dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We have built a composite dataset from the ACT and Planck-HFI channel maps and their associated noise maps. This dataset was used to perform an optimised reconstruction and extraction of the tSZ signal. In practice, we have followed two approaches. The first one is source-oriented and hence focused on the detection of tSZ clusters using a Matched Multi-Filter detection method. The second approach is based on the reconstruction of a tSZ y-map using an ILC technique. For either approach, we have shown that the composite PACT dataset benefits from the high resolution of ACT and the large scale modes and large number of frequency channels of Planck. For the tSZ cluster detection, we have shown that we recover all clusters detected in the individual datasets of Planck and ACT. In addition, we detect new tSZ sources some of which associated with bona-fide clusters at redshifts up to 0.
Acknowledgements. This research was performed in the context of the  ISSI international team project  " SZ clusters in the Planck era " ( P. I. N. Aghanim & M. Douspis ). The authors acknowledge partial funding from the  DIM-ACAV  and the  Agence Nationale de la Recherche  under grant  ANR-11-BS56-015. The research leading to these results has received funding from the  European Research Council  under the  H2020 Programme ERC  grant agreement no  695561. JMD acknowledges support from project  AYA2015-64508-P (MINECO/FEDER ,  UE ) funded by the  Ministerio de Economia y Competitividad. DC acknowledges support from the  South African Radio Astronomy Observatory , which is a facility of the  National Research Foundation , an agency of the  Department of Science and Technology. The development of Planck  has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU).
This research has made use of the following databases: the  NED  and IRSA databases, operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the NASA; SIMBAD, operated at  CDS, Strasbourg , France; SZ cluster database (szcluster-db. ias. upsud. fr)operated by Integrated Data and Operation Center (IDOC) operated by  IAS  under contract with  CNES  and  CNRS. This research made use of Astropy, the community-developed core Phyton package.
The synthesis reaction was carried out at 600 rpm stirring speed, in the isothermal mode. This experiment was realized at seven different temperatures  (10-15-20-25-30-40 and 50°C). The Mettler RC1 operating software allows to record the thermal signal (power profile) and to monitor the reaction progress. The final product was then transferred to a separating funnel and separated in two phases. Only the organic phase (which contains the nitrated products) was analyzed. Before the analysis, the organic phase was, first, washed with a bicarbonate solution at 2% w/w, and then with distilled water until a neutral pH was obtained. Methyl stearate 0. 5.
Young star evolution model Using the "PHYVE" code (Protoplanetary disk HYdrodynamical Viscous Evolution) thoroughly detailed in BC14, BCP15 and BCP16, we track the evolution of the mass distribution across the star+disk system: the viscous evolution governs the amount of material that is transferred from the disk to the star. In addition, the infall of mass onto the disk and onto the central star can be derived from Equations 6 and 9. At every time step in the simulation, we interpolate the stellar radius R * , luminosity L * and temperature T eff from tables of precalculated stellar evolutions modeled using the code CESAM detailed in  Morel (1997) ,  Morel & Lebreton (2008)  and  Piau et al. (2011) , as well as  Marques et al. (2013). These tables provide the radius, luminosity, and temperature of a constant-mass star as a function of its age and mass accretion rate.
Acknowledgements  UBD  and DA thank the  Agence Nationale de la Recherche (ANR) ,  Paris, France  (project  MobiDiC ;  ANR-16-CE05-0009 ) for funding. QL and KFAZ gratefully acknowledge nancial support by the  UNSW   Internal Research Grant  program as well as the  Office of Naval Research  (Award No:  ONRG -NICOP -N62909-16-1-2155 ). QL and KFAZ appreciate the use of instruments in the  Mark Wainwright Analytical Centre at UNSW  as well as equipment funded by the  Australian Research Council (ARC).
bound by dendrimers. In contrast, A1M could bind ˜5 molecules of cationic and neutral PAMAM G4 dendrimers, and was unable to bind anionic PAMAM G4 dendrimer. These results are in close agreement with our previous data on the interaction between HSA and cationic, neutral and anionic PAMAM G4 dendrimers  [20, [32] [33] [34] [35]. HSA could also bind 5-6 molecules of cationic and neutral PAMAM G4 dendrimers, whereas its interaction with anionic PAMAM G4 dendrimer was very weak. To check the effect of dendrimers on immunoreactivity of proteins, based on results of zeta-potential we chose 2 uniform molar ratios -1:1 and 1:10. At 1:1 the protein-dendrimer complex is negatively charged as its charge is determined mainly by protein net charge. At 1:10 (protein to dendrimer) the protein is fully surrounded by dendrimers and it surface potential is determined by positively charged dendrimers.
