dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantitation Quantitative information about metabolites constitutes an invaluable asset to obtain a deeper understanding of the biological reactions and processes occurring in an organism, as changes in metabolite concentrations reflect changes in protein concentrations or gene expressions. For this reason, efforts are being made to collect quantitative data and incorporate them in databases, as it was done for the human serum metabolome. 61 Quantitative data also helps toward more robust, discriminant, and predictive statistical models. 106 NMR-and MS-based quantitation methods are well established. 106, 107 NMR quantitation is relatively straightforward and lies on the use of a single internal standard such as TSP (sodium d4-3-(trimethylsilyl)-propionate), provided that data are acquired and processed under proper conditions known as qNMR.
Future Perspectives Public Databases The previous sections clearly showed that metabolite identification relies on comparing experimental spectral data to those present in various databases. With Metabolomics being a rapidly growing field, databases have been expending as well. Common examples are the Human Metabolome Database,  128  the human serum metabolome,  61  METLIN,  129  the Biological Magnetic Resonance Data Bank,  130  LipidMaps,  131  or the Yeast Metabolomics Database. 132 However, as indicated by their names, these databases are either instrument-, organism-, or compound class-specific. Furthermore, highquality spectra are sometimes missing, which limits annotation confidence, or experimental conditions are not reported, which makes the spectral comparison more difficult and less trustworthy.
Therefore, such databases do not always make good use of the MS/NMR complementarity. To overcome this issue, MetaboLights, the first metabolomics open-access repository regardless of the species or the analytical technique used, was released in 2012. 133, 134 It aimed to offer a repository where compound structure, spectral reference, biological concentration, location, and role as well as raw data could be submitted, stored, shared with the metabolomics community, and reused. However, automatic reporting of metabolomics data is not always performed by the community, although it has improved in the past couple of years. 135 Undoubtedly, this has been helped by initiatives such as COSMOS (Coordination of Standards in MetabOlomicS), which aimed to promote data reporting according to specific standards, in order to facilitate data exchange and dissemination.
140 Omics integration is thus already attracting attention in a number of different fields, either to have more insight into the human health risks linked to environmental chemical exposition,  141  ecological interactions through chemical signals,  142  plant biology through integration of transcriptome and metabolome data sets,  143  or many others. Some examples cited in this review did actually combine transcriptomics and NMR-and MS-based metabolomics to study tomato fruit composition and used correlation analyses to highlight associations between genes and metabolites, to finally visualize them in a common network. 76 Proteome and metabolome data sets were also combined in the same aim of studying tomato fruit composition. 144 This combination successfully promoted biomarkers discovery in diseases with a case study based on encephalomyelitis  145  and promises further applications to toxicological studies following a recent published workflow.
Conclusion In this review, various ways to combine NMR spectroscopy and MS technologies for metabolomics have been discussed, from hardware combination to computational tools, highlighting how it made it possible to access crucial information at the postmetabolomics level. It seems clear that metabolomics is a growing and dynamic field which will greatly benefit from NMR and MS combination, in particular from the computational point of view. Indeed, hardware combination seems a bit outdated when it comes to metabolomics but will probably remain relevant in natural product research. In terms of computational combination of NMR and MS-based metabolomics, the solutions to do so are clearly multiplying, and the massive improvements which have been made in the recent years guarantee that the use of correlation and data fusion approaches will spread.
Phase 2 Participants in this phase were recruited from among the 1025 members registered with the IAHPC as of April 2018. Excluding undergraduates, 1014 members were eligible for the survey. The IAHPC members were stratified by their respective countries' socioeconomic level according to the World Bank classification (high income, upper middle, lower middle, and low income). 29 From each country income-level group, 150 members were randomly selected. The selected members received an electronic mail invitation between June and July 2018 to participate in the survey. The IAHPC members who responded to the invitation received a link to an online survey. Participants were assured of confidentiality and privacy. The respondents' data from the survey were stored in a SurveyMonkey password-protected account.
