dataesr/scientific-paragraphs-categorization huggingface dataset:
Within a MSA, SDPs are amino acid positions that show a pattern of conservation in agreement with subfamily divergence. Two types of SDPs can be distinguished: a Type I corresponds to diverse amino acids in one group and a conserved one in the other(s) reflecting different levels of functional constraints between duplicated genes, whereas Type II positions are characterized by different conserved amino acids among groups associated to divergent constraints  [82, 83]. SDP prediction between the three vertebrate ST3Gal groups (GR1-GR3) led to the identification of five SDP, namely S197, Y233, V234, W304 and N307 in the reference porcine ST3Gal I structure (PDB: 2WNB) that are located in the active site  [53]. These SDPs in close contact to the ST3Gal substrates are indicative of the functional divergence of each group of ST3Gal sequences in early vertebrates.
As illustrated in Figure  9 , SDP prediction between ST6Gal I and ST6Gal II was carried out using SPEER server  [82, 84]. Six SDPs corresponding to positions 95, 122, 169, 357, 359 and 380 in the reference sequence human ST6Gal I (PDB: 4js2)  [81]  localized at the protein surface and could reflect protein-protein interaction evolution. Two type II SDPs corresponding to L326 and F346 were found in the sialylmotif S, which is involved in acceptor and donor binding  [85]  and three others V352, Q357 and F359 were found in the mobile loop nearby sialylmotif III (Figure  9A, B ). Interestingly, the highest scored SDP prediction Within a MSA, SDPs are amino acid positions that show a pattern of conservation in agreement with subfamily divergence. Two types of SDPs can be distinguished: a Type I corresponds to diverse amino acids in one group and a conserved one in the other(s) reflecting different levels of functional constraints between duplicated genes, whereas Type II positions are characterized by different conserved amino acids among groups associated to divergent constraints  [82, 83].
SDP prediction between the three vertebrate ST3Gal groups (GR1-GR3) led to the identification of five SDP, namely S197, Y233, V234, W304 and N307 in the reference porcine ST3Gal I structure (PDB: 2WNB) that are located in the active site  [53]. These SDPs in close contact to the ST3Gal substrates are indicative of the functional divergence of each group of ST3Gal sequences in early vertebrates. As illustrated in Figure  9 , SDP prediction between ST6Gal I and ST6Gal II was carried out using SPEER server  [82, 84]. Six SDPs corresponding to positions 95, 122, 169, 357, 359 and 380 in the reference sequence human ST6Gal I (PDB: 4js2)  [81]  localized at the protein surface and could reflect protein-protein interaction evolution. Two type II SDPs corresponding to L326 and F346 were found in the sialylmotif S, which is involved in acceptor and donor binding  [85]  and three others V352, Q357 and F359 were found in the mobile loop nearby sialylmotif III (Figure  9A, B ).
Conclusion This review has reported an overview of the evolutionary history of the β-galactoside α2,3/6-sialyltransferases. The human/mouse ST3Gal and ST6Gal families are comprised of eight members with low overall sequence similarities except for the conserved sialylmotif and family motifs in the catalytic domain that are hallmarks for homologs identification in databases. Genetics, molecular phylogeny and functional genomics approaches have been used to decipher their evolutionary relationships in the context of the dynamic remodeling of genome content (gene loss/gain, segmental and whole genome duplication events). Interestingly, the st3gal and st6gal genes could be identified in the sponge O. carmela suggesting their ancient occurrence in the metazoans and their expansion in deuterostome lineages.
Figure5. Evolutionary rates of β-galactoside α2,3/6-sialyltransferase subfamilies in Vertebrates. (A) Trees obtained from Minimum Evolution and JTT model implemented in MEGA 6. 0 [69]  using 91 ST3Gal and 27 ST6Gal sequences allowed calculation of the evolutionary rates of β-galactoside α2,3/6-sialyltransferase subfamilies for the major divisions in Vertebrates (Data S2). For Amniotes, 2 or 3 sequences were taken, including at least Man and a Marsupial in Mammals, Chicken and Ostrich in Avians, and Caroline Anole and Burmese Python in Lepidosaurians. Orange background denotes the highest values; (B) Mean evolutionary rates in the different β-galactoside α2,3/6-sialyltransferase subfamilies (˘standard error). The mean evolutionary rates of ST3Gal and ST6Gal subfamilies were calculated from Teleosteans to Amniotes.
Acknowledgments: This work has been funded by the  Centre National de La Recherche Scientifique (CNRS) , by the  University of Lille Nord de France  (PPF bioinformatique of the  Lille1 University ), by the  ANR-2010-BLAN-120401  grant from the  Agence Nationale de la Recherche , by the Ligue régionale de la recherche contre le cancer (2016).
