dataesr/scientific-paragraphs-categorization huggingface dataset:
To maintain symmetry, the geometric mean of the 2 indices was computed for every pair of countries in the comparison (Fisher-type index): F L PA j h j h j h / / / * = The Fisher-type indexes between each pair of countries were then converted into transitive, invariant multilateral indexes using the Elteko-Koves-Szulc (EKS) method  21, 22  : EKS F F j h l c l j l h c / / / / / =         = ∏ 1 1 where EKS j h / is the EKS PPP between countries h and j; F l j / and F l h / are Fisher PPPs between countries l and j and l and h respectively; c the number of countries involved. Those indexes are the PPPs. Two sets of indices are derived using PPP data for the health, hospital and AIC aggregates 18 : (a) price level indices, the ratio of PPPs to exchange rates; and (b) indices of real expenditures per capita (or standardised measures of volumes), computed by dividing the expenditure aggregate under study by PPPs by population.
In our study, health and hospital expenditure data are based on the System of Health accounts data collection framework. 23 his framework offers guidelines for reporting internationally comparable measures of health expenditure by financing source, provider and type of service. To explore the impact of using different PPPs indices for international comparisons of health spending, we generate Kernel density estimates of volumes of health spending per capita estimated using GDP, AIC and health PPPs. The Kernel density estimation is a non-parametric way to estimate the probability density of a random variable (in our case, per capita health spending), and can illustrate the effect of the PPPs on the shape of the OECD-wide health spending distribution. Finally, we also examine whether the health and hospital price levels are correlated with higher or lower health expenditure from public sources and better or worse outcomes.
Acknowledgements: We would like to extremely thank the whole team of the  Laboratory of Organic Chemistry and Therapeutic Chemistry , particularly to: - Géneviève HOU , for the selected compounds. - Alain NUHRICH ,  Martine LEMBEGE  and  Denis GRAVIER , for their guidance and advice, and for their contribution to the review of this manuscript. - Isabelle BESTEL , for welcoming us and supervising us in her Laboratory.
Synthesis of the neutral NHCÀ Zn adduct precursors The synthesis and characterization of the corresponding neutral precursors [(IPrCl)ZnR 2 ] (1 a, R=Et; 1 b, R=Me) and [(ItBu)ZnEt 2 ] (2) was performed before subsequent ionization. Adducts 1 a, 1 b and 2 (Scheme 1) were prepared by addition of ZnR 2 (R=Me, Et) to 1 equiv. of the corresponding NHC (toluene, room temperature), and isolated in high yields as colorless or paleyellow solids. The NMR data agree with the effective and quantitative formation of 1/1 NHC/ZnR 2 adducts. In particular, the presence of a characteristic 13 C{ 1 H} NMR resonance assigned to the Zn-C carbene moiety (δ = 192. 6, 192. 4 and 186. 0 ppm for 1 a, 1 b and 2, respectively) is consistent with adduct formation. These Zn-C carbene chemical shifts are comparable to those reported for related [(NHC)ZnR 2 ] adducts.
Data Description The dataset described here was obtained from the proteome analysis of the fungus Hemileia vastatrix Berkeley and Broome (Basidiomycota, Pucciniales). A total of 742 proteins of H. vastatrix were identified using the PEAKS software and the proteins were deposited in the MassIVE repository under the ID MSV0 0 0 087665. All the files presented in the MassIVE repository are  All files generated by Peaks software, in which the complete set of spectra and protein identification were analyzed. The tables of identified peptides and proteins can be found in the "export" subfolder. Supplementary_files The iteractive sequence of mappings (Uniprot, DB2DB on BioDBnet and BLASTKoala) and their results. described in Table  1. The proteins identified in H. vastatrix germinating urediniospores ( Fig.
File in the MGF (Mascot generic file) format, containing the peaklist used for identification in the Peaks software, required for validation in the MassIVE workflow. Result_files File in the MZID (mzIdentML) format, containing the identification results exported by the Peaks software, based on the search of the peaklist in the database, both mentioned above, also used for validation in the MassIVE workflow. Search_engine_files.
