dataesr/scientific-paragraphs-categorization huggingface dataset:
KRIPO. PDB ligands structural information were downloaded from Ligand Expo (http://ligandexpo. rcsb. org/) and prepared according to the KRIPO procedure (https://github. com/3D-e-Chem/kripo). Then KRIPO  [15]  v. 1. 0. 1 was used with the list of prepared PDB structures for the pharmacophore fuzzy fingerprints calculations, using default parameters (fragmentation procedure activated). The pairwise similarities of the fingerprints were estimated with kripodb (v. 3. 0. 0) using the modified Tanimoto coefficient as similarity metric. A threshold value of 0. 50 was used to distinguish similar from dissimilar binding sites. ProBiS. In a first place, the surface information (srf files) was computed for each prepared PDB structures with the default parameters referenced in the manual (3. 0 Å to the ligand).
ProBiS  [39]  requires a list of ligand HET code and residue number for each PDB entries. That list was provided to ensure that the ligands/sites considered are the same as in the binding site datasets used for other methods. Then, the alignment and comparison of the srf files were executed with default parameters, except for the Z-score that was set to a high negative value (-9999) as suggested by the authors to enforce the output of all results. Similarity between two binding sites was evaluated by the alignment score and Z-score. A threshold Z-score value of 2. 0 was used to distinguish significant from irrelevant binding site alignments. SiteAlign. For each entry, the list of natural amino acids in the 'SITE' mol2 files were provided as input. SiteAlign  [37]  v. 4. 0 describes a binding site by a polyhedron of 80 discretized triangles annotated with eight possible pharmacophoric features projected from cavity-lining C-α atoms.
Binding of HIV-1 RT inhibitors to human TNF-α (Microscale thermophoresis). Human TNF-α was labeled using the RED-NHS 2nd generation labeling kit (NanoTemper Technologies GmbH) using a protein concentration of 10 µM and a molar dye-to protein ratio ~ 3:1. A label/protein ratio of 0. S1 : sc-PDB subpockets sorted by decreased ProCare similarity to the inner cavity of human TNF-α. Table  S2 : PDB entries describing non-nucleoside inhibitors bound to HIV-1 reverse transcriptase. Table  S3 : Comparison of delavirdine subpockets, resulting from manual fragmentation, with TNF-α trimer pockets. Table  S4 : Dissociation constant (KD) of three HIV-1 RT inhibitor binding to human soluble TNF-α, according to MST experimental conditions. Table  S5 : CHEMBL entries describing HIV-1 RT non-nucleoside inhibitors.
AVAILABILITY OF DATA AND MATERIALS Data. Input  and results data are available at https://github. com/kimeguida/ProCare_TNF. Software. P r o C a r e , v e r s i o n 0. 1. 1 a n d 0. 1. 0 , h t t p s : / / g i t h u b. c o m / k i m e g u i d a / ProCare; Fuzcav, http://bioinfo-pharma. u-strasbg. fr/labwebsite/downloads/FuzCav. t g z ; G -L o S A , v e r s i o n 2. 2 , https://compbio. lehigh. edu/GLoSA; KRIPODB, version 3. 0. 0, http://3d-e-chem. github. io/kripodb; KRIPO, version 1. 0. 1, https://github. com/3D-e-Chem/kripo; ProBiS, http://insilab. org/probis-algorithm/; SiteAlign, version 4. 0, http://bioinfo-pharma. u-strasbg. fr/labwebsite/downloads/SiteAlign-4. 0. tgz; Shaper, version 1. 0, http://bioinfo-pharma. u-strasbg. fr/labwebsite/downloads/Shaper. tgz; RDKit python package, version 2019.
03. 4. 0, https://www. rdkit. org/; ROCS, version 3. 2. 0. 4, https://www. eyesopen. com/rocs; IChem, version 5. 2. 9, http://bioinfo-pharma. ustrasbg. fr/labwebsite/downloads/IChem_v. 5. 2. 9. tgz: Python OpenEye toolkits version 2020. 0. 4; FILTER, version 2. 5. 1. 4, https://www. eyesopen. com/filter; PLANTS version 1. 2, http://www. tcd. unikonstanz. de/plants_download; Python package Matplotlib version 3. 0. 2; Maestro vesion 2019-3, https://www. schrodinger. com/products/maestro; Pymol version 2. 1, https://pymol. org/2; Sybyl-X v. 2. 1. 1, https://www. certara. com/sybyl-x-software; MarvinSketch v e r s i o n 1 6. 1 0. 1 7 , https://chemaxon. com/products/marvin;.
