratory facility  for carrying these research experiments. This research has been performed also as part of the  Indo-French "Joint Laboratory for Natural Products and Synthesis towards Affordable Health". We thank  CSIR ,  CNRS  and  University of Rennes 1  for their support. We thank  CRMPO  ( University of Rennes 1 ) for the mass spectra analysis. Financial support by the  "Ligue contre le Cancer ,  Conseil Interrégional Grand Ouest " is gratefully acknowledged. We thank  Osmania University  for the financial assistance under  UGC-UPE FAR, SAP, OU-DST PURSE-II Programme , sanction letter no. C-DST-PURSE-II/15/2019.
However, there is insufficient data on its performance under real driving conditions that induce varying WBV and different lateral and longitudinal solicitations. Moreover, the benefit from seat suspension systems in cars for personal use remains to be explored. This study analyzed the evolution of the driver's sitting behavior by measuring pressure distribution and perceived discomfort under real prolonged driving conditions. Using real conditions enabled us to consider the effect of random road roughness due to surface asperity, which may induce vibrations varying in magnitude in z-axis. It also allowed us to take into account the effect of varying road types involving different levels of acceleration. Under these conditions, we investigated how (1) different seats (static factor) and (2) real driving (dynamic factor) affected pressure parameters (RM, pressure distribution, contact surface, and contact pressure) and subjective general and local discomfort.
Measurements Pressure and repositioning movements. Pressure parameters and RM were analysed via the pressure system XSENSOR 1 Technology Corporation (Xsensor, Inc. , Calgary, Alberta, Canada). The system consisted of two sensor pressure mats (model X3 LX100) placed on the backrest (40 x 64 sensors, 50. 8 cm x 81. 2 cm sensing area, mat LX 100:40. 64. 02) and on the cushion (40 x 40 sensors, 50. 8 cm x 50. 8 cm sensing area, mat LX 100:40. 40. 02). Using the associated software (XSENSOR Pro V8), acquisition frequency was set at 1 Hz to ensure detection of postural changes, whose frequency range is known to be lower. This system afforded a measurement range of between 0. 07 and 2. 7 N/cm 2 and a measurement threshold of 0. 07 N/ cm 2. Moreover, it provided an accuracy of ± 10 mmHg and uncertainty was calculated at ± 5.
Data processing Pressure analysis. All pressure data were analyzed via a MATLAB program (vR2017a, MathWorks Inc. , Natick, USA), which calculated the contact pressure (CP) and the contact surface (CS) for both pressure mats. The seat pressure distribution percentage (SPD%), representing the homogeneity of pressure distribution  [33] , was calculated only for the cushion mat. The lower the SPD%, the more uniform and homogeneous the pressure distribution. The first step of the analysis consisted in importing the calibration into the program so as to include the driver's weight and contours applied at the interface with each seat. After calculation of CP and CS for the full mats, spatial cutting was performed to divide each mat in such a way as to obtain CP and CS for six A temporal division of all pressure parameters according to time segments was performed for all subjects and all seats.
Moreover, all pressure parameters were normalized in terms of time spent in each road segment according to areas under the curve for each pressure parameter. This was calculated as follows: AUC of segment n / time spent in segment n (sec). However, pressure parameters were not normalized in terms of driver's bodyweight due to our within-subjects design. Pressure parameters were expressed as mean ± standard error of the mean (SEM) for each normalized road segment. Specifications for repositioning movements. A MatLab program was used to dynamically read backrest pressure and surface data and to calculate changes in trunk posture. Only macro-movements were considered as repositioning movements. Prior to RM identification, a calculation of the force applied (F) on the backrest was performed, combining CP and CS data.
Acknowledgments The present study was supported by the  OpenLab "Automotive Motion Lab " which is a collaborative structure involving  Aix-Marseille University ,  CNRS , and  Groupe Stellantis. We would like to thank  Serge Tribut ,  Julien Bonnet ,  John Masson ,  Antonio Gomes  and  Christophe Broquet  from Groupe PSA for their work on the vehicle and on the seats. Our thanks to  Issam Rosti  ( ALTRAN, Morocco ) for his help with the Matlab analysis, and to  Marjorie Sweetko  for English language editing.