Acknowledgements This research was supported by  Bayer (Grants for Targets)  (Project code  20. 21. 0 0. 042. 00. 0 0 ),  Embrapa Recursos Genéticos e Biotecnologia ,  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes) ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Institut de Recherche pour le Développement (IRD)  and  CONCAFÉ  (Project code  10. 18. 20. 0 01. 0 0. 0 0 ). The authors thank  Dr Marcelo O. Rodrigues  from  Instituto de Química -UnB  for the inestimable help and  Dr. Eduardo S. G. Mizubuti  ( Universidade de Viçosa ) for providing the isolate used in this study.
name: Centre edu/ProteoSAFe/ dataset. jsp?task=cc71ad75f767451abe72dd1ce0019387 © 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons. org/licenses/by/4. 0/ ) Specifications Table Subject Biological sciences Specific subject area Omics: Proteomics Fungus proteomics Type of data Raw data by nano UPLC-MS/MS How the data were acquired • LC-MS/MS was performed on an LTQ Orbitrap Elite mass spectrometer (Thermo Fisher Scientific) coupled to an Ultimate 30 0 0 RSLCnano UPLC system (Thermo Scientific Dionex). • MS was operated in DDA mode, acquiring precursor ions at 120 0 0 0 resolution and fragmenting the top 15 precursors by HCD. • MS/MS data was analysed by de novo sequencing and sequence database searching. Data format The set of spectra obtained was stored in.
RAW. Description of data collection Urediniospores of H. vastatrix were collected from infected Coffea arabica L. plants and spores with a germination rate higher than 80% were used for germination. Data source location • Institution: Embrapa Recursos Genéticos e Biotecnologia • City/Town/Region: Brasília • Country: Brazil Data accessibility Public Repository for Computational Mass Spectrometry -MASSIVE Data identification number: MassIVE MSV0 0 0 087665 Direct URL to data: https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task= cc71ad75f767451abe72dd1ce0019387 Instructions for accessing these data: Web access: https://massive. ucsd. edu/ MassIVE ID = MSV0 0 0 087665 PorteomeXchange ID = PXD026810 DOI = 10. 25345/C5FK0D.
Introduction One of the areas of research on psychedelics that can be used for various pathologies concerns the compounds contained in various Psilocybe-type hallucinogenic mushrooms comprising more than 150 species of which about 80 are referenced as hallucinogens  [1]. Some other mushroom species also contain these products  [2] [3] [4]. The first two indole derivatives identified in 1958 and then synthesized in 1959 by Hofmann and co-workers were psilocybin and psilocin  [5, 6]. This current review was conducted following a search, using SciFinder as a database, on methods of synthesizing psilocin or psilocybin. The psilocybin prodrug is rapidly dephosphorylated in the stomach to generate psilocin, which is the active ingredient; however, the latter exhibits instability, particularly in solution  [7] [8] [9] [10].
| 1D 1 H pure shift 1D Pure Shift 1 H spectra were recorded using the pulse sequence published elsewhere. [32] The duration of the π/2 pulse was 9. 6 μs for 1 H. For the ZS element, a Reburp2 pulse of 90 ms was implemented. The pulsed field gradient aligned with the center of the refocusing shaped pulse for spatial selection purpose was experimentally optimized to 0. 4 G. cm À1. All gradient pulses were followed by a recovery delay of 200 μs. The free induction decay was reconstructed from the acquisition of a series of 50 chunks of 40 ms each. The spectral windows in F1 and F2 were set to 25 Hz and 5,000 Hz respectively. For each chunk, a free induction decay of 8 k points was acquired, with 64 scans and recycle delays between scans of 3 s. Each pure shift experiment took 1h. The resulting pseudo-2D data were processed using the pshift macro to generate the 1D signal.
| 2D 1 H-1 H TOCSY and NOESY experiments The pulse sequences mlevph and noesyph from the Bruker library were used to record TOCSY and NOESY 2D spectra, respectively. In both experiments, a free induction decay of 2 k points was acquired for each of the 80 increments in t 1. TOCSY and NOESY experiments were acquired with 16 scans, 16 and four dummy scans respectively, and a 2-s recycle delay between scans. The spectral window was set to 10 ppm in both dimensions. A 80-ms MLEV mixing period flanked by 2. 5-ms spin-lock periods was used for the TOCSY experiment, and a 300-ms mixing time was used for the NOESY. Data were processed by using zero-filling up to 1 k points in t 1 , and 4 k points in t 2 , apodization with square cosine cardinal function, and automatic baseline correction in both dimensions.
Spectra Plot One of the simplest visualization tools is to plot all the spectra, as a function of the average spectrum, using a scatter plot. Figure  4  shows the result of this visualization tool for the three datasets. Figure  4a  clearly shows that the oil spectra are much more similar to one another than those of grapes (Figure  4b ) or flour (Figure  4c ). They differ by a small translation, corresponding to an additive effect not depending on the absorbance level. Figure  4b  shows a very large variability between the spectra of grapes. All spectra are contained within a cone with the vertex at (0,0). This is characteristic of a pure multiplicative effect, due here to differences in the size of the measured berries. Figure  4c  shows that the flour spectra are affected by isolated erroneous measurements, which dilate the vertical scale (spikes).