a Target Site Number of subpockets b ROCAUC HIV-1 RT non-nucleoside 195 (122) 0. 84 β2 adrenergic receptor orthosteric 14 (14) 0. 35 Carbonic anhydrase II catalytic 183 (137) 0. 38 Cyclin-dependent kinase 2 catalytic 461 (274) 0. 63 Heat shock protein 90α catalytic 214 (117) 0. 64 Thrombin catalytic 253 (126) 0. 35 a For each target, the similarity scores of the corresponding sites (actives) and decoys (any other site) to the TNF-α query (PDB ID 6OOY) is used to compute the area under the ROC curve. bThe number of PDB entries are in brackets.
Afterwards, the data were fitted to a special cubic polynomial model applying the least squares regression to estimate the unknown coefficients in Equation (  4 ): Y = b 1 X 1 + b 2 X 2 + b 3 X 3 + b 12 X 1 X 2 + b 13 X 1 X 3 + b 23 X 2 X 3 + b 123 X 1 X 2 X 3 ( 4 ) where Y is the response, bi the magnitude of the effect of each component Xi, bij the magnitude of the interactive effect of two-components, and bijk is the magnitude of the interactive effect of the three components on the response. Xi denotes the proportions of the component (i) in the mixture. This analysis was carried out using the SAS JMP software, version 8. 0. 1. Three mixtures were carried out. The first mixture, M1, combined the EOs of O. compactum, O. majorana, and T. serpyllum; the second mixture, M2, combined Eos of O.
majorana, T. serpyllum, and M. spicata; the third mixture, M3, associated A. herba-alba, M. communis, and T. serpyllum EOs (Table  2 ). The formulation was carried out to obtain solutions containing 3 mg/mL. The antioxidant activity of the mixtures was tested as described below in the screening. The experimental design for each mixture is composed of 15 experiments: three replications of the central point, and two for the other experiments. Afterwards, the data were fitted to a special cubic polynomial model applying the least squares regression to estimate the unknown coefficients in Equation (  4 ): Y = b 1 X 1 + b 2 X 2 + b 3 X 3 + b 12 X 1 X 2 + b 13 X 1 X 3 + b 23 X 2 X 3 + b 123 X 1 X 2 X 3 ( 4 ) where Y is the response, b i the magnitude of the effect of each component X i , b ij the magnitude of the interactive effect of two-components, and b ijk is the magnitude of the interactive effect of the three components on the response.
• C, 230 • C, and 150 • C, respectively, operating at 70 eV ionization energy and scanning the m/z range 50-550. The column used was an Agilent DB5 ms capillary column (30. 0 m × 0. 25 mm × 0. 25 µm film thickness) programmed from 60 • C to 245 • C at 3 • C/min. Essential oil samples were diluted with hexane (Sigma Aldrich) (1:3000). The injected volume was 2. 0 µL, in splitless mode, and the injector temperature was 250 • C. Identification of the individual components was based on: comparison with the NIST MS Search database 2012 where possible and the Adams terpene library [32].
Acknowledgments: The authors are grateful to the  Region Bretagne  for Ph. D. Grant of  C. Hubert  and  C. -H. Pélisson  as well as the  Institut des Sciences Chimiques de Rennes  ( UMR 6226 ) for the financial support of both ENSCR teams. The authors are indebted to  Patricia Beaunier  from the  Service de Microscopie Electronique at Sorbonne Universités UPMC  for Transmission Electron Microscopy analyses.
a b s t r a c t Near-infrared (NIR) spectra of pharmaceutical tablets get affected by light scattering phenomena, which mask the underlying peaks related to chemical components. Often the best performing scatter correction technique is selected from a pool of pre-selected techniques. However, the data corrected with different techniques may carry complementary information, hence, use of a single scatter correction technique is sub-optimal. In this study, the aim is to prove that NIR models related to pharmaceuticals can directly benefit from the fusion of complementary information extracted from multiple scatter correction techniques. To perform the fusion, sequential and parallel pre-processing fusion approaches were used. Two different open source NIR data sets were used for the demonstration where the assay uniformity and active ingredient (AI) content prediction was the aim.
In this study, the aim is to prove that NIR models of pharmaceutical products can directly benefit from the fusion of complementary information extracted from multiple scatter correction techniques. To perform the fusion, sequential and parallel pre-processing fusion approaches were used. Two different open source NIR data sets were used for the demonstration where the prediction of assay uniformity and active ingredient (AI) content was the aim. As a baseline, the fusion approach was compared to partial least-squares https://doi. org/10. 1016/j. jpba. 2020. 113684 0731-7085/© 2020 The Author(s). Published by Elsevier B. V. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/ 4. 0/). regression (PLSR) performed on standard normal variate (SNV) corrected data, which is a commonly used scatter correction technique.
