dataesr/scientific-paragraphs-categorization huggingface dataset:
1A , B and Table  2 ). One of the main factors that explains this wide range is of course the technical capability of the different instruments used. To roughly estimate the contribution of this factor, normalized values have been calculated (black points in the graph) using as normalization factor the ratio between the reported number of total peptides in the analysis of the preenrichment sample for each experiment (Table  2 ), and the average values for all the experiments. Using this normalization to "compensate" for instrument performance, the inter-laboratory %CV for the number of phosphopeptides decreases from 66% to 36% (Fig. 1B ). Other factors accounting for this variability would certainly include the enrichment protocol used, as well as the parameters used for data processing and database searches, but also reflect the different expertise of the different laboratories.
Acknowledgements We thank  Kevin Ray  from  MilliporeSigma, Saint Louis, MO , for fruitful discussions on the study design, and  MilliporeSigma  for kindly providing the Phosphomix standards used in the study. ProteoRed, PRB3 is supported by grant  PT17/0019/0001 , of the PE I+D+i 2013-2016, funded by  ISCIII  and  ERDF.
The heating of a chromophore due to internal conversion and its cooling down due to energy dissipation to the solvent are crucial phenomena to characterize molecular photoprocesses. In this work, we simulated the ab initio nonadiabatic dynamics of cytosine, a prototypical chromophore undergoing ultrafast internal conversion, in three solvents-argon matrix, benzene, and water-spanning an extensive range of interactions. We implemented an analytical energy-transfer model to analyze these data and extract heating and cooling times. The model accounts for nonadiabatic effects, and excited-and ground-state energy transfer, and can analyze data from any dataset containing kinetic energy as a function of time. Cytosine heats up in the subpicosecond scale and cools down within 25, 4, and 1. 3 ps in argon, benzene, and water, respectively.
52. For the simulations in argon and benzene, we ran 50 trajectories each. In water, we ran 75 trajectories. The initial state was distributed over S 1 to S 3 according to the transition probabilities in the 5. 25 AE 0. 25 eV energy window. 4] [65] [66]  The MM calculations were done using TINKER software. 67 Surface hopping dynamics was performed using the NEWTON-X software  68  interfaced with COLUMBUS and TINKER.
Acknowledgements E. V. thanks the  Brazilian agencies CAPES  for funding the grant through the  Capes/PrInt  project (Grant number  88887. 467063/2019-00 ). E. V. and S. A. M. thank the  Brazilian agency CNPq  (Grant numbers  303884/2018-5, 423112/2018-0  and  310123/ 2020-8 ) for support. M. T. do C. , J. M. T. and M. B thank the support of the  FetOpen grant BoostCrop (Grant agreement   828753 ). M. P. and M. B. thank the funding provided by  European Research Council (ERC)   Advanced grant SubNano (Grant agreement   832237 ). Centre de Calcul Intensif d'Aix-Marseille  is acknowledged for granting access to its highperformance computing resources.
It has been shown that the differential configuration significantly improves the accuracy of isotherm measurements and minimises the effect of temperature instability  [42]. In this contribution we present a new Adsorption Differential Volumetric Apparatus (ADVA) designed for accurate determination of fast mass transfer kinetics in nanoporous materials from vacuum to atmospheric conditions. The main aim is to validate the new system through the study of N 2 and Ar diffusion in a single zeolite pellet of 4A and its fragments in the pressure range 0. 8 to 55 kPa. These systems are chosen for their weak adsorption feature in order to demonstrate the high sensitivity of the ADVA. The N 2 -4A system has been studied extensively  [6, [46] [47] [48] [49] [50] [51] [52] [53]  while less data have been reported for Ar -4A  [50, [52] [53] [54] , with both systems showing micropore diffusion control.
The kinetics at 54 kPa were slightly faster and a time constant of 200 s was obtained. This pattern of behaviour is consistent with the previous study by Ruthven and Derrah  [50]  in that the micropore diffusivity increases at higher concentration due to the nonlinearity of the isotherm. As the isotherm is more linear at 20 • C and 35 • C, the diffusivity was found to be invariant with pressure and only a single time constant was extracted for each temperature. It should also be noted that this set of data was obtained with the latest system configuration. To prevent sample contamination in the dosing volume, two 60 μm filters were installed at the outlets of LV4 and LV5. The filter adds a resistance to the gas flow, thus resulting in a slower initial response especially at low pressures. This can be observed clearly in Fig.
