dataesr/scientific-paragraphs-categorization huggingface dataset:
Okada and Bukry (1980; commercial SPOT software. Additional observations with a Hitachi TM3000 tabletop scanning electron microscope (SEM) were made to verify the preservation state of calcareous nannofossils. Nannofossil taxonomy follows the online Nannotax 3 database (http://ina. tmsoc. org/Nannotax3). The zonal scheme ofOkada and Bukry (1980; CN-CP codes)was used for Cenozoic calcareous nannofossil biostratigraphy (Figures.
Okada and Bukry (1980; commercial SPOT software. Additional observations with a Hitachi TM3000 tabletop scanning electron microscope (SEM) were made to verify the preservation state of calcareous nannofossils. Nannofossil taxonomy follows the online Nannotax 3 database (http://ina. tmsoc. org/Nannotax3). The zonal scheme ofOkada and Bukry (1980; CN-CP codes)was used for Cenozoic calcareous nannofossil biostratigraphy (Figures IODP Proceedings 15 Volume 369.
Purpose: The aim of this study is to generate a metabolic database for biomedical studies of biopsy specimens by high-resolution magic angle spinning (HRMAS) nuclear MR (NMR). Methods: Seventy-six metabolites, classically found in human biopsy samples, were prepared in aqueous solution at a known concentration and analyzed by HRMAS NMR. The spectra were recorded under the same conditions as the ones used for the analysis of biopsy specimens routinely performed in our hospital. Results: For each metabolite, a complete set of NMR spectra (1D 1 H, 1D 1 H-CPMG, 2D J-Resolved, 2D TOCSY, and 2D 1 H-13 C HSQC) was recorded at 500 MHz and 277 K. All spectra were manually assigned using the information contained in the different spectra and existing databases. Experiments to measure the T 1 and the T 2 of the different protons present in the 76 metabolites were also recorded.
In our hospital, samples are transported rapidly by a pneumatic tube from the operating room to the room where the NMR spectrometer is located. ] [8]  HRMAS NMR has the potential to provide important medical benefits; however, metabolite identification and quantification needs to be fast and reliable. Identification is usually based on the literature and existing databases that have been developed mainly for urine, plasma and CSF  9  (HMDB: http:// www. hmdb. ca/). 10,11 A specific HRMAS database for the study of human biological tissues is still lacking and needs to be developed. Moreover, quantification is essential for network analysis and discovery of new biomarkers. The aim of this study is to generate a metabolic HRMAS database for the 76 most common metabolites found in human tissues.
| METHODS 2. 1 | Preparation of metabolite solutions 2. 1. 1 | Pure metabolite standards A total of 76 metabolite solutions were prepared to build the metabolic database. For each metabolite, a solution containing approximately 3 mM of the pure metabolite was prepared in D 2 O containing 9. 4 mM of phosphate-buffered saline (PBS) buffer (pH 7. 4) and 0. 125% trisodium phosphate (TSP). Twenty-four microliters of this solution were placed in a 25-µL disposable insert in KeLF. The number of nmoles actually present in the inserts of each standard solution was determined exactly using the Eretic 12 method (Table  1.
For all metabolites, the following 2D experiments were then recorded: J-Resolved,  13  TOCSY,  14, 15  T A B L E 1 Content of the metabolic database (Continues) | 65 RUHLAND et AL. T A B L E 1 (Continued) (Continues) T A B L E 1 (Continued) (Continues) | 67 RUHLAND et AL. • Group has been defined as cluster 0 in Chenomx.
| HRMAS NMR data processing All spectra of the metabolic database were processed using Topspin 3. 5 (Bruker BioSpin) and calibrated with respect to the TSP signal at 0. 00 ppm in 1 H and at -0. 25 ppm in  13  C. Phosphorus spectra were referenced to the 31 P signal of the PBS (H 2 PO 4 -/HPO 4 2-) buffer at 5. 36 ppm.
| Processing and analysis of 2D relaxation data For both T 1 and T 2 analysis, the free induction decays were first multiplied with a cosine squared weighting function before Fourier transformation. Data were then carefully phased and baseline corrected. The resulting data were analyzed using the Dynamics Center 2. 2. 4 package, which is a module of Topspin 3. 5 and which is able to perform advanced mono-and biexponential curve fitting. This software fits relaxation data using a combination of Simplex and Levenberg-Marquardt algorithms.