We present bulk-sensitive hard x-ray photoelectron spectroscopy (HAXPES) data of the Ce3d core levels and lifetime-reduced Ledge x-ray absorption spectroscopy (XAS) in the partial fluorescence yield (PFY) mode of the CeMIn 5 family with M = Co, Rh, and Ir. The HAXPES data are analyzed quantitatively with a combination of full multiplet and configuration interaction model which allows correcting for the strong plasmons in the CeMIn 5 HAXPES data, and reliable weights w n of the different f n contributions in the ground state are determined. The CeMIn 5 results are compared to HAXPES data of other heavy fermion compounds and a systematic decrease of the hybridization strength V eff from CePd 3 to CeRh 3 B 2 to CeRu 2 Si 2 is observed, while it is smallest for the three CeMIn 5 compounds. The f -occupation, however, increases in the same sequence and is close to one for the CeMIn 5 family.
Data and simulation Figure  1  shows the uncorrected 3d core level HAXPES data of CeMIn 5 (M = Ir, Rh, and Co), CeRu 2 Si 2 , CeRh 3 B 2 , and CePd 3. All spectra exhibit two sets of emission lines, due to the Ce3d spin orbit splitting. Each set, i. e. the Ce3d 3/2 and the Ce3d 5/2 , contains three spectral weights because of the core hole effect on the mixed ground state. At the bottom of the CePd 3 data the energy positions of the two sets of I( f 0 ), I( f 1 ), and I( f 2 ) are marked. It shows that the expected I( f 0 ) spectral weight at 915 eV binding energy does not overlap with any other spectral feature and that the I( f 2 ) intensity at ≈880 eV overlaps only partially with I( f 1 ). However, there is a multiplet structure underneath I( f 1 ) and I( f 2 ) which has to be accounted for when disentangling the two.
The CeMIn 5 data in Fig. 1  look very much alike. Only the Co data exhibit an extra hump at about 923 eV binding energy which is due to some intensity from the Co2s emission. We now compare qualitatively the ratio of the I( f 2 )/I( f 1 ) intensities for the Ce3d 5/2 emission: the ratio increases systematically from top to bottom, i. e. from the CeMIn 5 family to CePd 3. However the intensity ratio I( f 0 )/I( f 1 ) seemingly does not have the same systematic when comparing the intensities at the I( f 0 ) position at 915 eV. For the CeMIn 5 I( f 0 )/I( f 1 ) seems larger than for CeRu 2 Si 2. This is puzzling since increasing hybridization should lead to an increase of both I( f 2 )/I( f 1 ) and I( f 0 )/I( f 1 ). This puzzle is quickly solved when looking at the In3p emission spectra of the CeMIn 5 compounds at the top of Fig.
2. Strong and relatively sharp intensities in addition to the main In3p 1/2 and In3p 3/2 emission lines are visible at about 13-14 eV higher binding energies, respectively, and they are identified as plasmonic excitations. This is about the energy distance of the I( f 0 ) and I( f 1 ) spectral weights in the Ce3d core level spectra, meaning the I( f 0 ) intensities in the CeMIn 5 data are superimposed by plasmons as already pointed out by the authors of Refs. [33, 36] , leading to the misleading impression that the I( f 0 ) spectral weight in the 115 compounds is larger than in CeRu 2 Si 2. The data analysis comprises 1) the background correction, 2) the plasmon correction, 3) the assignment of spectral weights, and 4) the conversion of final state spectral weights I( f n ) into initial state contributions w n with the CI calculation.
CeFe 2 Al 10 is known to be more strongly intermediate-valent than the T = Ru and Os samples  [53]  and there is indeed a large difference in the spectra. The strongest absorption line at 5726 eV is strongly reduced while the intensity at 5737 eV is enhanced, i. e. the latter must be the I( f 0 ) spectral weight. Also here the band structure calculations reproduce the overall features of the the EXAFS and the similarity of the 5d-DOS of CeFe 2 Al 10 (green line) and CeRu 2 Al 10 (orange line) in Fig. 5(c ) confirms that the difference in the data must indeed be due to the difference in I( f n ) spectral weights. The respective central energy positions of the I( f n ) are marked in Fig. 5(a). In Fig. 5 (b) we show a consistency fit to the PFY data of CeMIn 5. Applying a CI calculation using the same initial state Hamiltonian as for HAXPES yields also good results for the PFY-XAS data when using identical lineshapes for each I( f n ), consisting of three Gaussians, a tanh-type step function with inflection point nearby the maximum of the respective I( f n ) absorption, and an additional Gaussian for mimicking the EX-AFS.
