dataesr/scientific-paragraphs-categorization huggingface dataset:
Thermodynamic modelling Thermodynamic modelling of the experiments was carried out using the Gibbs free energy minimization program GEMS  (Kulik et al. , 2013). GEMS is a broad-purpose geochemical modelling code which computes equilibrium phase assemblage and speciation in a complex chemical system from its total bulk elemental composition. The thermodynamic data for aqueous species as well as for brucite and portlandite were taken from the PSI-GEMS thermodynamic database  (Thoenen et al. , 2014). The amorphous SiO 2 data were from  Bernard et al. (2017b). The ion activity products (IAP) were calculated from the measured composition of the solutions for all the samples with respect to M 0. 78 SH 1. 48 and C 0. 80 SH 1. 94 to observe whether any change occurred upon variation of the pH and/or the concentration of other species.
Aqueous phase composition The pH values and measured concentrations of the solutions equilibrated with the co-precipitated samples are plotted in Figure  1  and compared to the data obtained for pure M-S-H and C-S-H. In the solution at equilibrium with the pure M-S-H sample, a pH of 8. 3 was measured, together with magnesium and silicon concentrations of 0. 38 mmol/L and 1. 44 mmol/L respectively. The substitution of magnesium by calcium at constant (Mg+Ca)/Si ratio of 0. 8 increased the pH (Figure  1 , Appendix A) from 8. 3 to 8. 9 (sample Co-0. 05) or 9. 3 (sample Co-0. 10). At the same time, the magnesium concentration was lowered. A comparable decrease of magnesium has been observed for pure M-S-H at higher pH values  (Bernard et al. , 2017b; Nied et al. , 2016)  and for M-S-H in the presence of some calcium  (Lothenbach et al.
The recorded 29 Si MAS NMR data were compared to calculated reference spectra (Cal-0. 70MC and Cal-0. 75MC) using a linear combination of the spectra of C-S-H 0. 8 and Co-0. 05 samples. This latter was preferred to pure M-S-H (Cal-0. 70M and Cal-0. 75M) since it provided a better fit of the experimental spectra, as shown in Figure  8. This better agreement together with the high fraction of silicate attributed to pure M-S-H could be explained by some uptake of calcium in M-S-H also at higher pH. However, it remained unclear whether calcium was sorbed on the surface and edge sites of M-S-H only or whether it could be present in the octahedral layer as well. Given the large amount of silicate associated with the M-(C)-S-H phase (28 and 18 % respectively, Table  3 ), all the magnesium in the solid fraction of the samples Co-0.
Acknowledgements The authors would like to thank  Daniel Rentsch  and  Catherine Lerouge  for helpful discussions,  Remi Chassagnon  for the TEM analyses,  Numa Pfenninger  for the ICP measurements,  Luigi Brunetti  and  Fabien Le Goff  for the support in the preparation of the samples and analysis. The NMR hardware was partially granted by the  Swiss National Science Foundation  ( SNSF , grant no. 206021_150638/1 ).
Body composition. Mice were scanned under isoflurane anesthesia using a high-resolution micro computed tomography (CT) scanner (La Theta LCT-100; Hitachi-Aloka Medical Ltd), to determine body composition and fat distribution. Indirect calorimetry. Measurements were carried out using the Phenomaster/Labmaster metabolic cages (TSE systems). Mice were adapted to single housing in cages similar to the Phenomaster cages for at least one week prior to measurements. Data displayed were collected after additional 2 days of habituation in the system. Animal sacrifice and tissue collection. Mice were food deprived for 2 h prior to sacrifice unless specified otherwise. All animals were sacrificed in the dark phase by decapitation, and trunk blood was collected in tubes containing 0. 5 M EDTA. The blood samples were centrifuged at 8,700 g for 10 min at 4 °C, and plasma was collected and stored at -80 °C until required.
Statistical analysis. All data were analyzed using GraphPad Prism (version 7. 0. 2) or IBM SPSS statistics (version 23). Outliers were identified using the Grubb's test and removed. Data normality was verified using the Shapiro-Wilk test (when n ≥ 7) and Kolmogorov-Smirnov test (when 5 ≤ n ≤ 6). Parametric data was analyzed using an unpaired Student's t-test (two-tailed) or 2 × 2 factorial parametric analysis of variance followed by Sidak's multiple comparison post hoc test whenever appropriate or a mixed repeated measures ANOVA followed by a simple effects analysis in the case of a significant interaction or a univariate ANCOVA. The nonparametric test (Mann-Whitney) was used when the data did not meet the criteria of normal distribution. Statistical tests used and P-values are mentioned in the figure legends.