PCA Another visualization tool is principal component analysis (PCA). It allows, by examining the scores, observation of the variability between the spectra. Examining the loadings also allows us to understand the source of the observed variability and to infer hypotheses about the presence of multiplicative or additive effects, and, thus, to guide the preprocessing step. Figure  5  shows the scores along the first two components of a PCA performed on the three sets of spectra. Figure  6  shows the corresponding loadings resulting from these PCA, together with the mean spectra of the three datasets. The scores of the grapes spectra (Figure  5b ) show, from another point of view, the cone structure already observed in Figure  4b. In addition, the loadings on the PC1 axis (Figure  6b ) are very similar to the mean spectrum (Figure  6e ).
Both observations are characteristic of a multiplicative effect. The reason for this is illustrated in Figure  7. It represents the spectra in the multi-dimensional wavelength space. When the spectra are impacted by a dominant multiplicative effect, they are organized in a very fine conical structure, passing through the origin of the space. When the data are centered, the canonical co-ordinate system is moved to the center of mass, at the end of the mean spectrum (x). Then, the axis of greatest inertia (PC1) is calculated and, due to the shape of the spectra set, this axis is aligned with the mean spectrum. The score plot and the loadings of the flour spectra (Figure  6c, f ) reveal that the presence of the spikes dominates. As a consequence, this dataset must be cleaned from these spikes before any other processing.
Both observations are characteristic of a multiplicative effect. The reason for this is illustrated in Figure  7. It represents the spectra in the multi-dimensional wavelength space. When the spectra are impacted by a dominant multiplicative effect, they are organized in a very fine conical structure, passing through the origin of the space. When the data are centered, the canonical co-ordinate system is moved to the center of mass, at the end of the mean spectrum ( ). Then, the axis of greatest inertia (PC1) is calculated and, due to the shape of the spectra set, this axis is aligned with the mean spectrum. The score plot and the loadings of the flour spectra (Figure  6c, f ) reveal that the presence of the spikes dominates. As a consequence, this dataset must be cleaned from these spikes before any other processing.
Median Filter A special case of noise is in the form of peaks affecting a single wavelength, as in the example of flour spectra (Figure  3c ). These peaks can originate from dead pixels in hyperspectral cameras. The elimination of such noise cannot be conducted using classical methods, such as moving averages. Instead, a median filter is used, which simply consists of replacing the value of the point i of the spectrum by the median of its neighbors. Figure  9a  shows one spectrum of the flour database. We notice an intense spike around 2050 nm. Figure  9b  shows the result of a moving average filtering on a window of five points. We can see that the filtering did not work because the noise is not zero mean. Figure  9c  shows the result with a moving median filtering. The result is much better.
Compared with the raw oil spectra (Figure  3a ), t detrended oil spectra have been cleaned from their inter-sample baseline variability. A ready, by fitting a 0-order baseline (Figure  10a ), we see that the spectra match perfect except after 1900 nm. This region, attributed to combinations of fundamental vibratio of C-H bonds, was identified by the authors of  [7]  as one of the most discriminating in t dataset. It can be seen that, with higher polynomial orders (Figure  10d ,g), the parts wi out absorptions, before 1200 nm and between 1300 and 1400 nm, are no longer horizont.
Compared with the raw oil spectra (Figure  3a ), the detrended oil spectra have been cleaned from their inter-sample baseline variability. Already, by fitting a 0-order baseline (Figure  10a ), we see that the spectra match perfectly, except after 1900 nm. This region, attributed to combinations of fundamental vibrations of C-H bonds, was identified by the authors of  [7]  as one of the most discriminating in the dataset. It can be seen that, with higher polynomial orders (Figure  10d ,g), the parts without absorptions, before 1200 nm and between 1300 and 1400 nm, are no longer horizontal. In conclusion, for the oil spectra, a detrend of order 0 allows a very satisfactory shape to be obtained for the spectra. The application of detrending to the grape spectra (Figure  10b, e, h ) does not give a good result.