Stimuli and Procedure The experiments were programmed using the CRS Toolbox and Color Toolbox (CRS, UK) for MATLAB (Mathworks, USA). In Experiment 1, stimuli were rendered on a 21-inch Viewsonic P227F CRT Monitor which was placed 70 cm away from the participant. The monitor was controlled through a Visage system (CRS, UK) and calibrated using ColorCAL2 (CRS, UK). Colours were generated on the basis of measurements taken with a SpectroCAL (CRS, UK). Participants gave their responses using a Cedrus R530 response box (San Pedro, USA). In Experiment 2, colours were presented on a Display ++ (CRS, UK) device. Responses were recorded using a CT-6 button box (CRS, UK). Different sets of colours were used in the two experiments. In Experiment 1, stimulus colours were selected from a normative dataset  (Wuerger and Xiao, 2015)  (see Supplementary Figure  S3A ).
The hue angles for unique red (UR) and unique green (UG) stimuli corresponded to mean values in the dataset -in the perceptually uniform CIE 1976 UCS space  (Schanda, 2016) , hue angles for the UR and UG stimuli were 14. 4º and 133. 4º respectively. Orange and turquoise stimuli were chosen such that orange (hue angle 41. 5º) was the intermediate hue between UR and unique yellow, and turquoise (hue angle 185. 1º) was the intermediate between UG and unique blue. All four stimuli were equally saturated in the CIE 1976 UCS plane. Three stimulus luminance levels were used: nominal isoluminance (24 cd/m 2 ), 45% Weber contrast (34. 8 cd/m 2 ) and 90% Weber contrast (45. 6 cd/m 2 ). In Experiement 2, stimuli consisted of participants' subjective settings for two unique hues (yellow and green) and two intermediate hues (orange and turquoise), and hues situated 10° to the left and right of the subjective settings (in CIELCh colour space).
Thus, for each observer, we effectively had four clusters of colours corresponding to the hues orange, yellow, green, and turquoise, with each cluster consisting of the observer setting for that hue, along with two flanking colours ±10° from the setting (e. g. , unique yellow, a yellow 10° counter-clockwise and a yellow 10° clockwise). All colours were nominally isoluminant with the background  (CIE 1931 xyY coordinates: 0. 3127, 0. 3290, 22. 93  / 2 ). EEG data was recorded during a shape discrimination task. The purpose of the task was to engage participants' attention in a stimulus dimension orthogonal to colour -i. e. , shape. The stimuli consisted of uniformly coloured shapes shown against a grey background. Each trial began with the appearance of a fixation cross, followed by a 2-degree circular stimulus (passive viewing event) which changed shape (shape change event) into either a diamond or a square (Figure  1 ).
EEG recording and pre-processing Continuous brain activity was recorded from 64 scalp locations using active Ag-AgCl electrodes and 4 ocular channels (providing VEOG and HEOG) connected to a BioSemi Active-Two amplifier system (BioSemi, Amsterdam, The Netherlands) at a sampling rate of 256 Hz. Data processing was performed using EEGLAB  (Delorme and Makeig, 2004)  for Matlab (Mathworks, UK). Epochs lasting 900 ms were extracted: 200 ms before the relevant event (stimulus onset or shape change) and 700 ms afterwards. Data was low pass filtered at 40 Hz. All trials with incorrect answers were excluded prior to the analysis. Artifact removal was then performed by using the FASTER toolbox  (Nolan et al. , 2010) , the ADJUST toolbox  (Mognon et al. , 2011) , and self-written procedures in MATLAB.
Experiment 1: Decoding unique and intermediate hues with and without luminance contrast We measured EEG signals in a cohort of 20 participants while they viewed coloured stimuli (coloured shapes on a grey background) consisting of two unique hues -unique green and unique red, and two non-unique hues -orange and turquoise. In each trial, a coloured disc changed shape to a diamond or a square at a random time-point 800-1500ms after stimulus onset (Figure  1 ). The participant's task was to identify the target shape. The stimuli were either isoluminant with the background (0% luminance contrast), or presented at 45% or 90% luminance-contrast. This gave us a dataset of EEG signals labelled both in hue and luminance-contrast. The task was easy, resulting in high overall accuracy (95% ± 1% SE, see Supplementary Figure  S4A ) and very fast responses (mean response time (RT) of 462 ± 15 ms, see Supplementary Figure  S4B ).