To evaluate the immunoreactivity of the solid-phase protein, a conjugate of anti-albumin antibody with peroxidase was used. In the direct competitive ELISA, antibodies to albumin were immobilized in the wells of the microplate, and a competitive interaction of human albumin in the test and calibration samples and the albumin-peroxidase conjugate for binding to antibodies was measured during the assay. In the indirect competitive immunoassay, human albumin was immobilized on the wells of the microplate and, in the assay, a competitive distribution of antibodies to albumin conjugated with peroxidase, between albumin in the test and calibration samples and solid phase albumin occurs. The experimental data (Table  1 ) clearly show that inhibition of albumin immunoreactivity occurs when interacting with the modified phosphoric dendrimers being used.
From the experimental data (Table  2 ), either dendrimers or natural ligands (T4 and E2) had no significant effect on immunoreactivity of adsorbed A1M on a solid phase. Dendrimers, after complexing with the protein, partially prevent the recognition of A1M by antibodies, and A1M immunoreactivities at its equimolar complexes were in the range of 93. 3-94% or 83. 4-85. 6%, as determined by the ELISAs with MAb G6 or MAb F9, respectively. As in the case of albumin, a 10-fold excess of dendrimers during the interaction in solution significantly reduced (48-57%) A1M immunoreactivity (immobilized complexes). Also, dendrimer AE2G4 was a stronger inhibitor on A1M compared to AE2G3. In contrast to dendrimers, the interaction of A1M with natural ligands T4 and E2 does not occur on the surface of the protein globule, but mainly in the hydrophobic cavity of the ligand-binding site of this lipocalin.
Acknowledgements This work was supported by the  Belarusian Republican Foundation for Fundamental Research , grants  B18TYUB-001 ,  B18PLShG-004 , by the  Polish National Agency for Academic Exchange , grant  EUROPARTNER , No. PPI/APM/2018/1/00007/U/001 , by grant of the President of the Russian Federation No. MK-2278. 2019. 4. Thanks are due to  CNRS (France)  for financial support (J-P. M. ). This work (S. M. ) was supported by  FCT-Fundaçao para a Ciencia e a Tecnologia  (project  Pest-OE/QUI/UI0674/2013 , CQM,  Portuguese Government  funds), and  Associaçao Regional para o Desenvolvimento da Investigaçao Tecnologia e Inovaçao (ARDITI)  through the project  M1420-01-0145-FEDER-000005  - Centro de Quıimica da Madeira -CQM+ (Madeira 14-20 Program ).
Enzymatic assay (Enzyme-coupled) Determination of enzymatic glycosylation for thiohydroximates PATH, PPTH and hydroximates PAH, PPH were done using a tri-enzymatic assay that couples UDP formation with NADH consumption, using pyruvate kinase and lactate dehydrogenase as enzymatic mixture. Reactions conditions were identical to those reported previously  [25]. Kinetics data were analyzed and fitted using Prism 4 (GraphPad).
pKa determination by NMR Thiohydroxamic and hydroxamic acids, and CTP were first characterized by NMR on a Bruker Avance III 600 equipped with a CPTXI-cryoprobe. Then, 1 H chemical shifts of these compounds were monitored for pH in order to determine their pKa in a 90:10 mixture of phosphate buffer pH 7. 3 and methanol (CD3OH). The method consists of making 1D 1 H acquisitions following successive additions of acid or base. Labile protons are not detectable under these experimental conditions (phosphate buffer) because they undergo rapid exchange. The chemical shift of the Hα protons closest to the deprotonation site is the one which is followed by this method. A titration curve was established by following the chemical shift of the chosen proton as a function of the pH. The obtained data were fitted with an asymmetrical sigmoidal function, which gave the value of the pKa.
However, the catalytic turnover kcat could not be determined, as Asp113Ala mutant could not be obtained as a pure enzyme. Maximum velocity Vmax was determined to be 1. 2 and 2. 4 µM. min -1 for each substrate, indicating that Asp113Ala residual activity is not insignificant, considering the low amount of impure enzyme introduced in the reaction mixture. These mutation studies indicated that the acceptor acidity is crucial in UGT74B1 activity, and to assess this hypothesis we determined the pKa of sulfur-(PATH, PPTH) and oxygen-(PAH, PPH) containing acceptors. Concerning pKa values, no recent data were available for thiohydroximates in the literature  [39]. Acidity constants of all four substrates were thus determined by NMR. Protons located on the alpha-carbon (Hα) of the 4 substrates were used as probes for pKa determination (Fig.
Acknowledgments & funding sources The authors thank  G. Jousset  and  A. Duvergé  for technical help in producing and characterizing UGT74B1 wild-type and mutants' activities. PL, RD, MS, AT, SMar, GC and HO thank the  Labex SynOrg  ( ANR-11-LABX-0029 ) for financial support. SMar, GC and HO thank the  Région Normandie, CNRS , the  University  and  INSA of Rouen  for financial support. SMarq and SMarr were funded by  French Ministry of Research.
Premixing of NaOH Pellets (2 mm) with Iron Sulfate Sodium hydroxide conditioned as 2-mm pellets was used for the experimental tests. Iron sulfate monohydrate was chosen as iron salt for the same tests. The reaction of these solids was carried out in a rotary reactor of 2. 5 L without presence of any gas and using an apparatus of premixing step as illustrated in Figure  2. About 170 g of solids with a molar ratio Na/Fe close to 8 (to satisfy Equation (  2 )) were loaded in the reactor and rotated at a speed of 20 rpm. The premixing time was fixed at 30 min. The evolution of temperature during the reaction was recorded and the data are displayed as plots of temperature versus time in Figure  4. in a rotary reactor of 2. 5 L without presence of any gas and using an apparatus of premixing step as illustrated in Figure  2.
Synthesis of Na-Ferrate in Various Fluidized Beds A fluidized bed with a cross section of about 7. 07 cm 2 (Ø = 3 cm) was used for the synthesis of Na-ferrate based on the NaOH-pearls mixed previously with iron sulfate. It was interesting to follow the evolution of the temperature inside the fluidized bed as the reaction progressed; this was performed by placing a thermocouple in the fluidized bed and recording the temperature. About 10 g of prepared mixture were loaded in the FB. Tests were performed at regulated water temperatures varying from 20 °C to 65 °C. The total flow rate of (air + Cl2) and (N2 + Cl2) gaseous mixture was 1800 L/h corresponding to the operational fluidization velocity for the particles in the FB of Ø 3 cm. The data obtained for a water temperature set at 35 °C are plotted in Figure  12a ,b when air + Cl2 and N2 + Cl2 are used, respectively.
Synthesis of Na-Ferrate in Various Fluidized Beds A fluidized bed with a cross section of about 7. 07 cm 2 (Ø = 3 cm) was used for the synthesis of Na-ferrate based on the NaOH-pearls mixed previously with iron sulfate. It was interesting to follow the evolution of the temperature inside the fluidized bed as the reaction progressed; this was performed by placing a thermocouple in the fluidized bed and recording the temperature. About 10 g of prepared mixture were loaded in the FB. Tests were performed at regulated water temperatures varying from 20 • C to 65 • C. The total flow rate of (air + Cl 2 ) and (N 2 + Cl 2 ) gaseous mixture was 1800 L/h corresponding to the operational fluidization velocity for the particles in the FB of Ø 3 cm. The data obtained for a water temperature set at 35 • C are plotted in Figure  12a ,b when air + Cl 2 and N 2 + Cl 2 are used, respectively.
3  In-depth analysis of  1  H and  13  C NMR data of smenospongidine 7 Other nuclei 7. 1 Fluorine: paramagnetic and diamagnetic effects 7. 2 Fluorine and its role in ADME 7. 3  The complex  19  F NMR spectrum of 4,4-diuorinated proline 7. 4  Nitrogen: an underrepresented nucleus in the structural investigation of natural metabolites 7. 5 Nitrogen: chemical shi referencing, accuracy, and precision 7. 6  Nitrogen: NMR structural information encoded in  15  N NMR spectra 7. 7 Phosphorus:  31  P NMR in natural product structural investigations 8 Databases 8. 1 Database introduction 8. 2  The urgent need for spectral repositories and automation support for peer-reviewing of spectral data 8. 3  Databases for dereplication 8. 4 which represent the actual (raw) spectroscopic data from the excited nuclear spins in the NMR experiment ("spin choreography").