Data set Two open source tablet data sets were used for this study. The first was the 'NIR shootout 2002' data set published by the International Council for Near-infrared Spectroscopy. The data were downloaded from the official website of Eigenvector Research Inc, USA in MATALAB readable format (http://www. eigenvector. com/ data/tablets/index. html). The data in the downloaded. mat file contained spectra from two instruments. In this study, the spectra corresponding only to instrument 1 was used. In total, 654 tablets were measured with NIR (600-1898 nm) and reference analysis (assay uniformity). The second data set comprised NIR transmission data related to measurement of 310 tablets and the corresponding active ingredient (AI) content (%). The data were obtained from http://www. models.
Then the residuals of Y are fitted to the orthogonalized X block after application of the second pre-processing method and the scores (T2) are estimated. The procedure is continued for as many blocks (4 in this case) as there are pre-processing methods. In this work, the order of application of the scatter correction methods was 2nd derivative, VSN, SNV and MSC, making a total of 4 blocks of data. In the case of PORTO, a combination of PLS regression, generalized canonical analysis (GCA) and multiple orthogonalization steps are performed with the aim of extracting the common and distinct information presented in data pre-processed with different scatter correction methods. The concept of PORTO is to identify common and distinct information as shown in Fig. 1B. The three circles represent data pre-processed with three different scatter correction methods and the letters D and C indicate the distinct and the common information, respectively.
Optimising the number of LVs is highly important for both SPORT and PORTO. In the case of SPORT, all possible combinations of LVs were explored with the optimum number chosen based on the lowest RMSECV. In PORTO, several local cross-validations (CVs) were performed in sequence, as discussed in  [17]. SPORT was implemented with the algorithm presented in  [12]  and with freely available multiblock data analysis toolbox  [18]. PORTO was implemented using the multi-block data analysis codes from NOFIMA  (https://nofima. no/en/) for the implementation of parallel orthogonalised partial least-squares (POPLS). All analysis was performed in MATLAB 2017b (The MathWorks, Natick, USA).
PLSR modelling vs SPORT vs PORTO The results from PLSR with SNV pre-processed data and the pre-processing fusion approaches (SPORT and PORTO) are shown in Fig. 2. It can be seen that fusion of information from different scatter correction methods improved the predictive performance of the models. In the case of prediction of assay uniformity with NIR data (Fig. 2A, C, E ), the SPORT approach decreased the error and bias by 9% and 47 %, respectively, compared to PLSR with SNV pre-processed data. The PORTO approach further decreased the error and bias by 15 % and 57 %, respectively, compared to PLSR with SNV pre-processed data. For prediction of the AI content with NIR data (Fig. 2B, D, F ), the main improvements were observed in error reduction with a 6% and 3% decrease using SPORT and PORTO approach, respectively.
The improvement was attained with the same number of LVs as for the PLSR model constructed using SNV pre-processed data (3 LVs). Further, in all cases the best models were obtained using LVs from data corrected using multiple scatter correction methods, explaining that complementary information is being extracted and modelled by the pre-processing fusion approaches (PORTO and SPORT). As an example to show the complementary and efficient information modelled by the SPORT and PORTO approaches, the loadings from prediction of the AI are shown in Fig. 3. It can be seen that com-Fig. 3. Loadings corresponding to PLSR applied to SNV pre-processed data (A), SPORT (B) and PORTO (C) modelling approaches. (A) Three loadings were extracted by PLSR for optimal modelling, (B) three loadings were extracted by SPORT approach (2 from VSN pre-processed data and 1 from SNV pre-processed data), and (C) three components were extracted by the PORTO approach (1 common component for all pre-processing methods and 2 distinct components from 2nd derivative).
By using SICM as a charge-sensitive technique, 150 the interdependence between surface charge and electrocatalytic activity was studied by Tao et al. 151  in defective highly oriented pyrolytic graphite (HOPG). SICM data of pristine HOPG were acquired with a 0. 5 mm nanopipette-HOPG distance. Aer the introduction of defects by plasma irradiation, the distance changed from 0. 5 to 6. 6 mm, indicating an increase in the surface charge of the sample (Fig. 14 ). ORR performance was better in the defective HOPG, with an onset potential of 0. 745 V vs. the reversible hydrogen electrode (RHE) compared to 0. 171 V in pristine HOPG in 0. 1 M KOH.