Summary The f -occupation of the CeMIn 5 family has been investigated with bulk-sensitive HAXPES and PFY-XAS and compared with spectra of cerium compounds of different hybridization strengths. The PFY-XAS data show that the f -occupation of the CeMIn 5 compounds is identical within the accuracy of the present experiments. A detailed analysis of the HAXPES data, which includes a quantitative plasmon correction, shows further that the effective hybridization V eff and also the amount of f 0 in the ground state in the CeMIn 5 is even smaller than in the non-magnetically ordering compound CeRu 2 Si 2.
Figure1shows the uncorrected 3d core level HAXPES data of CeMIn 5 (M = Ir, Rh, and Co), CeRu 2 Si 2 , CeRh 3 B 2 , and CePd 3. All spectra exhibit two sets of emission lines, due to the Ce3d spin orbit splitting. Each set, i. e. the Ce3d 3/2 and the Ce3d 5/2 , contains three spectral weights because of the core hole effect on the mixed ground state. At the bottom of the CePd 3 data the energy positions of the two sets of I( f 0 ), I( f 1 ), and I( f 2 ) are marked. It shows that the expected I( f 0 ) spectral weight at 915 eV binding energy does not overlap with any other spectral feature and that the I( f 2 ) intensity at ≈880 eV overlaps only partially with I( f 1 ). However, there is a multiplet structure underneath I( f 1 ) and I( f 2 ) which has to be accounted for when disentangling the two.
The CeMIn 5 data in Fig. 1look very much alike. Only the Co data exhibit an extra hump at about 923 eV binding energy which is due to some intensity from the Co2s emission. We now compare qualitatively the ratio of the I( f 2 )/I( f 1 ) intensities for the Ce3d 5/2 emission: the ratio increases systematically from top to bottom, i. e. from the CeMIn 5 family to CePd 3. However the intensity ratio I( f 0 )/I( f 1 ) seemingly does not have the same systematic when comparing the intensities at the I( f 0 ) position at 915 eV. For the CeMIn 5 I( f 0 )/I( f 1 ) seems larger than for CeRu 2 Si 2. This is puzzling since increasing hybridization should lead to an increase of both I( f 2 )/I( f 1 ) and I( f 0 )/I( f 1 ). This puzzle is quickly solved when looking at the In3p emission spectra of the CeMIn 5 compounds at the top of Fig.
Acknowledgments For M. S. , F. S. and A. S. this work was supported by  DFG  through project  600575. We thank  S. Wirth  for fruitful discussions and reading the manuscript. The PFY experiment was performed under the approvals with  Japan Synchrotron Radiation Research Institute  (Proposal No. 2011A4254 ,  2012A4252 ,  2012B4251 ) and  National Synchrotron Radiation Research Center, Taiwan  ( 2011-2-053 ,  2012-3-078 ). Work at Los Alamos was performed under the auspices of the  U. S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering.
Acknowledgements This work was partly supported by the  National Research Foundation of Korea(NRF)  grant funded by the  Korea government (MSIP)  (Nos. 2R32-2008-000-10155-0  ( WCU ),  2010-0027136 , and  014M2B2B1071110 ). Partial support via the  French-Japanese LIA for Nuclear Structure Problems  is also acknowledged.
3. 2. Instruments. NMR spectra were recorded with a VAR-IAN Unity Inova 500 MHz ( 13 C: 125. 9, 1 H: 500. 6 MHz) or a VARIAN Gemini 200 MHz ( 31 P: 80. 9 MHz) spectrometer in CD 3 OD. Chemical shifts are reported in parts per million (ppm) on the  scale. The residual solvent peak (CD 3 OH) was used as a reference for 1 H NMR (3. 31 ppm) and 13    NMR (49. 0 ppm). 31 P NMR spectra were recorded using phosphoric acid (85%) as the external reference. Data are reported as follows: s = singlet, d = doublet, t = triplet, q = quartet, dt = doublet of triplet, and m = multiplet. Coupling constants are given in Hz. The FTIR spectra were recorded using a Nicolet FTIR 380 spectrophotometer and measured in wavenumbers (cm -1 ). The mass spectra were run on a MALDI-TOF mass spectrometer (Biflex IV, Bruker Daltonique) with 2,5-dihydroxybenzoic acid as matrix.