Conclusions In this study, a novel differential volumetric apparatus (ADVA-1) developed for the accurate determination of gas diffusional time constants in nanoporous materials over the pressure range from vacuum to 130 kPa was presented. On the ADVA-1 a ± 3. 5 kPa differential pressure transducer was employed to measure adsorption kinetics and thanks to the fast data acquisition module, time constants of a few seconds can be measured. Since for kinetic measurements a small sample mass is desirable to minimise any secondary effects, ADVA-1 was built with small cell volumes for high sensitivity and this allows the measurements of even weakly adsorbed molecules on individual beads or pellets. To demonstrate the advantages of ADVA-1 and also to validate the system, N 2 and Ar diffusion in zeolite 4A fragments and a single pellet (both ~ 40 mg) were studied.
00 or 7. 50 μL of a 10 mM solution of Na2S2 (final concentrations: 2. 5, 5. 0, 10. 0 and 15. 0 μM). The esterase activity was recorded at various time intervals by monitoring the slope of the absorbance at 405 nm vs. time upon addition of 1 mL of the aforementioned solution to 3 μL of a 200 mM solution of pNPA in DMSO. The pseudo-first order rate constants kobs were obtained for each concentration by fitting the data with SigmaPlot 10 to the mono exponential function A[1exp(-kobs t)] + B. The bimolecular rate constant kinac is the slope of the linear fit obtained from the plot of kobs vs. [Na2S2]. DJ-1 protection of guanosine monophosphate (GMP) against modification induced by methylglyoxal (MGO) was determined by LC-MS. GMP (40 μM) was incubated with MGO (400 μM) and either wt DJ-1 (4 μM) or wt DJ-1 + Na2S2 (4 μM, from a stock solution prepared by incubating 40 μM DJ-1 with 60 μM Na2S2 for 20 min at 20 °C) for 12 min at 37 °C.
Data were calculated by measuring the surface area of the glycated GMP peak (m/z = 436. 09, ESI+) or lactate (m/z = 89. 00, ESI-). Note, 100% protection corresponds to the reaction [GMP + MGO + DJ-1], while 0% protection corresponds to the reaction [GMP + MGO], since 6 μM Na2S2 did not influence the reaction between GMP and MGO.
Molecular Dynamics The crystal structure of the DJ-1 dimer was downloaded from the Protein Data Bank (PDB ID: 3SF8, https://www. pdb. org [19b] ). The dimer formed by sub-units A and B was prepared using the Prepare Protein module in Biovia Discovery Studio ® (DS) 2021 with the default parameters and the CHARMM force field. Briefly, all crystallographic water molecules were removed. Bond orders were assigned, and hydrogen and missing atoms were added. The protonation states on protein were adjusted at pH 7. 4. C106 was deprotonated and E18 protonated on the basis of previous studies [19], sulfur or oxygen atoms were added to the sulfonate form of C106 when required, and the structures were minimized. To assess the influence of persulfidation at C106 and compare it to the wt or sulfinylated protein, 70 ns molecular dynamics (MD) simulations were run using the CHARMM36m force field and the NAMD protocol  [37]  implemented in DS 2021.
Scheme 1. General reaction between cysteine and 2,2'-dithiopyridine (DTP). The release of thiopyridine TP upon the reaction between DJ-1 and DTP can easily be monitored by UV-visible spectroscopy at 343 nm. The kinetic data obtained at pH = 7. 4 (Figure  1 , blue data) can be nicely fitted with a double exponential function, yielding kobs values of 1. 667 ± 0. 002 and 0. 088 ± 0. 004 min -1. This observation suggests either the reaction of two of the three Cys residues of DJ-1 with DTP or the reaction of a single Cys residue with DTP followed by reduction of the mixed reactive disulfide by a second Cys residue. To distinguish between these two processes, we performed a similar experiment with the mutant C106S DJ-1. Its reactivity with DTP yields a single kobs (0. 098 ± 0. 006 min -1 ) similar to the slower kobs previously measured with the wt protein, ruling out the second hypothesis.
Acknowledgement S. Renou acknowledges the financial support of the  French Research Ministry. Authors from CEISAM acknowledge the  French National Infrastructure for Metabolomics and Fluxomics MetaboHUB - ANR-11-INBS-0010  (www. metabohub. fr) and the  Corsaire metabolomics core facility (Biogenouest). This work includes NMR experiments carried out on the CEISAM NMR platform.