Acknowledgements The authors acknowledge the  SENTINEL  project, funded by the  European Union 's  Horizon 2020 Research and Innovation Program  under the  Marie Skłodowska-Curie  grant agreement no. 812398 , and the  PEGASUS  project, funded by the  European Union 's  Horizon 2020 Research and Innovation Program   FCH-01-2-2017 , no. 779550.
Acknowledgements Pierre Milcendeau thanks the  CHARMMMAT Laboratory of Excellence  ( ANR-11-LABX0039 ) and the  Université Paris-Saclay  for financial support. This work was granted access to the HPC resources of CINES under the allocation 2020-A0070810977 made by  GENCI. We thank  Valérian Gobé  for his contribution to the very early experiments of this project.
Acknowledgements This work has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  GENIORS , grant agreement  N° 755171 ). RT and MC (NNL) were additionally supported by the  £46m Advanced Fuel Cycle Programme as part of the UK Department for Business, Energy and Industrial Strategy's (BEIS) £505m Energy Innovation Programme.
The structure of the synthesized powder was determined by X-ray diffraction (XRD) with a RU-200B rotating anode X-ray generator using Mo Kα1,2 radiation (λ=0. 7093Å) in transmission mode. Spherical diffraction patterns were acquired using a photo-sensitive plate that was digitized using a scanner. The software FIT2D  [16]  was used to integrate the diffraction patterns. The morphology and composition of the In-Pb and In-Pb:C powders were studied by Scanning Electron Microscopy coupled with energy X-ray dispersive spectroscopy (EDS) using a FESEM JEOL JSM-7001F or a SEM-FEG Carl Zeiss Ultra 55. The Saclay nuclear microprobe was also used to determine In and Pb contents using Particle-Induced X-ray Emission (PIXE) with a proton microbeam of 3×3 μm 2 at 2595 keV. Composite electrodes were prepared by mixing In-Pb or In-Pb:C powders, poly(vinylidene fluoride) (PVDF, Solef, Solvay) and Super P carbon black (Timcal) in a 80/10/10 mass ratio with N-methyl-2-pyrrolidone (NMP, 99%, Acros Organics) and casted onto a copper foil (99.
Experimental Section All experiments were carried out under argon atmosphere using a glovebox or a vacuum line using standard Schlenk techniques unless some special conditions are pointed out. s singlet (s), doublet (d), triplet (t), virtual triplet (vt), quartet (q), quintet (qt), multiplet (m). IR spectra were recorded on a Nicolet 6700 FT-IR spectrometer equipped with a Praying Mantis mirror chamber (from Harrick Scientific) by using a DRIFT cell equipped with KBr windows. The samples were prepared under argon in a glovebox. Typically, 64 scans were accumulated for each spectrum (resolution 4 cm -1 ). Data are reported as follows: weak (w), medium (m), strong (s) and very strong (vs).
Acknowledgments This work was performed in partnership with the SAS PIVERT, within the frame of the  French Institute for the Energy Transition (Institut pour la Transition Energétique (ITE) P. I. V. E. R. T. (www. institutpivert. com)selected as an  Investment for the Future ("Investissements d'Avenir "). This work was supported, as part of the Investments for the Future, by the  French Government  under the reference  ANR-001-01. The authors also thank the  CNRS  and  University of Lille  for their financial support.
ACKNOWLEDGMENT This work has been carried out within the H- CCAT  project that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  720996. F. M. W. gratefully acknowledges financial support from the  Deutsche Forschungsgemeinschaft (DFG , grant number  WI 4721/3-1 ) and from  CNRS  through  Momentum 2018 excellence grant. The authors are very grateful to IRCELYON scientific services and to  Prof. Dr. G. Kickelbick  ( Saarland University ) for providing access to CHN-analysis.
Using the bottom-up approach to proteomics, CPTAC noted recently that "the aggregated NCI-60 proteomics dataset covers only 12% of the whole encoded proteome, and only ~5% of the genes had sequence coverage of >50% of their protein coding regions. " 4 of 8  (13). Regarding alternative splicing, "there is yet a major gap between the number of alternative transcripts asserted by RNA sequencing and that detectable by proteomics (e. g. , <0. 1% of putative novel splice junctions in cancer xenografts)" (  13 ). This state of affairs underscores the critical need to advance the state of the art in proteomic analysis (14-17) via new technologies and extensive proteoform-level characterization of biological systems. To achieve the objectives outlined above, it is critical to expand our technological abilities through a concerted long-term and multifaceted research and development effort.