| Human brain tumor specimens Six human brain specimens (normal cerebral cortex [n = 2], oligodendroglioma [n = 2], and glioblastoma [n = 2]) were prepared following our standard protocol in order to evaluate the performances of our database for metabolite assignment. Additionally, the T 1 and T 2 relaxation times of 17 brain metabolites (20 chemical groups) were determined in these samples. These relaxation times were measured to confirm the applicability of our database to biological samples.
| RESULTS For each of the 76 metabolites contained in the database, the full set of experiments described in Methods was acquired. The 1D and 2D spectra were manually assigned using the information contained in the different spectra as well as the Human Metabolome Database 9 and simulated NMR spectra. 17 The complete list of 1 H/  13  C chemical shifts and 1 H-1 H J-couplings for the 76 metabolites is presented in Table  1. It also lists the T 1 and T 2 values of the different protons of (1) S Measured = S o 1 -exp - t a T 1 × exp - t b T 2 (2) S Loss = S o 1 -1 -exp - t a T 1 exp - t b T 2 (3) C Factor = 1∕ 1 -exp - t a T 1 × exp - t b T 2 the molecules in the metabolic database. An example of the T 1 /T 2 measurement for the different signals of ascorbate is shown in Figure  1. As expected for small molecules in solution, the values of the T 1 and T 2 are very similar.
The simplified metabolite pathways used in our laboratory is presented in Supporting Information Figure  S1. The analysis of the variations in concentration of the different metabolites is usually performed by multivariate or network analysis. 28 This approach allows to study the up-and downregulation of different pathways in order to find potential biomarkers. In this context, rapid and robust identification as well as reliable quantification are critical. When performing a study by HRMAS NMR on a large cohort of samples, the detailed metabolite assignment is usually performed only on a few samples. For these samples, the combination of 1D and 2D NMR spectra is particularly powerful, and the database presented in this article makes this assignment relatively straightforward. A unique feature of our metabolic database is that it contains all the spectra used classically to study biopsy specimens: 1 H 1D,  Several softwares, like Metabominer  29  and AssureNMR (Bruker BioSpin) can provide a semiautomated identification using J-Resolved, TOCSY, and 1 H-  13  C HSQC spectra.
29 Once the different peaks have been assigned in these samples, the assignment can simply be reported in the 1D spectrum of the remaining samples. Because the conditions for sample preparation and data acquisition follow a strict protocol, there is little variation in peaks position attributed to pH or temperature. Our database was applied to analyze multiple biopsy specimens obtained from tissue banks or from routine surgery. It is clearly a powerful tool that helps provide an accurate and fast metabolic profile. To illustrate this point, Figure  3A  shows a typical example of the use of the database when assigning the 1D 1 H CPMG spectrum of a biopsy sample. The CPMG is the experiment that is used systematically to characterize biopsy specimens. In this example, the assignment of Allocystationine is highlighted.
In case of severe overlap, the assignment can also be confirmed by superimposing the different 2D spectra of the biopsy sample with the corresponding spectra of the database. Figure  3B  illustrates this approach by showing the assignment of Allocystationine in the de 2D J-Resolved spectrum of a human biopsy. This approach allows to assign the different signals contained in a biopsy sample with a very high degree of confidence. For biomedical applications, it is essential to obtain quantitative information on metabolites present in tissues, that is, in the cells, extracellular space, and microenvironment. This information is usually presented as the number of nmoles of metabolites per mg of biopsy material. The reliability of the analysis will depend on the quality of the tissue sample controlled in histopathology (with a preference for the HRMAS sample itself or the mirror sample).
The T 1 and T 2 data presented in this article allow to improve the quantification of metabolites in biopsies by computing, for each chemical group of a given metabolite, a correction factor that takes into account relaxation losses during the CPMG experiment. Under our experimental CPMG conditions, this loss of signal ranges from 11% to 23%. Similar relaxation times measurements have been performed in vivo at 17. 2T in the rat brain to improve quantification of metabolites. 30 A very important feature of our HRMAS database is that it contains spectra recorded on a known quantity of metabolites. This implies that the CPMG spectrum of the database can be used to directly estimate the amounts of metabolites contained in the CPMG spectrum of a biopsy sample. The scaling factor used to match the peaks in the biopsy spectrum can be used to calculate the concentration of that specific metabolite.