Human and machine processing of NMR data 6] [7] [8]  Indeed, computers may calculate all the solutions allowed by a potentially incomplete set of constraints. 2] [13] [14]  The last two heavily rely on the accuracy of the chemical shi prediction, which in turn heavily relies on the quality and amount of known structure assignments available for training algorithms. As a consequence, most automatic spectral interpretation programs rely on large databases of previously assigned spectra; tools such as LSD [http://www. univ-reims. fr/LSD] or CCASA  15  developed by Nuzillard et al. are notable exceptions. Ensuring that these data are correctly assigned is essential to avoid continual propagation of structural errors. Therefore, even with the assistance of cheminformatics, the challenge of peerreviewing published spectral interpretations still remains.
Iterations continue until a steady state is reached, i. e. , no new atom-NMR signal pairs can be assigned. When new data is submitted, the system assigns it and may run a new iteration. Hence, the algorithm builds its own database of assigned spectra without any human intervention. Peak-picking should be implemented as part of this selflearning loop also. Indeed, modied data must be considered a representation of the original. A missing signal because of low signal to noise ratio or an additional signal from a poorly identied impurity are common errors that affect the outcomes of such a system. Although assignment is performed on peak-picked data, automatic peak-picking itself should be seen and implemented as an iterative process that ends when a successful assignment is found. Having brought assignment, prediction and peak-picking into a self-learning loop allowed the demonstration that a program may be conceived to avoid any human assumptions and faithfully generate all the solutions to the assignment problem.
About a decade ago, Nicolaou and Synder  19  showed in a comprehensive study that, in the process of NMR-based structure elucidation, erroneous structures resulted with noticeable frequency and ultimately reected inadequate structure elucidation efforts. Very recently, Wolfgang Robien affirmed this postulate by running the  13  C NMR database CSEARCH against recently published structures. He again was able to show that erroneous assumptions in the structure elucidation process (e. g. , lacking spectral evidence, no 2D methods performed) were leading to incorrect structures. 20.
Rationale 6data repositories Raw NMR data only reaches its maximum potential if it is universally accessible. Unfortunately, chemists have fallen behind the geneticists in the establishment and general acceptance of a universal database. Although, several laudatory efforts have assembled databases, with some described here, the amount of NMR data generated around the world makes the compiling of a single database for each nucleus a growing, and already gargantuan, task, discussed further in the conclusions.
The structure of aldingenin B The initially reported structure of aldingenin B (12), containing a highly unusual intramolecular ketal, was assigned based on extensive analysis of NMR spectral data (COSY, HMQC, HMBC). 71 The reported structure was recently determined to be incorrect by total synthesis of 12  72  An alternate ve-membered hemiacetal structure (13), was proposed based on computational simulations of the 1 H NMR spectrum of both the originally reported structure and the revised proposed structure with comparison to the experimental NMR data for the synthetic material corresponding to the reported structure and the original NMR spectrum of aldingenin B. 73  Inspection of models of the reported structure reveals the H-6-H-5 dihedral angle to be 90 (AE2 ); the expected coupling of such vicinally orthogonal hydrogens is <2 Hz.
101 A multiplet belonging to H-19, critical for the determination of the conguration at the fusion of rings C and D, was reported as a dd 8. 5 and 4. 6 Hz, while the calculated values were 4. 7 and 4. 3 Hz. In the copy of the spectrum in the ESI, † this multiplet does not look like a dd 8. 5 and 4. 6 Hz, but it is virtually impossible to extract any useful information from the picture. In summary, the conguration of ansellone C (  28 ) is either misassigned or the H-19 multiplet is interpreted and reported incorrectly. Raw FID data would have helped to resolve this issue. In general,  13  C NMR spectra are less prone to the problems outlined above, but even there one sees occasional misinterpretation of an impurity signal and typos in transcribed tables of chemical shis are plentiful. For example, a complex diterpenoid, gaditanone, (28) possessing an unprecedented 5/6/4/6fused tetracyclic ring skeleton, was recently isolated and characterized by solution NMR,  102  with its only carbonyl carbon, C-7, assigned the chemical shi value of 206.