THE PIVOT FROM PROTEOFORM DISCOVERY TO PROTEOFORM SCORING A central principle in comprehensive proteoform analysis concerns the distinction between discovery and scoring. Comprehensive analysis of protein primary structure requires the generation of highly complex data necessary in the discovery phase of proteoform analysis. However, once we have in hand a comprehensive index of these proteoforms for the system under study, efforts can shift to a scoring mode informed by the previous knowledge. This transition from discovery to scoring is central to many fields: in genomics, for example, the initial discovery of single-nucleotide polymorphisms (SNPs) led to the generation of SNP databases and technologies for their scoring at scale. The scoring technology enabled cost-effective functional studies and disease-based research across human populations.
Acknowledgments: We acknowledge  M. W. Mullowney  for assistance in generating figures. Funding: J. N. A. was supported by the  ALS Association  under award 508452. Y. G. was supported by the  NIH  under awards  R01 HL096971 ,  GM117058 , and  GM125085. N. L. K. was supported by the  National Institute of General Medical Sciences (NIGMS)  at  NIH  under award  P41GM108569  and the  NIH Common Fund  under award  UG3CA246635. J. A. L. was supported by the  NIH  under award  R01GM103479  and the  Department of Energy  under award  DE-FC02-02ER63421. L. M. S. was supported by the  NIGMS  under award  R35GM126914. L. P. -T was supported under the  Intramural program  at the  Environmental Molecular Sciences Laboratory  (a  DOE Office of Science User Facility  sponsored by the  Office of Biological and Environmental Research  and operated under contract  DE-AC05-76RL01830 ) and the  NIH Common Fund  under award  UG3CA256959.
Rheology The rheology experiments were performed on a Malvern Kinexus rheometer (Malvern Instruments limited, Worcestershire, UK). The data were analyzed using the software rSpace for Kinexus by Malvern Panalytical. The geometry used for the experiment was a 15. 4 mm cup and a 14 mm cylinder. Complementary experiments were performed on a TA Instruments ARES rheometer (New Castle, USA). In this case, analysis of the data was done in the software TA Orchestrator and the geometry used was a 16. 5 mm cylinder and 17 mm cup. The flow curves were recorded at a shear rate range of 0. 01-300 s À1 with 7 measurements per decade. The linear viscoelastic region (LVER) was determined for each system, and subsequent experiments were conducted at a constant shear stress of 0. 05 Pa over the frequency range 0.
Dynamic light scattering (DLS) The hydrodynamic radius (R H ) of the investigated systems was determined with a Zetasizer Nano-ZS (Malvern Instruments Ltd. , Worshestershire, UK). The laser was a 4 mW He-Ne laser with a wavelength of 632. 8 nm. Measurements were conducted in backscattering mode, with a scattering angle of 173°. The surfactant samples (0. 5 ml, 10 mM) were injected in PMMA semi-micro disposable cuvettes purchased from BRAND GmbH (Wertheim, Germany) and analyzed from 70 °C to 10 °C with steps of 5 °C. Subsequent to any change in temperature the sample was equilibrated for 5 min and triplicates were measured for every point. Precipitation was detected at 20 °C and below for samples with high b-C 16 G 2 ratios and results from such measurements were thus removed from the data set.
Q. Analysis of the SAXS and SANS data was performed in SasView 4. 2. 2 by fitting to form factor models conforming to the micellar shape  [27]. As the Q-range covered in the SAXS experiment did not reach sufficiently low-Q values to observe the longest dimension of the micelles, the model used for the SAXS data was a core-shell cylinder model describing the cross-section dimensions of the micelles. The SANS data were fitted with a flexible cylinder model, as these experimental data allowed the length and stiffness of the micelles to be determined. For a detailed description of the analysis of the SAXS and SANS data, see previous work  [4].
The fit to Maxwell's model is poor for G'' at high frequencies. This is common for WLM systems, since at shorter time-scales additional relaxation mechanisms, for instance stretching of the micelles, which are not accounted for in the Maxwell model become significant  [21]. The differences in the rheological behavior of aand b-C 16 G 2 anomers can be explained by the formation of different micellar structures, as confirmed by SANS. We found that a-C 16 G 2 behaves as a Newtonian fluid, consistent with the previous report that this anomer forms elongated micelles that are not sufficiently long to confer the system non-linear response to stress, even at high surfactant concentration  [4]. Scattering data show that b-C 16 G 2 forms WLM  [4] , which in the semi-dilute regime correlates well with the non-linear response to stress, i.