Techniques of quantification of metabolites in biopsy specimens are becoming very performant; however, they still need to be improved, notably in terms of speed and automation. In our laboratory, these quantification routines are currently being implemented in the Chenomx and AssureNMR software. This database has the potential to become a reference to identify metabolite peaks in tissues outside our laboratory, provided data are collected under the exact experimental conditions. It should be useful at both 500 and 600 MHz, which are the 2 magnetic fields most commonly used in this type of study.
| CONCLUSION HRMAS NMR metabolomics is becoming an important technique in clinical research and could potentially improve personalized medicine. The database presented in this study considerably simplifies the assignment of signals in the spectra of biopsy specimens. The spectra of the database are all provided along with the number of nmoles used for the standard sample, which makes them useful for quantification applications. Additionally, the loss of magnetization attributed to T 1 and T 2 effects observed during CPMG experiments run under our experimental conditions is reported for metabolites in solution (average loss 16%) and in biopsy specimens (average loss 17%).
I G U R E 3 (A) A 500-MHz 1D 1 H CPMG HRMAS spectrum of a human brain tumor sample (right temporal glioblastoma) and several spectra of the database superimposed on top. The resonances of allocystathionine are highlighted with blue squares. The green squares show the spectral region at 3. 3 ppm, where there is a strong signal overlap that will require additional 2D experiments. (B) A 500-MHz 2D J-resolved HRMAS spectrum of a human brain tumor sample (right temporal glioblastoma) superimposed with the allocystathionine spectrum (the spectrum has been slightly shifted to the right in order to visualize the good agreement between the peaks).
F I G U R E 3 (A) A 500-MHz 1D 1 H CPMG HRMAS spectrum of a human brain tumor sample (right temporal glioblastoma) and several spectra of the database superimposed on top. The resonances of allocystathionine are highlighted with blue squares. The green squares show the spectral region at 3. 3 ppm, where there is a strong signal overlap that will require additional 2D experiments. (B) A 500-MHz 2D J-resolved HRMAS spectrum of a human brain tumor sample (right temporal glioblastoma) superimposed with the allocystathionine spectrum (the spectrum has been slightly shifted to the right in order to visualize the good agreement between the peaks).
A recently proposed tuning method for resistive-inductive (RL) shunts is implemented in a commercial finite element (FE) code (ANSYS ® ). A main result of the paper is therefore the consistent formulation of the tuning method in terms of variables directly available as solutions in any commercial FE code: The two natural frequencies associated with short-and open-circuit (SC and OC) electrodes and a modal charge obtained as the electrical SC reaction force. An alternative method is based on quasi-static solutions with SC and OC electrodes, convenient for both numerical analysis and experiments. The proposed shunt tuning method is suitable for implementation in any commercial FE software supporting electromechanical analysis and ANSYS ® has been used to assess its accuracy for a piezoelectric smart plate benchmark problem.
The present work concerns pure passive vibration control, in which case the attainable damping is governed by the magnitude of the transducer capacitance with the magnitude of the EMCC as a limiting factor in the design of the transducer and the corresponding shunt tuning procedure. In the present work, a recently proposed shunt tuning method, based on the effective EMCC, is implemented in the commercial finite element (FE) code ANSYS ®  [12]. Correction terms, that represent the interaction with non-resonant modes, are consistently derived from the natural frequencies obtained by the three eigenvalue problems associated with piezoelectric SC and OC electrodes and a pure inductive shunt. However, as demonstrated in  [13] , the solution for the pure inductive shunt is only needed when the effective EMCC is almost vanishing because of a very indirect location of the transducer(s).
Therefore, in most practical problems, the tuning formulae only rely on the two natural frequencies from the SC and OC limits and a modal charge that appears as a supplemental reaction force associated with SC electrodes. This tuning procedure is simply and very directly implemented in a commercial FE code. The present tuning method is implemented in ANSYS ® , which supports three-dimensional (3D) coupled analysis of electromechanical structures, previously used for the assessment of both 2D and 3D evaluations of the effective EMCCs  [14]. Furthermore, a full 3D analysis of a CD-ROM drive base with shunted piezoelectric patches has previously been analyzed in ANSYS ®  [15] , while the possibility of exporting system matrices and vectors has been further utilized in  [16]  to determine optimal shunt calibration and in  [17]  for optimal patch positioning by optimization functions written in Matlab.