This study describes the review on candidate biomarkers of food intake (BFIs) for leafy, bulb, and stem vegetables, which was conducted within PubMed, Scopus and Web of Science for studies published through March 2019. Results: In total, 65 full-text articles were assessed for eligibility for leafy vegetables, and 6 full-text articles were screened for bulb and stem vegetables. Putative BFIs were identified for spinach, lettuce, endive, asparagus, artichoke, and celery, but not for rocket salad. However, after critical evaluation through a validation scheme developed by the FoodBAll consortium, none of the putative biomarkers appeared to be a promising BFI. The food chemistry data indicate that some candidate BFIs may be revealed by further studies. Conclusion: Future randomized controlled feeding studies combined with observational studies, applying a nontargeted metabolomics approach, are needed in order to identify valuable BFIs for the intake of leafy, bulb, and stem vegetables.
Literature search to identify putative BFIs To identify relevant studies on leafy vegetables and bulb and stem vegetables, an extensive literature search was conducted following the Biomarker of Food Intake Reviews (BFIRev) methodology as proposed previously  [25]  built on the PRISMA statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses). Briefly, a primary search was performed in the three databases Scopus, PubMed central, and Web of Science with the name of the specific vegetables and its botanical genus, i. e, (lettuce OR lactuca sativa OR escarole OR frisée), (spinach OR spinacia oleracea), (endive OR cichorium endivia), (garden rocket OR eruca sativa OR rucola OR rocket salad OR arugula OR rucoli OR rugula OR colewort roquette), (asparagus OR asparagus officinalis), (artichoke OR cynara cardunculus OR cynara scolymus), (celery OR apium graveolens) along with the common keywords: AND (urine OR plasma OR serum OR excretion OR blood) AND (human* OR men OR women OR patient* OR volunteer* OR participant*) AND (Biomarker* OR marker* OR metabolite* OR biokinetics OR biotransformation OR bioavailability OR ADME) AND (intake OR meal OR diet OR ingestion OR administration OR consumption OR eating OR drink*).
Evaluation of the identified candidate biomarkers The quality of the individual candidate BFIs was assessed by evaluating their plausibility and specificity, dose-response, time-response, robustness, reliability, stability, performance, and reproducibility of their analysis method  [26]. The name of the potential biomarkers and their synonyms were queried in the previously mentioned databases in combination with AND (biomarker* OR marker* OR metabolite* OR biokinetics OR biotransformation OR pharmacokinetics OR bioavailability OR ADME) to collect the relevant information for each identified candidate biomarker. In addition, the databases HMDB (https://www. hmdb. ca), FooDB (http:// foodb. ca/), Phenol-Explorer (http://phenol-explorer. eu/), Dictionary of Food Compounds (http://dfc. chemnetbase.
com/faces/chemical/ChemicalSearch. xhtml), Duke's phytochemical and ethnobotanical databases (https://phytochem. nal. usda. gov/phytochem/search), eBASIS, (http://ebasis. eurofir. org/Default. asp), Knapsack (http://kanaya. naist. jp/ knapsack_jsp/top. html) and PhytoHub (http://phytohub. eu) were searched to identify all possible sources of each candidate biomarker and thus check its specificity. The validation level of the candidate BFIs was evaluated via the pre-defined criteria set by Dragsted and colleagues  [26] , which were based on eight questions related to the biological and analytical aspects: Q1: is the marker compound plausible as a specific BFI for the food or food group (chemical/biological plausibility)? Q2: is there a dose-response relationship at relevant intake levels of the targeted food (quantitative aspect)? Q3: is the biomarker kinetics described adequately to make a wise choice of sample type, frequency, and time window (time-response)? Q4: has the marker been shown to be robust after intake of complex meals reflecting dietary habits of the targeted population (robustness)? Q5: has the marker been shown to compare well with other markers or questionnaire data for the same food/food group (reliability)? Q6: is the marker chemically and biologically stable during bio specimen collection and storage, making measurements reliable and feasible? Q7: are analytical variability (CV%), accuracy, sensitivity, and specificity known as adequate for at least one reported analytical method? Q8: has the analysis been successfully reproduced in another laboratory (reproducibility)? This set of criteria therefore reflects the current level of validation of that particular compound and pinpoints the additional research needed to increase its validation.
Leafy vegetables The primary literature search on leafy vegetables resulted in 361 articles. After removal of duplicates, 247 publications were screened based on their title and abstract, 108 publications underwent full-text assessment, and eventually 61 publications were included in this review. In March 2019, the primary literature search was updated. The three databases yielded 34 new hits of which 24 remained after removal of duplicates, ten publications remained after title/abstract screening and four were selected based on the full-text assessment (Fig. 1 ). Main reasons for exclusion were that the: (1) intake assessment was too general (i. e. , vegetable intake), (2) focus was on the effect of the intake on health-related markers, and/or (3) exposure in a nutritional intervention study was not only green leafy vegetables.
Finally, the two searches combined resulted in 65 potentially relevant papers that provided data on the intake of one or more of the selected leafy vegetables (lettuce n = 7, endive n = 1, rocket salad n = 1, spinach n = 65) and measured one or more related compounds in body tissue, such as plasma, urine, and/or feces. The main characteristics of these studies are displayed in Additional file 1: Table  S1. A wide range of metabolites were reported in biofluids after consumption of leafy vegetables. However, as discussed below, many of these metabolites have other dietary origins and were therefore not specific enough to be retained as candidate BFIs. Carotenoids and their derivatives and conjugates were the most commonly reported metabolites after consumption of leafy vegetables. Carotenoids belong to the family of tetraterpenoids; where oxygen-containing carotenoids are called xanthophylls (e.
However, this compound is a common amino acid and a constituent of collagen that has also been suggested as a biomarker for processed meat consumption and can therefore be excluded as candidate biomarker for spinach  [59]. The last relevant study aimed at identifying the polyphenol metabolites recovered in human plasma after ingestion of 5 g freeze-dried 13 C-labeled spinach. The main metabolites were a glucuronide and a sulfate of 5,3′,4′-trihydroxy-3-methoxy-6,7-methylendioxyflavone (TMM), and spinacetin-glucuronide-sulfate  [49]. The secondary search in the literature and in various databases (FooDB, Phyto-Hub, Duke's phytochemical and ethnobotanical database, Phenol-Explorer, CRC press Dictionary of Food Compounds) revealed that the derivatives of spinacetin and TMM are compounds with a high specificity for spinach.
Bulb and stem vegetables The literature search in the three databases resulted in 40 papers considering asparagus or celery and 38 papers considering artichoke. Most papers reported on health effects rather than on candidate biomarkers of intake, used in vitro and animal models, and particularly focused on immune-modulation upon different asparaguspreparations. As such, these studies did not match our inclusion criteria and were excluded. After removal of duplicates, title/abstract screening, and full-text screening, eventually six papers were considered relevant for this review (asparagus (n = 2), artichoke (n = 3), and celery (n = 1)) (Table  2 ).
Funding This work was financially support by the  EU Joint Programming Initiative "A Healthy Diet for a Healthy Life" on Biomarkers BioNH FOODBALL  (grant number  529051002 ); the  German Federal Ministry of Food and Agriculture (BMEL)  through the  Federal Office for Agriculture and Food (BLE)  (grant numbers  2814ERA01E ,  2814ERA02E ,  2814ERA03E ); the  Canadian Institutes of Health Research (CIHR) ; the  Spanish National Grants  from the  Ministry of Economy and Competitiveness (MINECO)  ( PCIN-2014-133-MINECO Spain ), the  Generalitat de Catalunya's Agency AGAUR  ( 2014SGR1566 ); the  Research Foundation Flanders  (grant number  G0D4615N ); the  Agence Nationale de la Recherche  (# ANR-14-HDHL-0002-02 ); the  Italian Ministry of Agriculture, Food and Forestry Policies (MiPAAF)  within the  JPI-HDHL  ( MIUR D.
Acknowledgements This work was supported by the  Royal Society International Exchange  Grant n. IES \R2\181176. L. G and C. F. B. also acknowledge the support of the  Engineering and Physical Sciences Research Council  (Grant No. EP/R005192/1 ). G. K. was also supported by the  Agence Nationale de la Recherche  through  the  project  GIANTE ANR-18-CE30-0020-01. Computations were carried out on the Msocentre SIGAMM hosted at the  Observatoire de la Cte  dAzur and on the  HPC Rocket Cluster at Newcastle University  The authors also acknowledge the  University of Washington ,  Institute of Nuclear Theory program  entitled INT-19-1a Quantum Turbulence:  Cold Atoms ,  Heavy Ions , and  Neutron Stars ,.