X-ray Diffraction Structure Analysis of 4a A colorless crystal of 4a has been mounted on a Apex II diffractometer (Bruker Nonius Kappa, Karlsruhe, Germany) and the intensity data have been collected at 115 K with Mo Kα radiation of λ = 0. 71073 Å. These data were further treated with Denzo and Scalpack programs  [54]. The model of the structure has been solved by direct methods with SIR92  [55]  and refined with SHELXL-97  [56]. All non-hydrogen atoms were refined with anisotropic temperature factors. All H atoms were placed in calculated positions and refined as riding on the heavy atoms bearing them. The resolution of the structure revealed the presence of two enantiomer molecules in the asymmetric unit of P2 1 /c space group. Crystallographic data have been deposited with the Cambridge Crystallographic Data Center: deposition number CCDC 1405500 contains detailed crystallographic data for this publication.
Crystallography The data were collected at 150(2) K on a Nonius Kappa-CCD area detector diffractometer  [32]  using graphitemonochromated Mo Ka radiation (l ¼ 0. 71073 Å). The crystals were introduced into glass capillaries with a protective coating of Paratone-N oil (Hampton Research). The unit cell parameters were determined from ten frames, and then refined on all data. The data (combinations of 4and uÀscans with a minimum redundancy of 4 for 90% of the reflections) were processed with HKL2000  [33]. Absorption effects were corrected with SCALEPACK  [33]. The structures were solved either by direct methods with SHELXS-97  [34]  or with SHELXT  [35] , expanded by subsequent difference Fourier synthesis and refined by full-matrix least-squares on F 2 with SHELXL  [34]. All non-hydrogen atoms were refined with anisotropic displacement parameters.
This work has been done in the frame of the  ANR Clustomesogen :  ANR-13-BS07-0003-01  and a  Marie Curie International Incoming Fellowship  within the  7th European Community Framework Program. Both organizations are gratefully acknowledged. The authors also wish to thank  Dr F. Camerel  for DSC access and the  Fonds der Chemischen Industrie (Kekulé fellowship for S. B. )  for generous financial support.
Replacing Tris buffer with phosphate buffer (entry 5) had a negative effect on the yield that dropped to 42%. For comparison, the reactivity of 13 in organic solvents was investigated (entries 6 and 7). A very strong solvent effect was observed. While no formation of 23 occurred in methanol, it was isolated in excellent yield in DMF. The difference in reactivity between 12 and 13 in Tris buffer may be due to the unfavorable formation of the hydrate form of 12. However, these first data confirmed the feasibility of this dipolar cycloaddition in aqueous solutions starting with 4-cyanopyridinium salts 7 and 13. The mixtures of pyridinium salts (1 eq. ) and ethyl propiolate (1. 5 eq. ) were stirred at the chosen temperatures for 18 h. Results are given as yields of isolated indolizines. The mixtures of pyridinium salts (1 eq.
Material and Methods Melting points were determined using a Reichert Thermovar apparatus (Depew, NW, USA) and are uncorrected. NMR spectra were recorded on the Bruker Avance 400 spectrometer (Bruker Corporation, Billerica, MA, USA) of the "Fédération de Recherche" ICMG (FR2607) platform, using the solvent as the internal reference; the chemical shifts are reported in parts per million (ppm) units. High-resolution mass spectra (HRMS) were performed on a Bruker maXis mass spectrometer Q-TOF (Bruker Corporation) by the "Fédération de Recherche" ICOA/CBM (FR2708) platform. Reversed-phase HPLC was performed with a µ-bondapak-C18 analytical column (Waters Corporation, Milford, MA, USA). A Waters chromatographic system was used, with two M-510 pumps and a photodiode array detector Waters 996 using Millenium 32 software.
Acknowledgements The work was funded by the  EPSRC (UK)  research grant: " Elucidate and Separate (ELSEP) -Palladium Catalysts in C-C and C-N Coupling Reactions " (grant number:  EP/G070172/1 ). EMB was supported by  Xunta Galicia , and CJM is supported by an industrial  Doctoral Training Grant  (AstraZeneca). The Diamond light source (UK) and the  Swiss Norwegian Beamline  at the  ESRF (France)  are gratefully acknowledged for access to facilities. We thank  Johnson Matthey  plc for the provision of Pd salts through an academic loan scheme and  Dr Ana Iglesias-Juez  and  Prof. Marcos Fernandez-Garcia  for their advice regarding the preparation of the catalysts used in this work. Ekaterina Wade  ( Imperial College ) is gratefully acknowledge for assistance with the TEM measurements. MAN is extremely grateful to the  Department of Physics, University of Warwick ,  United Kingdom  or a visiting academic position.