In particular during the teaching process some applications of the case should be provided. Some of the main theorems and algorithms should be accompanied by programming in MATLAB. The main steps of modernization to be taken are: • Include more practical assignments. • Demonstrate applications of algebra and geometry. • Construct bridge between the problems of linear algebra and programming (this would be quite important especially for students of Informatics). • Use ICT tools for complex calculations. • Implement algorithms for the basic problems of linear algebra. (Gauss method, Cramer's rule, matrix inversion. ) • Use Math-Bridge for more practice and for the theoretical background. • Students should do experiments for geometric objects using ICT tools. • Before the final examination students should prepare a paperwork with the solution of problems assigned by the teacher and the results of their experiments.
Use of Technology Some programming languages (C++ or Pascal) are used for homework, writing of programs (topics are synchronized with the course content) is a mandatory part of the midterm tests. E-mail and social networks are sometimes used to have closer connection with students, give assignments etc. After participation in the MathGeAr project we use Math-Bridge and Moodle for teaching Mathematical Analysis-1. There are about 50 IAM students attending the course; for these profiles lectures and tutorials are set separately. Currently it is still too early to give details of the course outcome; we will have the results after the second middle test and final examination. But student's unofficial feedback is very positive. Finally, we would like to mention that quite recently we have got four foreign students in this course, and so far, they appreciate the course Mathematical Analysis-1.
Individuals with migraine who reported changing their preventive medication for any reason at least once were defined to have had a preventive treatment failure (TF). The applied quotas were required to ensure that the occurrence of TF was adequately represented among study participants, with a substantive proportion of individuals that had a history of ≥2 TFs. Participants were recruited by means of existing online panels (GfK Health) and support organizations for people with migraine. Participants' consent was obtained prior to participation in the survey and those who completed the survey were compensated with an incentive in the form of a voucher and aligned with local standards. Data were collected via Confirmit, an online platform, and internet surveys were completed independently by respondents.
Data analysis Data analysis was conducted using IBM SPSS Statistics (version 24) to generate summary statistics on country and aggregate levels, and Qlikview software was used for charting. Quality assessment of the data was performed and included an evaluation of response patterns and inconsistencies and an analysis of answers to open-ended questions. Comparisons of different subset of participants were completed using frequencies, means, medians, and standard deviations. The reported P-values are from the T-student test when testing differences between averages, and from the Chi-square test when testing differences between percentages or proportions.
While the sample does not entirely mirror the overall general population of people with migraine, it allowed selecting a representative and generalizable sample of individuals with severe disease. Recruitment bias may affect the estimates where differences are present between survey participants and the population from each country. For example, individuals with 1 to 3 MMDs are not included as part of this study, nor are people with migraine without ready access to the internet or those who are not part of the catchment group contacted to participate in this survey. Pooling data from different countries may also introduce some variability in the dataset, unless country-specific nuances can be accounted for. Another limitation of this study is the use of self-reported data because diagnosis of migraine and other relevant diagnoses (eg, those used in the comorbidity index) and reporting of healthcare visits and other variables of interest cannot be clinically confirmed.
The impact of migraine on the lives of affected individuals was also measured by some patient-reported outcome measures. A webbased approach appears to be an appropriate method to capture cross-sectional or longitudinal data and grants access to a population that would typically not be found in clinical settings, allowing a determination of the global burden of migraine  [5, 13]. A follow-up study, IBMS-II, was conducted  [14]  with the objective to characterize patterns of preventive medication use in persons with migraine; it concluded that persons experiencing a higher frequency of migraine days tried more medications than those with a lower number of MMDs. My Migraine Voice adds to the existing literature since it includes a large number of countries, involves people with migraine meeting the ICHD-3 criteria, and assesses migraine burden during premonitory, headache, and postdrome phases of the migraine attack, thereby allowing for a comprehensive and truly global assessment of migraine burden.