Crystal Synthesis The determination of the chemical constitution of the synthesized crystals were performed by Fourier transform infrared spectroscopy (FT-IR). The obtained spectrum is represented in Figure  6A. Figure  6B  shows the evaluation of the percentage of inhibition of calcium oxalate by the turbidimetric method with different concentrations of extracts of roots of Saussurea costus (Falc) Lipsch, and the positive control of cystone. Analysis of the data in Figure  5B  showed a high efficiency of the ethanolic extract compared to the aqueous extract in inhibiting calcium oxalate crystals. In addition, the percentage of inhibition of crystals is (91. 017 ± 0. 299) (p < 0. 05) for the ethanolic extract of Saussurea costus (Falc) Lipsch of 2 mg/mL, followed by the aqueous extract of Saussurea costus (Falc) Lipsch with a percentage of (79.
The European Biochar Certificate (EBC) (https://www. european-biochar. org/en, accessed on 28 March 2022) is well-known for its efforts in this area, and already suggests biochar qualities for certain applications. However, there is still a long way to go before one can rely on databases that will suggest biochars, their activation/functionalization methods, and the best applications for them. This work contributes to the ongoing research on low environmental impact and economically attractive methods to improve the adsorption performance of biochars by functionalizing and activating the materials produced in a standard medium-temperature pyrolysis process. The current research results discuss the suitability of biochars from flax shives and beech wood as pollutant adsorbents. The biochars were post treated with either a mild organic acid (citric acid), or with oxidation of the surface by heating up briefly at 250 • C in a standard muffle furnace.
Biomass Feedstock and Biochar Production The ultimate and proximate analysis of the feedstock is shown in Table  1 , data are property of TNO (Phyllis database, https://phyllis. nl/, accessed on 28 March 2022, and HortiBlue-C project). These two feedstocks do not show any large differences in their composition, they are not contaminated, and the ash content is relatively low; therefore, there are no contraindications as to their conversion into biochar based on EBC suggestions. The high calcium content of the flax shives is expected to influence the pH of the resulting biochar, as will be discussed in the relevant section on the biochar characterization results. The pyrolysis trials have been performed at TNO (Dutch Institute of applied research) in the Netherlands, in an indirectly heated auger-fed (screw) reactor (Figure  1 )  [11].
The Carbamazepine solutions were prepared by dissolving a quantity of CMZ in ethanol and adding distilled water to form the various concentrations. A Shimadzu UV-visible spectrophotometer (SHIMADZU UV-1800, Canby, OR, USA) was used for the quantification of the concentrations at λ max 664 nm for methylene blue and λ max 265 for carbamazepine. The calibration of the UV-vis spectrophotometer was carried out by plotting various optical densities at various concentrations of methylene blue dye in distilled water. The absorbance and concentration are plotted (Figure  3a, b ) to obtain the calibration data for the two components. The calibration curves are available in the Supplementary Materials (Figure  S1 ).
q e = V m (C 0 -C e ) (1) Removal efficiency = (C 0 -C e ) C 0 × 100% (2) where q e is the amount of adsorbate in mg per gram of adsorbent, C 0 and C e are initial concentration and equilibrium concentration, respectively, of the pollutant (mg/L) as calculated by the calibrated spectrophotometer absorbance signal, V is the volume of the solution (L), m mass of adsorbent (g). From the data on C e , q e , and C 0 the saturation curves and the isotherms were drawn. Further testing with CMZ was carried out only with the biochar that showed better adsorption efficiency on methylene blue. To gain insight into the role of the pH in the adsorption, some supplementary tests were carried out with varying pH values for the two model pollutants. In aqueous phase adsorption systems including active carbons and clay materials, factors that affect the adsorption of dyes onto biochar and its modified forms are solution pH, temperature, initial dye concentration, or the presence of other substances  [32].
Acknowledgments: The excellent contributions of engineering students  Paul Jamard  and  Marion Bazin  in carrying out some of the experiments and the characterisation of the biochar materials during their internship, under the supervision of  Sylvain Giraudet , lecturer at the  Chemical Engineering School of Rennes , are highly acknowledged. In the same way, the authors would like to thank  Ivane LELIEVRE  and  Sali MOUHAMADOU  ( Unilasalle Rennes ) for their technical help. The authors are.