Presently, the implementation of the new shunt calibration method in ANSYS ® is used to perform full 3D analysis of a benchmark problem concerning a simply supported plate presented in  [6]  with a single pair of piezoceramic patches and subsequently with multiple pairs placed symmetrically with respect to the targeted vibration forms. The aim of the paper is to introduce an adapted calibration procedure that is suitable for use and implementation in commercial FE software and consistently incorporates the effective EMCC to accurately represent the effects from non-resonant modes in a flexible structure.
Constitutive equations The constitutive relations for piezoelectric materials can be written in four different forms, depending on the choice of independent variables. The most commonly implemented form in commercial software, such as ANSYS ® , is the so-called e-form, {T } = [C E ]{S} -[e]{E} (1) {D} = [e] t {S} + [ǫ S ]{E} (2) where superscript t denotes the transpose operation. For a full 3D representation, the six engineering stresses in {T } and three electric displacements in {D} are expressed in terms of the energy conjugated strains in {S} and electric fields in {E} through the SC elastic stiffness matrix [C E ], piezoelectric (stress) coupling coefficients matrix [e] and dielectric blocked constants matrix [ǫ S ]. Details about the coupled constitutive relations in Eqs. (  1 )-(  2 ) can be found in  [14].
From SEDs defined by broadband optical and infrared (IR) photometry and optical prism observations with R ≈ 30, those authors derived SFHs for 20 000 galaxies at 0. 25 < z < 0. 75 and with stellar mass m > 10 10 M. In this article, we make use of the spectral database of the Main Galaxy Sample (MGS) of the Sloan Digital Sky Survey (SDSS) Data Release 12 (DR12), comparing the SFHs of over 400 000 galaxies between two SFH codes, using a total of 7 models of single stellar populations. This allows us to estimate the fraction of VYGs as a function of galaxy stellar mass for each spectral model. We describe our sample in Sect. 2 and explain our methods in Sect. 3. In Sect. 4, we present the fractions of VYGs inferred from the SDSS spectra. These results are summarized and discussed in Sect. 5.
Summary We extracted our sample from the MGS of the SDSS data release 12 (DR12), according to the following criteria: (i) flux limit: r 0 Petro ≤ 17. 77; (ii) object spectra obtained with original 3 arcsec fibre; (iii) redshift range: 0. 005 < z < 0. 12; (iv) the STARLIGHT  (Cid Fernandes et al. 2005 ) SFH code does not fail when applied to the object; (v) object is in the VErsatile SPectral Analysis database (VESPA) 2 of SFHs; (vi) single spectrum for each galaxy; (vii) surface brightness limit: µ 0 r,50 ≤ 23. 0; (viii) stellar mass range: 6 < log(m/M ) < 12. 5 for all spectral models; (ix) reasonable magnitudes: g Petro > 0, r Petro > 0, i Petro > 0; (x) colours are not extreme: -1 < (gi) 0 model < 2. 5 AND -1 < (gi) 0 fibre < 2. 5; (xi) fibre colours are not too different from model colours: |(gi) 0 fibre -(gi) 0 model | < 1; (xii) all 6 spectral fits yield χ 2 > 0; (xiii) redshift is not too large to fail to see passive galaxies: z < z max (m).
In addition, we allowed ourselves to apply the following criteria to the VYG candidates (but not to the parent sample): (xiv) galaxy does not contain an Active Galactic Nucleus 1 According to the NASA Extragalactic Database at http://ned. ipac. caltech. edu/Library/Distances/ 2 The VESPA database is publicly accessible at http://wwwwfau. roe. ac. uk/vespa. MNRAS 000, 1-22 (2017) (AGN, using the curve of  Kauffmann et al. 2003  that conservatively separates AGN from SFGs in the  Baldwin, Phillips & Terlevich 1981, hereafter BPT, diagram) ; (xv) fibre colour is not bluer than the model colour: (gi) 0 fibre > (gi) 0 model.
We added extra requirements based on our stellar population synthesis analysis (see Sect. 3). The STARLIGHT SFH code (see Sect. 3) did not provide SFHs (criterion (iv)) for 842 spectra (2 per cent), leaving us with 423 664 galaxies. The VESPA galaxy sample (see Sect. 3) is limited in surface brightness to µ 0 r,50 < 23. 0 (criterion (v)), which leads to a smaller sample of 406 005 galaxies. We then excluded 200 galaxies that have more than one spectroscopic observation per galaxy (criterion (vi)): after visually inspection, we kept the spectrum that was closest to the galaxy centre. This left us with 405 805 unique galaxies. The µ r,50 < 23. surface brightness limit (criterion (vii)) of the VESPA galaxy sample had been applied to photometry from the 7th data release (DR7) of the SDSS. Since the SDSS photometry is different in DR12, a few (361) galaxies end up with µ 0 r,50 > 23 and are removed, leaving us with 405 444 galaxies.