Acknowledgements The authors thank  Vishwas Agashe from Xcenda  for his assistance with medical writing for this manuscript and all study participants for their generous contribution to the  My Migraine Voice initiative. The authors wish to thank  Angela Fiorin  and  Stephanie Ribbe  from  Novartis  for their assistance with this study. Pharma, Alder,  Allergan ,  Almirall, Amgen, Autonomic Technology, AstraZeneca ,  Bayer Vital, Berlin Chemie, Böhringer Ingelheim, Bristol-Myers Squibb, Chordate, Coherex, CoLucid, Electrocore, GlaxoSmithKline, Grünenthal, Ipsen, Janssen-Cilag, Labrys Biologicals, Lilly, La Roche, 3 M Medica, Medtronic, Menerini, Minster, MSD, Neuroscore, Novartis, Johnson & Johnson, Pierre Fabre ,  Pfizer ,  Schaper and Brümmer ,  Sanofi ,  St. Jude ,  Teva  and  Weber & Weber.
Financial support for research projects was provided by  Allergan ,  Almirall ,  AstraZeneca ,  Bayer , Electrocore,  GSK ,  Janssen-Cilag ,  MSD  and  Pfizer. Headache research at the  Department of Neurology in Essen  is supported by the  German Research Council (DFG) , the  German Ministry of Education and Research (BMBF)  and the  European Union. H. C. Diener  has no ownership interest and does not own stocks of any pharmaceutical company. HCD serves on the editorial boards of Cephalalgia and Lancet Neurology. HCD chairs the Clinical Guidelines Committee of the  German Society of Neurology  and is member of the Clinical Trials Committee of the  IHS. Paolo Martelletti  received funds for research, education, advisory boards and editorial activity from  ACRAF Angelini, Allergan, Amgen, Electrocore ,  Novartis ,  Teva ,  Springer HealthCare ,  SpringerNature.
In 2017, EFNA funding was provided by the following companies:  MSD ,  Merck ,  Genzyme ,  Teva ,  Biogen ,  UCB ,  Pfizer ,  Grunenthal ,  Janssen ,  European Academy of Neurology. In a personal capacity, she has been reimbursed for participation on advisory/steering committees organized by  Pfizer  and  Novartis Paula K. Dumas  is on the Board of the  American Migraine Foundation  and the  Steering Committee of Coalition of Headache  and  Migraine Patients. She  has an equity interest in Migraine Again LLC and  Migraine Health Ventures Annik K. Laflamme ,  Rachel Fink , and  Pamela Vo  are  Novartis employees. European Headache Federation, Rome, Italy. 2  Department of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy. 3  Department of Neurology, Mayo Clinic , 5777  East Mayo Boulevard ,  Phoenix , AZ 85054,  USA.
This proved to be a difficult task in both SemEval-16 and SemEval-17 as it is the one with the least amount of data available. The baseline was the ranking retrieved by performing a Google search, which proved to be a strong baseline beating a large portion of the systems submitted. Contribution Our baseline is a simple multitask feed-forward neural network taking distance measures between pairs of questions as input. We use a question-answer dataset as auxiliary task; but we also experiment with datasets for pairwise classification tasks such as natural language inference and fake news detection. This simple, easy-totrain model is on par or better than state-of-theart systems for question relevancy ranking. We also show that this simple model outperforms a more complex model based on recurrent neural networks.
Datasets For our experiments, we use data from SemEval shared tasks, but we also take advantage of potential synergies with other existing datasets for classification of sentence pairs. Below we present the datasets used for our main and auxiliary tasks. We provide some summary statistics for each dataset in Table  3. SemEval 2016 and 2017 As our main dataset we use the queries from SemEval's subtask B which consists of an original query and 10 possibly related queries. As an auxiliary task, we use the data from subtask A, which is a questionrelated comment ranking task. et al. , 2015; Nangia et al. , 2017) , since it contains different genres. Our model is not built to be a strong NLI system; we use the similarity between premise and hypothesis as a weak signal to improve the generalization on our main task.
05* 82. 22* 71. 36* MTL -ALL Aux MTL-MLP 75. 90** 84. 17** 89. 59** 78. 51** 47. 45** 51. 12** 82. 90** 73. 10** Natural Language Inference Natural Lan- guage Inference (NLI), consists in predicting EN- TAILMENT, CONTRADICTION or NEUTRAL, given a hypothesis and a premise. We use the MNLI dataset as opposed to the SNLI data (Bowman.