Investigation Techniques We employed a number of approaches to investigate the two produced complexes in the remaining part of our examination. The morphology and the elemental composition were observed using a scanning electron microscope JEOL-6610-LVSEM spectrometer (JOEL-IT 300, Tokyo, Japan), operated at an acceleration voltage of 10 KV, coupled with an energy-dispersive X-ray spectrometry detector (Oxford X-Max Micro-analysis) (EDX, Oxford, UK) to obtain semi-quantitative elemental results about a specific location for both complexes (I) and (II). It is worth noting that a platinum-rich tape was used to partly cover the sample in order to minimize charge effects. Then, suitable crystals of (I) and (II) were selected with a stereo-microscope and then mounted for XRD data collection on a Rigaku OD SuperNova diffractometer (four-circle kappa geometry goniometer) (Rigaku Oxford Diffraction, SuperNova, AC3, TUD) with an Atlas CCD detector (Agilent Technologies), and a Bruker D8 Venture diffractometer with a PHOTON III 14 CPAD detector (bleuscientific, UK), respectively.
Both data collections were carried out with a microfocus X-ray source at Mo(Kα) energy. CrysAlisPRO  [37]  and APEX4  [38]  software were respectively used for (I) and (II)to optimize the data collection strategy after cell parameters and orient matrix determination and realize data processing (integration, scaling and absorption corrections by the multi-scans method). The structure was solved with the SHELXT  [39]  structure solution program using intrinsic phasing and refined with the SHELXL  [40]  refinement package using least squares minimization. Hydrogen atoms were initially located in Fourier residual maps, except in the methanol group, where they were placed in geometrically idealized positions. During the structure refinement, the HX distance of all H atoms was constrained to standard values.
Crystal data and refinement details of compounds (I) and (II) are provided in Table  1 , and the designated bonds and angles can be observed in Table  S1  (Supplementary Materials). For the remaining physical characterizations, single crystals were chosen based on their morphologies. The single crystals were then pulverized into a polycrystalline powder using an agate mortar. Powder X-ray diffraction (PXRD) was used to verify the powder's purity. The PXRD was measured on a Siemens D5000 powder diffractometer (Siemens, Aubery, TX, USA) with Cu-Kα monochromatic radiation (1. 542) and 2θ angular range from 5 • to 50 •.
Computational Methods The fingerprint plots of contacts around the organic molecule in the two crystal structures were computed with the program Crystal Explorer 17. 5  [41]. The contact statistics and enrichment ratios were obtained with the program MoProViewer  [42]. In that case, the Hirshfeld surface was computed around all entities (metal cation, each chloride anion and organic molecule) constituting the asymmetric unit. In all Hirshfeld surface calculations, only the main conformation of the disordered methanol group was considered. For this, moieties not in contact with each other in the crystal packing were selected to obtain an integral Hirshfeld surface around each item. The highly charged H-O/H-N atoms bound to nitrogen or oxygen were differentiated from the hydrophobic hydrogen atom H-C linked to a carbon atom.
Powder X-ray Diffraction Patterns and SEM/EDX Analysis PXRD was carried out to confirm the phase purity of the synthesized materials. The experimental and simulated patterns of (I) and (II) seen in Figure  1  are in good agreement. It is worth mentioning that low temperature data were systematically shifted to a higher 2-theta angle due to cell contraction. However, all of the diffraction peaks matched well with the pure phase with respect to their positions. This confirms the purity and homogeneity of the synthesized materials.
2. 5. Computational Methods The fingerprint plots of contacts around the organic molecule in the two crystal structures were computed with the program Crystal Explorer 17. 5  [41]. The contact statistics and enrichment ratios were obtained with the program MoProViewer  [42]. In that case, the Hirshfeld surface was computed around all entities (metal cation, each chloride anion and organic molecule) constituting the asymmetric unit. In all Hirshfeld surface calculations, only the main conformation of the disordered methanol group was considered. For this, moieties not in contact with each other in the crystal packing were selected to obtain an integral Hirshfeld surface around each item. The highly charged H-O/H-N atoms bound to nitrogen or oxygen were differentiated from the hydrophobic hydrogen atom H-C linked to a carbon atom.