42, À10. 41 and À10. 01 ppm. The mass spectrum shows a molecular ion peak at m/z 698. 0799. The 31 P{ 1 H} NMR shows the presence of no 31 P chemical shi. All the spectroscopic data along with mass spectrometric data suggest 4 as a mixture of two RuÀborate species. However, the identity was unclear until an X-ray crystallographic analysis was carried out for one of them. A slow evaporation of a CH 2 Cl 2 /hexane solution of 4 at À5 C yielded two different types of crystals. The X-ray diffraction analysis was performed on a yellow crystal which was manually picked up from the Schlenk tube. The solid-state X-ray structure of this yellow crystal, shown in Fig. 5 , shows a distorted-octahedral geometry in which four hydrogen atoms are placed in a square plane and two S atoms occupy the axial positions.
Acknowledgements This research was funded by  CEFIPRA , grant number  5905-1. DST-FIST, India. M. Z. thanks  IIT Madras  for a research fellowship. A. A. thanks the  Council of Scientic & Industrial Research (CSIR)  for a research fellowship. S. S. thanks  INSPIRE  for a research fellowship. R. R. thanks the  University Grant Commission (UGC), India , for the research fellowship. Notes and references 1 (a)  R. Peters ,  Cooperative Catalysis ,  Wiley-VCH , Weinheim, 2015; (b) L. Omann,  C. D. F. Konigs ,  H. F. T. Klare  and  M. Oestreich ,  Acc. Chem. Res. , 2017, 50,  1258-1269 ; (c)  K. -S. Feichtner  and  V. H. Gessner ,  Chem. Commun. , 2018,  54 ,  6540-6553.
Data availability Crystallographic data have been deposited with the Cambridge Crystallographic Data Center as supplementary publication no CCDC-1875697 (mer-2a), CCDC-1984218 (fac-3a), CCDC-2126931 (fac-3c), CCDC-2040802 (trans-4). These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via https://www. ccdc. cam. ac. uk/data_request/cif.
Details are available in the Supplementary Methods (Supplementary Figs. 1, 2  and Supplementary Tables  7 8 9 10 ). as a supporting electrolyte. Data were collected using a BioLogic SP-300 potentiostat connected to a three-electrodes system, including a glassy carbon disk (d = 1 mm) working electrode, a platinum wire counter electrode, and an Ag/AgCl reference electrode. The voltammograms of each compound were recorded at different scan rates (from 20 mV/s to 20 V/s). The linearity of the oxidation current with the square root of the scan rates was checked for the four compounds in the different experimental cases. Potential calibration was performed at the end of each data collection cycle using the ferrocene/ferrocenium couple as an internal standard (E°= 0. 380 V/SCE). More details are available in the Supplementary Methods (Supplementary Figs.
Acknowledgements This work was supported by the  Swiss National Science Foundation  (project  200021L_182078 ) and the  ANR  (project  ANR-18-CE93-0004 ) under the  Lead Agency and International Co-Investigator Scheme. V. M. and J. -M. M. thank the  Labex ARCANE  ( ANR-11-LABX-0003 ) and  CBH-EUR-GS  ( ANR-17-EURE-0003 ) for partial funding.
Data availability The X-ray crystallographic coordinates for structures reported in this Article have been deposited at the Cambridge Crystallographic Data Centre (CCDC), under deposition number CCDC 2111680 (3a), CCDC 2111681 (α-8g), CCDC 2111682 (E-9), and CCDC 2111684  (11). These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/cif. The cif files for 3a, α-8g, 9 and 11 are also available as Supplementary Data 1-4. DFT geometryoptimized coordinates are available in the Supplementary Data 5. Any other relevant data are available from the authors upon reasonable request.
To support suspect screening, the (neutral) monoisotopic masses and molecular formulae are included in many of the lists. This information, along with several other predicted values, is also included in the merged NORMAN SusDat. Several other fields may be present, depending on the context of the suspect list, and are included where available. More details on the chemical structure identifiers and recommended chemical structural data templates are provided elsewhere  [24, 37]. The suspect lists (commonly submitted via email to NORMAN contact points, see Fig. 2 , top left) are processed upon submission, with the subsequent processing steps highly dependent on both the type of submission and the size of the list. While the suspect list number is assigned sequentially, the abbreviation, name and description are assigned following pre-defined conventions, and in discussion with authors.