Moreover, understanding the interactions between phaeoviruses, their Ectocarpales hosts, and the environment is also lacking. To address both issues, we screened kelp and Ectocarpales samples from Africa, Asia, Australia, Europe, and South America. We present a summary of the broad prevalence of phaeoviruses in these 2 major orders of brown algae including previous phaeoviral PCR screen data  [55, 67, 68]. We also describe the interactions between phaeoviral infections, Ectocarpales host species, and wave exposure, and finally the phylogeny of novel phaeoviral MCPs found in the kelp species E. maxima, E. radiata, M. pyrifera, and U. pinnatifida.
Phylogenetic Analysis and Tree Construction For phylogenetic analysis we used the protein sequences translated from the mcp gene fragments amplified from kelp and Ectocarpales (Figures  2  and 3 ) and from the mcp genes of known Phycodnaviridae and Mimiviridae (Figure  4 , see Tables  S1  and S3  for all accession numbers). Only the conserved MCP region aligned with the MCP fragment found in kelp and Ectocarpales was used to construct Figure  4. The additional sequences in Figure  4  were obtained using the GenBank blastp algorithm and selecting the sequence with the highest homology to phaeoviral MCP within each available genome of Phycodnaviridae and Mimiviridae. All translated amino acid sequences were aligned using MUSCLE using MEGA7  [82]. Bayesian inference analysis was performed using MrBayes v3.
2  [83] , stopping the analysis once the number of generations was over 300,000 and once the posterior probabilities no longer changed with each generation. Trees were visualized using Inkscape 0. 92  [80]  and Dendroscope 3  [84]  and rooted using MCP from the poxvirus Fowlpox virus (Poxviridae). The Phaeovirus MCPs reported in the genome of S. japonica  [70]  were compared to MCPs from other kelp species. Saccharina japonica MCPs were found with the GenBank blastn algorithm searching the S. japonica genome using MCP genes from EsV-1, FsV-158, and FirrV-1. MCP ORFs were identified from the S. japonica genome scaffolds using Artemis  [85]. MCPs from phaeoviral genomes were aligned with MCPs from S. japonica and the MCP primers (Figure  S2 ) to examine their homology. S1  for accession numbers and sample details, and Figure  3  for alignment.
Acknowledgments: D. M. and K. S. were funded by the  University of Plymouth. D. C. S. was funded by a  Marine Biological Association Fellowship. A. F. P. was funded by a  Marine Biological Association Ray Lankester Fellowship  and the project  IDEALG (France :  ANR-10-BTBR-04 ). J. J. B was funded by  National Research Foundation   CPRR grant   111719. C. A. S. was funded by the project  FONDECYT N • 11160369.
Comparison of metabolomics data among analytical platforms In order to compare the results from all platforms, we developed a specific strategy to standardize the data. First, the LC-MS (Fig. 1A, B ), GC-MS (Fig. 1C ), and NMR (Fig. 1D ) raw data were collected and standardized by the amounts of proteins. The analytical drift was then corrected using Workflow4Metabolomics, operating under the Galaxy environment, by a Van Der Kloet transformation  14. As each data table contained intensities expressed as arbitrary units specific for each instrument, we used a log10 and then a Pareto standardization for data acquired by mass spectrometry and only Pareto for NMR data. The NMR results were somewhat distinct from those of the MS platforms, but the PCA analysis showed that NMR data were consistent with the (control, docetaxel) vs.
Metabolites and lipids identification To determine the nature of the metabolites and lipids specifically altered by each treatment condition, several databases were queried, including the local database (NIST), online databases (LipidMaps, MassBank, HMDB) or in-house databases  15. One hundred and eleven distinct compounds-some being detected by more than one platform, including amino acids, small organic acids, sugars, spermidine, phosphatidyl-choline or triglycerides-were sorted out across the nine data matrices generated by all platforms (Table  1 ). Depending on the metabolite considered, changes in its intracellular content varied specifically with the treatments. For instance, glutamine was increased in lovastatin or (lovastatin + docetaxel) extracts. Spermidine was decreased with (lovastatin + docetaxel).
Kinetic effects We used quantitative GC-MS data for these experiments. Each set of data was normalized with respect to its own control at the same time of treatment. We analyzed 31 metabolites from cells treated by (lovastatin + docetaxel) for all time-points. The (lovastatin + docetaxel) treatments were best separated from their respective 17 h and 24 h controls according to the first dimension of the PCA (Fig. 7 ), like for cells treated by lovastatin alone at the same time points (data not shown). The first dimension of the PCA, which explained the largest fraction of the variance, showed a clear separation of the (lovastatin + docetaxel) condition at 36 h, from either the control or the drug combination at earlier time-points. Strikingly, nearly all metabolites were increased for the 36 h timepoint, suggesting increased synthesis over time (Supplementary Table  2 ).