3. 1. Powder X-Ray Diffraction Patterns and SEM/EDX Analysis PXRD was carried out to confirm the phase purity of the synthesized materials. The experimental and simulated patterns of (I) and (II) seen in Figure  1  are in good agreement. It is worth mentioning that low temperature data were systematically shifted to a higher 2-theta angle due to cell contraction. However, all of the diffraction peaks matched well with the pure phase with respect to their positions. This confirms the purity and homogeneity of the synthesized materials.
Acknowledgments: The authors thank Researchers Supporting Project number ( RSP-2021/242 ),  King Saud University, Riyadh ,  Saudi Arabia. The authors thank Taif University Researchers Supporting Project number ( TURSP-2020/272 )  Taif University, Taif ,  Saudi Arabia. The authors would like to thank the  Deanship of Scientific Research at Shaqra University  for supporting this work. We appreciate the  Institut Jean Barriol 's PMD 2 X X-ray diffraction facility at  Université de Lorraine  for X-ray diffraction measurements, data processing and analysis, and submitting reports for publication: http://crm2. univ-lorraine. fr/lab/fr/services/pmd2x(accessed on  1 January 2022 ).
In 2011, Bogdanović and Novaković evaluated the frequency of occurrence of the ferrocene dimer in crystals reported in the Cambridge Structural Databank  [84] , finding that 46. 8% of ferrocene derivative crystals contained a dimer. Vargas-Caamal et al. showed by computations that dispersion is the major contribution to stabilize a metallocene dimer  [85]. In this study, final equilibrium geometries were calculated at PBE/def2-TZVP and PBE-D2/def2-TZVP levels, the latter approach including the D2 version of Grimme's dispersion corrections  [86]. Indeed, DFT methods have shown fundamental problems to describe properly dispersion forces that require suitable methods or corrected DFT methods for a proper description. On the other hand, the impact of dispersion on geometry optimization and calculation of some properties of molecules in their unbound state may be low.
The development of computational tools that can predict adverse effects might thus reduce the attrition rate, avoiding health risks for participants and cutting drug development costs. In general, DSE computational prediction methods range from simple predictors to machine learning (ML) techniques such as support vector machines or multilayer perceptrons (MLPs), and to more complex models based on random forests or deep learning  [6] [7] [8]. However, most of the DSE prediction techniques are still limited by the reliance on euclidean data  [9, 10] , which prevents models from considering relational information in the task, since molecular data must undergo preprocessing to be encoded into vectors, with an inevitable loss of possibly useful information. In this context, usually a molecule structure is encoded in a computer-readable format such as the SMILE string or International Chemical Identifier (InChI) keys, which are strings of symbols of variable length, used by computer software to facilitate the search for molecular information in databases and for creating two or three-dimensional models.
Following the approach proposed in DruGNN  [14] , the data of interest consist of a heterogeneous graph including two types of nodes (drugs and genes) and three types of edges (drug-gene, drug-drug, and gene-gene relationships). Formally, the task is a node-focused, multi-class, multi-label classification problem: namely, the GNN model is trained to predict the DSEs associated with the drug nodes. The method proposed in this paper aims to overcome both the aforementioned limitations by processing directly graph-structured data and by generating NFs at learning time. In this context, the structure of a generic drug can be represented as a single graph, consisting of nodes representing atoms and linked by chemical bonds, making any GNNbased model a proper choice for this task. Indeed, since GNNs can handle graph-structured molecules directly and learn how to encode them during the training process, they are ideal to overcome the limits in DSE prediction and in the calculation of fingerprints, by adapting the molecule representation to the task they are being trained for-thus capturing structural and relational information relevant for the specific task, which is something traditional fingerprints and euclidean-based ML models cannot do.
Furthermore, a mixed inductive-transductive learning scheme  [37]  has been adopted, which takes advantage of the knowledge on DSEs of some drugs in order to predict DSEs on novel drug molecules. The performances of the model have been measured in terms of AUC and F1-score and compared with those of other GNN models, showing that the proposed approach is very promising. The rest of this paper is organized as follows: Section 2 describes the dataset, the data sources, and the GNN model, from both a theoretical and an architectural point of view, with a focus on the fingerprint generator sub-model. Section 3 presents the obtained results and discusses their relevance and meaning. Finally, Section 4 draws conclusions on this work, summarizing the main obtained results and deriving some future perspectives.