All lists (except S18 TSCASURF, for which no InChIKeys exist) are listed in numerical order in the PubChem NORMAN-SLE Tree. In addition, certain lists with detailed classification content appear again at the top of the browser. These are mapped via structural information in the CSVs (not the InChIKey files) to profit from the detailed additional information available in these lists. The PubChem Classification Browser can also be accessed programmatically (i. e. , in an automated manner), with documentation available on PubChem [67] and the ECI GitLab pages  [76]. The PubChem NORMAN-SLE Tree also enables users to download individual lists (or even various combinations thereof via advanced queries) in the variety of formats offered by PubChem, including the structure data format (SDF) not currently offered on the NORMAN-SLE website, see documentation available in e.
g. ,  [77]. PubChem has also integrated several categories of annotation content, i. e. , detailed information about individual chemicals, into the compound records in PubChem. As of 30 April 2022, a total of 17 annotation categories, which equate to headers in the Table of Contents entries in PubChem  [78] , were integrated. Many relate to the chemical role or use (e. g. , the Anatomical Therapeutic Chemical (ATC) Code for pharmaceuticals, Agrochemical Category, Chemical Classes, Use Classifications and Uses) and transformation information (e. g. , included in the Transformations, Metabolism/Metabolites, Drug Transformations and Agrochemical Transformations headers). Others relate to chemical information (e. g. , molecular formula) and measurement data, such as nuclear magnetic resonance (NMR- 13 C, 19 F, 1 H, and 31 P), tandem MS (MS/MS) data and collision cross section (CCS) data from ion mobility experiments.
Detailed classification content for S77 FCCDB  [89]  is already drafted on the test site. A screenshot of the top portion of the PubChem NORMAN-SLE Tree is shown on the left in Fig. 5. The collision cross section (CCS) content (S50 CCSCOMPEND  [121] , S61 UJICCSLIB  [150]  and S79 UACCSCEC  [187] ) has also been merged and extended in the "Aggregated CCS Classification" tree on PubChem to combine this with the data from CCSbase  [249, 250]  and to allow browsing by adduct categories across all datasets  [251]. All datasets mentioned here can be accessed via hyperlinks available at the NORMAN-SLE Data Source page on PubChem  [73]. Documentation   [190, 191] ; S30 PHENANTIOX 209  [192] ; S24 HUMANNEUROTOX 190  [56, 59] ; S56 UOATARGPHARMA 185  [193] [194] [195] ; S28 EUBIOCIDES 160  [196] ; S5 KWRSJERPS 159  [99, 197] ; S12 NORMANEWS 156  [198, 199] ; S26 MYCOTOXINS 149  [200] ; S74 REFTPS 146  [71] ; S64 NATOXAQ 130  [201, 202] ; S91 CECTOYS 126  [203, 204] ; S68 HSDBTPS 101  [174, 205] ; S86 TATTOOINK 98  [206] [207] [208] ; S41 CCL4 96  [209, 210] ; S83 CCL5 96  [211, 212] ; S52 THSMOKE 95  [213]  (F) < 95 S95 PFASANEXCH 94  [214, 215] ; S63 UBADWGW 84  [176, 216] ; S59 NPINSECT 83  [217] ; S67 TBUTYLPHENOLS 77  [218] ; S97 UBABPAALT 71  [219, 220] ; S88 UBABIOCIDES 62  [221] [222] [223] ; S93 CECMOUTHING 60  [203, 204] ; S40 ALGALTOX 54  [224] ; S20 BISPHENOLS 52  [225, 226] ; S81 THSTPS 52  [227] ; S45 SYNTHCANNAB 39  [63] ; S90 ZEROPMBOX1 38  [228, 229] ; S44 STATINS 18  [230] ; S98 TIRECHEM 16  [231]  Table  2  Selected overall summary statistics for the NORMAN-SLE, compiled in May 2022 Further details are given in the "stats" subfolder of the ECI NORMAN-SLE GitLab repository  [234].
Acknowledgements The authors wish to acknowledge all contributors to the NORMAN-SLE and to the information behind the NORMAN-SLE who are not otherwise mentioned in this article. All authors thank those who contributed to all the open software and web services used in this study that have underpinned these efforts. We gratefully acknowledge the contributions of those we could no longer contact and/or who made contributions without our explicit knowledge. Specifically, the authors wish to acknowledge Anca Baesu (McGill University, Canada, S74), Barbara Günthardt (formerly Eawag/Agroscope, S29), Jan Oltmanns (Forschungs-und Beratungsinstitut Gefahrstoffe GmbH (FoBiG), Germany) and Rosa Sjerps (Oasen, Netherlands, S5, S27) who were all approached to be authors and preferred to be acknowledged, along with Robert Mistrik (HighChem, Slovakia, S19) who was approached to be authors but did not respond.