II. METHODS High-quality single crystalline samples of EuIr 2 Si 2 were cleaved in situ under ultrahigh vacuum conditions at low temperature. The obtained surface exhibited large (up to several hundreds of microns in size) magnetic domains, which were easily identified by ARPES due to different (rotated) band structures of the studied surface states  [9]. The ARPES experiments were performed at the I05 beamline of the Diamond Light Source. Photoemission data for the paramagnetic (PM) and FM phases were obtained at temperatures of 200 K and 7 K, respectively. Fully relativistic density functional theory (DFT) calculations of the electronic and spin structures of EuIr 2 Si 2 were performed using FPLO code  [17]. Further details can be found in the Supplemental Material (SM)  [18].
It is essential that the magnetization should not be orthogonal to the surface of the studied crystal. The ARPES data must be collected from a single magnetic domain. The described approach is valid only in the regions of k space where the spin-split states can be well described by the simple twoband Hamiltonian with constant exchange coupling parameter J ex. Regions with band anticrossings should be avoided. The analysis relies on the determination of the magnetization direction and on the knowledge of the sign of J ex. If the latter is unknown, the spin structure can be determined only with accuracy to the sign (direction) of the spin vector. The proposed approach allows to analyze only one component of the spin vector in the direction of J, however, usage of the crystal symmetry can allow us to obtain the full spin structure of the studied surface states.
ACKNOWLEDGMENTS This work was supported by  Saint Petersburg State University  (Grant No. ID 51126254 ) and the  Russian Foundation for Basic Research  (Grant No. 20-32-70127 ). We acknowledge financial support from the  Spanish Ministry of Economy  (No. MAT-2017-88374-P ) and the  German Research Foundation (DFG)  through Grants No. LA655/20-1 , No. KR3831/5-1 , and  Fermi-NEst. We acknowledge  Diamond Light Source  for access to beamline I05 (Proposals No. SI18844-1  and No. SI17761-1 ). We thank  I. Nechaev  for fruitful discussions.
Acknowledgements We would like to thank the crew and officers of the Pourquoi Pas ?for their commitment during the DRADEM cruise, the Ifremer staff,  W. Roest  and  D. Graindorge  for their help in the preparation of the cruise, and the  Suriname and French Guiana authorities  for allowing the cruise to be performed in their Exclusive Economic Zones. Nathaniel Findling  provided help with the scanning  Electron Microscope. This work has been supported by  Ifremer ,  INSU ,  Labex  OSUG@2020, ISTerre, CEFREM and Géosciences Océan. Anonymous reviewers are thanked for their constructive comments and suggestions.
• The values of r 0 determined from the mass loss of the coupons NBS12/28, NBS36/21, and NBS31/15 at the end of the experiments are in good agreement with the solution data (Fig. 2a-c ). This gives credit to the rate determination. • Reproducibility of r 0 measurement was studied for NBS14/18, NSAC17, NSAC19, and NSAC21 for which tests were performed 3, 2, 3, and 5 times, respectively. Slight, non-impactful variations of the protocols were applied, such as the change in the glass powder size fraction or the change of the operator (tests on NSAC21 were performed by three different operators). For the four tested glasses, the obtained values fall within uncertainties. Although the number of tests is not sufficient to perform a robust statistical analysis, nonetheless these sets of experiments confirm that the uncertainty of 30% on r 0 taken in this study is reasonable.
By using the corrected n c values, we find that the RMSRE of the TCT-base predictive model decreased from 4. 63 to 2. 45. Further, if Fig. 3  Forward dissolution rate as a function of the number of constraints per atom (n c ) for the 7 borosilicate glasses selected to train the model (training set, CJ series, see Table  1 ). The data are fitted by Eq. 1. The standard deviation on the slope is 0. 356. the NSAC35 glass is excluded from the prediction, the RMSRE decreased from 0. 57 (with the bulk composition of NSAC19 and NSAC21 glasses) to 0. 37 with the Na-depleted composition of these two glasses. This last number means that the model can predict the initial dissolution rate of a new borosilicate or aluminosilicate glass dissolving congruently with a relative error of e 0. 37 , i. e. , 45%.