Overlap between transcriptome changes and metabolites/ lipids altered levels Metabolites with a fold change >1. 5 and a p-value <0. 05 (lovastatin + docetaxel condition vs. control) and genes with a fold change >2 (lovastatin + docetaxel condition vs. control) were integrated into a global network using the Stitch software  17  for interaction analysis of metabolites and proteins, and the Cytoscape environment for visualization  18. This highlighted links between "cholesterol homeostasis" and "cellular carbohydrate biosynthetic processes" through intermediates from the "response to transition metal particles" and "female gonad development". "Cellular amino acids metabolic processes" appeared unlinked to these pathways (Supplementary Figure  4 ). We next used the ClueGO  19  application that extracts the non-redundant biological information for large clusters of genes, and PaintOmics 20 , which permits overlay transcriptomics and metabolomics data onto KEGG pathways.
Metabolomics and lipidomics analytical methods (see Supplementary information) Five instrumental platforms contributed to the analyses: NMRn°1 (1H-NMR, Bruker), LC-MSn°1 (UHPLC-LTQ-Orbitrap, Thermo), LC-MSn°2 (UHPLC-Exactive, Thermo), LC-MSn°3 (UPLC-HRMSe, Synapt Q-TOF G2, Waters), GC-MSn°1 (GC-MS, Agilent). From extraction to analysis, each analytical platform processed the samples using its in-house procedure (Supplementary Table  3 ). Altogether, the analyses, i. e. metabolomics, lipidomics, in both positive or negative ionization modes (for MS), resulted in 9 data matrices from 5 platforms. Each platform generated its own dataset in a tabular format where variables were the peak area of a m/z index for the mass spectrometry analysis or a bucket for NMR. These datasets included the 3 replicates for each treatment (control, lovastatin, docetaxel and co-treatment lovastatin + docetaxel), the analytical blank samples, and quality control samples (QC).
Data analysis Nine data matrices were generated by XCMS included in the workflow4metabolomics. We have collected all raw data from each platform, and we then have tuned each extraction parameter as finely as possible: one metabolomics data matrix in the positive electronic impact mode for GC-MSn°1, two metabolomic data matrices obtained in the positive and the negative ESI mode for LC-MSn°1, two metabolomic data matrices and two lipidomic data matrices generated in the positive and the negative ESI mode for LC-MSn°2, one lipidomic data matrix obtained in the positive ESI mode for LC-MSn°3 and one metabolomic data matrix for NMRn°1 platform. For each of the data matrices generated, a batch correction was applied to minimize the signal drift considering the inherent QCs. Then, to compare these data matrices, a normalization step was introduced, i.
e. combining all the generated data to be analyzed in the same order of magnitude. Mass spectrometry data were first normalized by a log 10 transformation, then all datasets (including MS and NMR) were Pareto transformed  13. The relationship between the data tables from the different platforms were analyzed using the RV coefficient  40, 41. This RV coefficient can be interpreted as a multivariate equivalent of the coefficient of determination, which is a measure of the quality of the prediction of a linear regression (R²) ranging from [0-1]. When considering two matrices X and Y, a coefficient RV equaling to 1 means that the relative position of the samples in X is similar to that in Y. In other words, the information contained in the two tables is identical. For each data matrix generated, a principal component analysis (PCA) was also performed with FactoMineR and the FactoExtra package working under the R computing environment  42.
Acknowledgements This work is dedicated to our dear colleague  Christophe Caron  who has been instrumental in the development of the Galaxy pipeline for Metabolomics studies. This work was supported by  Id2 Santé Bretagne , the  INSERM , and the  Université de Brest. We thank Biogenouest, particularly the Corsaire network of analytical platforms, for their support of this collaborative project. We also thank the support from the  IDEALG  project ( P. Potin ) and the  Régions Bretagne and Pays de Loire.
Methods The French Brain Tumor DataBase (FBTDB) identified and recorded all patients with newly-diagnosed and histologically confirmed primary brain tumors (e. g. , GBM) since 2006 in France, and prospectively collected initial data. FBTDB is one of the largest clinical databases for brain tumors in Europe  [18] [19] [20] [21] [22] [23] [24] [25] [26].