15 ppm for C-12 at the g-position from the carbonyl group at C-9 and the d-position from the hydroxyl group on C-15 will be important to determine the methylene chain length between C-9 carbonyl and C-15 hydroxyl groups. Together with these assignments, signal assignments of C-3, C-5, and C-6 for 33, and C-3, C-5, and C-7 for 34 are important to characterize the partial structure of the tetrahydrofuran or tetrahydropyran ring system, respectively. Exact  13  C chemical shi values could be obtained from raw NMR data readily and, e. g. , are useful for direct comparison and to create database queries for the CAST/C NMR system. For acetogenins, 1 H NMR raw data of intact compounds are also useful, but also raw data of MTPA ester derivatives are very important. These are required to determine the absolute conguration of hydroxyl groups and relative congurations of separated chiral centers.
It was determined to bear a lactone moiety and appeared to t well into the biogenetic reasoning for far more complex compound classes such as the panellins or avaglins. 118, 119 Integrated analysis of HR-MS, IR, and NMR data was straightforward and led to the assignment of structure, 35. "Missing" NOE contacts were explained by congurational and spatial considerations. However, when re-investigating the aglalactone  13  C NMR data by means of the CSEARCH database (see also Section 8. 2),  120, 121  it became evident that a single  13  C NMR shi value (a CH element resonating at about 81 ppm) showed a signicant mismatch relative to the calculated value. Hence, a reinvestigation of the structure elucidation process was commenced. An alternative hypothesis was generated and a set of possible regional isomers formulated.
Although NMR data from the original investigation were available at the time of the aglalactone revision, the data set was deemed incomplete, as HMBC data was unavailable. While it was possible to re-isolate the compound, the group of Hofer and Greger experienced further difficulties. In one instance, a collaborative effort was necessary, 126 in another case, only total synthesis was able 127 to correct a structure. Almost two decades aer the structural revision, the correct structure of aglalactone is still not disseminated properly to the scientic community, including in major resources and databases. Notably, if NMR raw data sets would be available and become a routine part of deposited data, it would be straightforward to correlate the structures and (different) structural proposals via their ngerprint NMR spectra, independent of the limitations of spectral gures in publications and their ESI.
Dereplication during fractionation The NMR spectrum of a fraction is a ngerprint of its entire chemical composition and, therefore, never lies about the composition of fractions. While 1 H-NMR has been frequently used in metabolite ngerprinting of NPs, the advent of high eld instruments together with cryoprobes and small volume tubes (3 or 1. 7 mm NMR tubes) have addressed the previous limitation of low sensitivity so that NMR spectra of fractions can be directly analyzed to identify constituents. The deposition of raw data and the associated FID data will allow complete analysis of fractions. The advantages can be two-fold: to easily and quickly identify known compounds within fractions without further isolation, and to identify signals that are not within known compounds in the search for novel molecules.
204 hese examples demonstrate the need to establish a 1 H NMR NPs database of raw data that can be freely accessible in order to focus on novel NPs. Moreover, they exemplify the need for NMR raw data to allow NMR ngerprints to become a universal tool. Typical NMR ngerprints of fractions are shown in Fig. 24  and 25, and can be analyzed using the proposed database of raw les.
However, a thorough analysis of just the 1 H NMR spectrum, enabled by the availability of the raw data, would have revealed any inconsistencies with Ang Li et al. 's interpretation. Indeed, the 1 H NMR chemical shi of C18 methyl of the synthesized compound (87b, 19S) (1. 4 ppm) differed from that reported for natural lanciferine 87a (1. 2 ppm). Furthermore, for the original isolation of 87a, the authors reported the unambiguous assignment of the congurations of all its chiral centers except that of C-19. 214  light of these data, it would seem that Ang Li et al. , actually, did not synthesize 87a but a diastereoisomer, 87b. Continuing interest of Beniddir's group in MIA chemistry led to the development of a spectral database of a cumulative collection of alkaloids, for dereplication purposes.