No water was used during the preparation of the powders and monoliths of the studied glasses. The 11 B MAS NMR spectra of the borosilicate glasses were collected on a Bruker AVANCE II 500WB spectrometer operating at a Larmor frequency of 160. 14 MHz (magnetic field 11. 72 T) using a 4 mm (outer diameter of the ZrO 2 rotor) Bruker (boron-free) CPMAS probe. The powder samples were spun at a frequency of 12. 5 kHz. A recycle delay of 2 s and a pulse length of 1 μs (tip angle of about 20°) were used to ensure quantitativeness of the spectra (i. e. , homogeneous excitation of BO 3 and BO 4 species, independently of their quadrupolar interactions). Data processing occurred via an in-house code (for details see Angeli et al. 40  ), including the correction of the center band from the contribution of n = 0 spinning sideband coming from the satellite transitions.
Solution samplings were carried out with a 5 mL syringe. Samples were filtrated at 0. 45 µm. Generally 5 or 6 samplings were carried out at regular intervals. For some tests, a few more samples of 1 mL each were taken and acidified with HNO 3 0. 5 M for ICP-OES analyses. The pH was controlled at the end the test, except for the glasses that dissolve incongruently. In this case the pH was regularly adjusted to the target value by addition of small quantities of HNO 3 0. 5 N. Si concentrations were determined by colorimetry with a Merck Spectroquant kit and a Cary Varian UV-visible spectrophotometer at a wavelength of 820 nm (concentration range: 0. 05-5 mg L -1 ). For some tests, extra samplings were also analyzed by ICP-OES (Thermo Scientific ICAP 6300 Duo) to confirm the data and get insight into the stoichiometry of the dissolution reaction.
ACKNOWLEDGEMENTS The planning and strategy of this work and the experimental work were supported as part of the  Center for Performance and Design of Nuclear Waste Forms and Containers , an  Energy Frontier Research Center  funded by the  U. S. Department of Energy (DOE), Office of Science, Basic Energy Sciences  under Award #  DESC0016584. The modeling work was supported by the  Department of Energy's Office of Nuclear Energy's Nuclear Energy University Programs  (Award # 18-15020 ) and  National Science Foundation  (Grant No. 1562066 ). The authors thank  PRIME VERRE (Montpellier, France)  for the preparation of the glass samples.
METHOD The data behind this paper was gathered as part of a bachelor's thesis at the Western Norway University of Applied Sciences, at the Institute of Economics and Administration during the spring semester of 2018. The method behind the thesis was based on a qualitative method including a survey via email of key persons responsible for the studies of economics at the university or university college. A total of 16 institutions were contacted, and 14 responded. The survey was carried out in the period 22 nd February -13 th April 2018. All respondents were first contacted by telephone for a brief orientation about the project and in order to seek consent for the survey, which was sent out later. The initial sample consisted of 25 universities and university colleges. After studying the curricula at the different schools of economics, 16 of them were contacted by telephone after identifying the key persons for the study programs.
The answer Nakamoto provided with Bitcoin was the use of the consensus method "Proof of Work" (PoW)  (Dwork & Naor, 1992). This method requires proof that someone has done an amount of work in order to be able to perform certain tasks. In Bitcoin and most other cryptocurrencies, this translates as solving a mathematical puzzle that requires a great deal of computational power. The mathematical puzzle is a SHA-256 hash operation on the transaction data that is about to be committed and approved for storing on the ever-growing blockchain (A. M. Antonopoulos, 2014). The only way to solve the puzzle is by brute force -that is, to try every possible combination until a result that meets the requirements is obtained. This is a stochastic process that guarantees randomness  (Nakamoto, 2008). The hash value of the previous block in the blockchain is included in the present block, and in this way a tamper-evident chain of transaction data is constructed  (Narayanan, Bonneau, Felten, Miller & Goldfeder, 2016).