8 nm line up to 46 in the case of 1058. 5 nm line. Figure  3  shows a histogram of 32 individual measurements of A hfs value for the 43 249. 3 cm -1 level. Similarly in g J determinations we used 13 recorded Zeeman spectra in the case of 563. 2 nm line and for the line 1058. 5 nm the number of spectra was 46. Figure  4  shows a histogram of 44 measurements of Landé-g J factor for the 52 612. 4 cm -1 level together with the fitted Gaussian curve. In Table  1  the atomic structure data are presented together with statistical errors. Numbers in brackets give errors corrected by the Student's t-distribution coefficients. In the case of levels 18 464. 2, 43 249. 3, 45 945. 3, 49 391. 1 and 58 835. 5 cm -1 the A and g J -constants were obtained with higher reliability as a weighed mean values from observation of more than only one line.
In vivo assays could also be performed directly at-line with miniaturised setups, particularly those involving zebrafish  [19, 20]. This high-resolution biological profiling approach allows the localisation of LC peaks responsible for the activity in the chromatogram. In addition, structural information may be generated on-line or at-line from the LC peak permitting identification of the compounds responsible for the activity by accurate chemical analysis using mass spectrometry (MS) or miniaturised nuclear magnetic resonance (NMR) methods combined with database search (i. e. , dereplication  [18, [21] [22] [23] [24] [25] ). Dereplication, combined with high resolution biological profiling, thus represents a rational and efficient method for the early identification of bioactive compounds  [26].
Thus, spectroscopic information of MS features are highlighted without the need of physical separation. They correspond to NMR signals exhibiting positive correlations with specific MS features. A similar approach is proposed in the present study to overcome coelution problems. Statistical correlations between LC-MS, NMR and/or bioactivity data were evaluated for their help in the deconvolution of profiles obtained from microfractions. This should eventually help a more accurate identification of the active principles in a crude extract by rapid partial fractionation without the need for complete purification  [31]. For that purpose, a mixture of 18 compounds, including a compound known to be bioactive, was microfractionated using semi-preparative HPLC. Each fraction was further profiled by high-throughput ultra-high pressure liquid chromatography (UHPLC) coupled to high-resolution (HR) MS and micro-NMR.
This setup led to microfractions containing Molecules 2016, 21, 259 3 of 13 sufficient amounts of constituents for both HPLC-based activity profiling and further LC-MS and NMR analysis  [26, 27]. This format has already been reported to be compatible with dereplication by either MS or NMR  [26]. The AE was thus submitted to small-scale semi-preparative microfractionation by reversed phase HPLC. This yielded 80 microfractions that were transferred to a deep-well microtiter plate (Figure  1 ). All microfractions were profiled using UHPLC-TOFMS in positive and negative ionization (PI and NI) modes, and 1 H-NMR. On one side, all m/z features of the microfraction LC-MS fingerprints were automatically extracted using MZmine 2 software  [37]  and two data matrices, one for each ionisation mode, were generated.
On the other side, the 1 H-NMR profiles were bucketed to generate a complementary orthogonal data matrix. In this case residual methanol and water signals were removed. The 18-compounds mixture was microfractionated by semi-preparative reversed phase HPLC and each fraction was profiled using UHPLC-TOFMS in positive (PI) and negative (NI) ionization modes, and 1 H-NMR. In addition, each well was evaluated in a cell-based assay for quinone reductase (QR) induction activity.
Microfractionation Combined with Statistical HeterospectroscopY to Improve Compound Identification To deconvolute the NMR signals of compounds not completely separated by chromatography, as often occurs in natural extracts, Statistical HeterospectroscopY (SHY) was used. This method takes advantage of both data sources, highlighting signal from the 1 H-NMR acquisition related to LC-MS features  [28] [29] [30] 32, 33]. In our case, SHY MS-NMR correlations were established between features coming from both 1 H-NMR and LC-MS profiles for each microfraction  [32, 33]. In the case of LC-peak coelution, the ratio of constituents eluting slightly differently, within microfractions should permit their deconvolution without the need for a fully resolved chromatographic separation. In the presented dataset, features observed in LC-MS were thus correlated with 1 H-NMR buckets to highlight chemical shifts related to each specific MS feature.
Untargeted Deconvolution of Biologically Active Compounds Using LC-MS Data In a second step, LC-MS fingerprints were evaluated in an unsupervised manner. Peak detection was performed automatically using MZmine 2 software and all extracted features were taken into consideration, in contrast to the targeted approach discussed above  [37]. All detected peaks were correlated with both biological activities evaluated at 0. 08% concentration using log-and logittransformed data from QR and cell viability assays, respectively  [34]. The results are represented in Figure  4A ,B as correlation scatter plots. This approach allowed features responsible for high QR induction or low cell viability to be rapidly detected. NI and PI showed similar correlation patterns with many features without particular correlation with QR induction and cell viability (Figure  4A, B ).