AGES FROM GALAXY SPECTRA Galaxy SFHs and ages were inferred from the SDSS-DR12 spectra through stellar population synthesis analysis. These spectra have the advantage of fairly high spectral resolution (R ≈ 2000), excellent signal to noise (above ∼20), wide spectral range (from below 3800 Å to 9200 Å) and they are flux calibrated. They allow to estimate the SFH of each galaxy, by fitting the observed spectrum with the predicted spectrum obtained by the combination of single stellar population (SSP) spectra convolved by the SFH. We considered three algorithms to estimate the SFH. We used two non-parametric codes:  STARLIGHT (Cid Fernandes et al. 2005 ), which we ran ourselves, and the VESPA database  (Tojeiro et al. 2009). Both codes fit non-parametric SFHs and metal histories with an internal extinction, considering typically 16 (roughly equally spaced) bins of log age, as well as 4 to 6 bins of metallicity, and a single extinction (see Table  1 ).
This leads to a total of typically 16 × 5 + 1 = 81 free parameters. We also used the method of  Gallazzi et al. (2005) , who analysed the SFHs of the 175 128 galaxies in the 2nd data release (DR2) of the SDSS. Gallazzi et al. adopted a Bayesian approach to derive median likelihood estimates of stellar population properties such as r-band weighted and mass-weighted mean stellar age, stellar metallicity and stellar mass. For each galaxy, observed absorption features were compared with the predictions from a large library of metallicities and SFHs, described by an exponential declining law on top of which random bursts are added, convolved with BC03 SSP models. The probability distribution functions (from which the 16, 50, 84 percentiles are measured) of the light-and mass-weighted mean ages are obtained by marginalising over all the other parameters of the models.
We also ran STARLIGHT on 424 506 SDSS spectra. STARLIGHT uses the Medium resolution Isaac Newton Telescope Library of Empirical Spectra  (MILES, Sánchez-Blázquez et al. 2006) , using the updated version 10. 0  (Vazdekis et al. 2015, hereafter V15)  of the code presented in  Vazdekis et al. (2010). These MILES models were computed with the Kroupa (2001) IMF, and stellar evolution tracks from BaSTI (Bag of Stellar Tracks and Isochrones,  Pietrinferni et al. 2004 Pietrinferni et al. , 2006)). While STARLIGHT was run assuming a screen dust model, VESPA assumed either a mixed slab interstellar dust model  (Charlot & Fall 2000)  or combined it with extra dust around young stars (also from Charlot & Fall). Gallazzi et al. (2005)  used a screen dust, with the λ -0. 7 extinction curve used later by VESPA.
Indeed, the VESPA code does not consider these effects, and for consistency we have chosen to use the same approach with STARLIGHT. Supernova explosions affect massive stars that are short-lived, thus depleting more the stars formed earlier. Similarly, mass loss from stellar evolution takes time, thus decreases more the stars formed earlier. So both type II supernovae and stellar evolution should result in enhanced VYG fractions. Fig. 12  confirms that mass loss does raise the VYG fractions, but never more than by 0. 2 dex for low and intermediate galaxy stellar masses (log (m/M ) ≤ 10). (iv) There is disagreement between different spectral models using the same SFH code (e. g. BC03 vs. M05 with VESPA) and using different SFH codes (e. g. STARLIGHT vs. VESPA). (v) The spectral models may lead to worse fits for lowmass galaxies, which show the highest fractions of VYGs.
ACKNOWLEDGMENTS We thank  Andrea Cattaneo ,  Daniel Kunth ,  Sophia Liannou , and  Polychronis Papaderos  for enlightening discussions and the referee,  Erik Tollerud , for his numerous constructive comments that strengthened this article. M. T. and T. X. T. are grateful to the hospitality of the  Institut d'Astrophysique de Paris , where a large part of this work was performed. G. A. M. acknowledges the  Brazilian CNPq  (grant # 451451/2019-8 ) and the  Universidade Federal do Rio Grande do Sul  for its hospitality. We are grateful to  Roberto Cid Fernandes  for making his STARLIGHT code publicly available and  Rita Tojeiro  for making the VESPA output publicly available. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration.