dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This work was supported by the  SIRIC ONCOLille, IWMF  and Celgene funding. We thank  Dr S. P. Treon  for the gift of BCWM. 1 cell line,  Dr S. Rosen  for the MM. 1S cell line and  Dr. S. Ansell  for the gift of MWCL-1 cell line. The authors also thank  Drs S. Guillard ,  S. Galiègue-Zouitina  and  A. -L. Nugues  for their support.
TGX Criterion 4-15% gel (Bio-Rad) was used for misfolded rec-PrP samples and 4-12% NuPAGE Midi gel (Invitrogen Life Technologies) for mammalian samples. The recombinant proteins were electroblotted onto polyvinylidene fluoride (PVDF) membranes (Trans-Blot Transfer Pack Turbo PVDF, Bio-Rad) and the mammalian proteins onto nitrocellulose membranes (Protran BA85, GE Healthcare). Membranes were probed with antibody 3F4 (diluted 1:10,000 in 0. 1% (w/v) of fat-free powder milk in phosphate buffered saline (PBS, Fisher Bioreagents) with 0. 05% Tween-20 (Sigma-Aldrich, Atom) as described previously  75. The immunoreactive bands were visualized by chemiluminescence using the Super Signal West Pico kit (Thermo Scientific Pierce) and the digital images were recorded by FluorChem Q (Alpha Innotech). Densitometric analysis of the immunoreactive bands obtained by WB was carried out using the AlphaView software (Alpha Innotech).
Acknowledgements This work was supported financially by  Spanish government  grants  AGL2015-65046-C2-1-R, PCIN-2013-065  and  BFU2013-48436-C2-1-P  and  Basque government  grant  2014111157. The authors would like to thank the following for their support: IKERBasque foundation, vivarium and maintenance from  CIC bioGUNE. Oxford Protein Production Facility UK (OPPF)  for the plasmid pOPIN E,  Dr. Belén Pintado  for the TgNN6h mouse embryo rederivation,  Dr. Ester Vázquez Fernández  for useful advice in the initial steps,  Drs. Isidro Ferrer  and  Carmen Navarro  for the GSS brain tissue samples. Mark P. Dagleish  ( Moredun Research Institute ) for useful discussion and advice.
1  Methylation level was measured by the beta value, which is the proportion of methylated molecules at each CpG locus, a continuous variable ranging between 0 and 1. 21  The Illumina 450k Bead Chip measures methylation levels at 4480 000 loci in human DNA and has been used in many cohort studies. 1  We report results from two common methods for the analysis of such methylation data sets. The first main analysis is an EWAS, which considers regression models for each CpG locus with EA. Using the EWAS results, we then performed a series of follow-up analyses: enrichment analyses, prediction analyses, correlation with tissue-specific methylation, and gene-expression analysis (Supplementary Note). The second main analysis uses the 'epigenetic clock'  22, 23  method, which employs a weighted linear combination of a subset of probes (that is, measured CpG methylation loci) to predict an individual's so-called 'biological age'.
Participant inclusion criteria To be included in the current analysis, participants had to satisfy six criteria: (1) participants were assessed for EA at or after 25 years of age; (2) participants were of European ancestry; (3) all relevant covariate data were available for each participant; (4) participants passed the cohort-level methylation quality control (QC); (5)  participants passed cohort-specific standard QCs (for example, genetic outliers were excluded); and (6) participants were not disease cases from a case/control study. DNA methylation measurement and cohort-level QC Whole-blood DNA CpG methylation was measured genome-wide in all cohorts using the Illumina 450k Human Methylation chip (San Diego, CA, USA). We standardized the cohort-level QC and preprocessing of the methylation data as much as possible, while ensuring some degree of flexibility to keep the implementation feasible for all cohorts (leading to slight variation in preprocessing across cohorts, as is common  13, 15, 17  ).
Epigenetic clock analyses To construct our epigenetic clock variables (Supplementary Note 3), the cohort-level raw beta-value data were entered into the online Horvath calculator,  23  as per our preregistered analysis plan. The 'normalize data' and 'advanced analysis for Blood Data' options were selected. The following variables were selected from the calculator's output for subsequent analysis:   45  These Clock measures are annotated in the Horvath software as follows: 'AgeAccelerationResidual', 'AAHOAdjCellCounts', 'AAHAAdjCellCounts', and 'BioAge4HAAdjAge'. We analyzed two regression models, both with EA as the dependent variable and a clock variable as an independent variable. In the basic age acceleration model, we control for sex, and in the adjusted age acceleration model, we additionally control for BMI and smoker status (current, previous or never smoker).
The outcome variables in this cohort were average educational achievement test scores (Key Stages 1-4  48  ) from age 7 up to age 16 years. To examine the relationship between epigenetic and genetic associations, we also constructed a single-nucleotide polymorphism polygenic score (SNP PGS) for EA. We used SNP genotype data available in the three adult prediction cohort studies (LBC1936, RS-BIOS and RS3). We constructed the SNP PGS in each cohort as a weighted sum of the individual genotypes from all available genotyped SNPs, with GWAS meta-analysis coefficients as weights. We obtained the coefficients by re-running the largest GWAS meta-analysis to date of EA  35  after excluding our prediction cohorts (LBC1936, RS-BIOS and RS3). We evaluated the predictive power of the PGMS by examining the incremental coefficient of determination (incremental R 2 ) for predicting EA (or test scores in ALSPAC ARIES).
Overlap between EWAS probes and published GWAS associations. To supplement our polygenic score analyses of the overlap between epigenetic and genetic associations, we next investigated whether our lead probes are located at loci that contain SNPs previously identified in GWAS of EA and smoking The method is described in Supplementary Note 7. Correlations that are significant after Bonferroni correction are marked with two asterisks (**), and marginal significance (P o0. 05) is marked with one asterisk (*). The tissue-specific methylation data are from the Roadmap Epigenomics Consortium, and we used their categorization and color code for simplicity of comparison. 52 Supplementary Note 5). Considering jointly the 141 approximately independent EWAS probes with P o 1 × 10 -4 , we did not find evidence of enrichment for either EA-linked SNPs (P = 0.
Clinical data Cardiovascular Disease (CVD) Risk Factors, treatment at admission, in-hospital management were collected for each patient. Ischemia time was defined as the time from symptom onset to complete reperfusion after percutaneous coronary angioplasty. Left Ventricular Ejection Fraction (LVEF) was evaluated using the Biplan method by routine cardiac ultrasound during the ICU admission. Cardiac magnetic resonance imaging protocol 20 of the 101 patients underwent a ce-CMR study one month after initial admission for MI. Patients were scanned in a supine position on a 1. 5 T MAGNETOM Avanto TIM system (Siemens, Erlangen, Germany). Ce-CMR were analysed as previously described  [32] [33] [34]. Briefly, LV function at rest was assessed with retrospective ECG-gated steady-state free precession pulse cine sequences (cine TrueFISP) in long and short axis views in the true heart axis.
An intravenous bolus of Gadolinium-DOTA at a dose of 0. 2 mmol/kg body weight for the late enhancement analysis was performed. Late gadolinium enhancement (LGE) was evaluated 10 minutes after contrast injection using a 3D-gradient spoiled TurboFLASH sequence with a selective 180˚inversion recovery pre-pulse, in the short axis covering the whole ventricle. MI was identified by LGE within the myocardium, defined quantitatively by myocardial postcontrast signal intensity > 2SD above that within a reference of region remote noninfarcted myocardium within the same slice with the post-processing software Osirix (OsiriX Foundation, Geneva, Switzerland).
Limits of our study Our study was monocentric and retrospective and this induces a potential risk for selection bias. Further, blood samples were collected at the early phase of acute MI (H0 and H4), and we did not assess IL-17A after H4. As shown in an experimental study on mice, IL-17A RNA levels peaked at 24 hours after MI  [24]. Our data only allows us to assess the early phase of reperfusion and therefore is incomplete. This also raises the limitation of direct measurement of cytokine levels in the myocardial tissue and their correlation with serum levels. In our study we did not perform myocardial biopsies, that could have shown different results in terms of IL17A expression. Finally, we had a small sample size for the comparison with CMR IS.
In this analysis, the approach was implemented in our software, CIAO (provided at https://sites. google. com/site/mtray-lor263/software/covariate-informed-gwas-analysis). Specifically, the approach taken is to model phenotype data using a continuous unobserved normally distributed quantitative trait, called the disease liability (u5 P J j51 c j ðt j 2 t j Þ1m1e), where e5cg1 N ð0; 1Þ and g denotes the genetic effects. Then, an individual is a case (z 5 1) if and only if u 0 and is a control (z 5 0) otherwise. c j is a parameter estimating the effect of a given covariate j on the liability scale. m denotes the disease prevalence p at the covariate mean t j under a normal cumulative distribution function Uð2mÞ5p. This model is used to approximate the effect of a disease covariate-in this case, ageat-onset-on the liability scale, based on estimates of risk of IS by age from epidemiological data, thereby estimating c j.
Controls were modeled in the same way, but were assumed to take the posterior mean from the lower (unaffected) portion of the distribution in the liability threshold model (Eðejz; tÞ5 Ð 2cðt2 t Þ2m 1 e 1 ffiffiffi 2p p e 2e 2 2 ð Þ d e Ð 2cðt2 t Þ2m 1 1 ffiffiffi 2p p e 2e 2 2 ð Þ d e ; if z50Þ. Where age data was missing, individuals were assigned the median age value (<1% of cases). Regression was then performed on posterior liabilities (Eðejz; tÞÞ by multiplying the number of samples by the squared correlation between the expected genotype dosage and posterior mean liabilities for each of the discovery cohorts in the four IS phenotypes (all IS, CE, LAS, and SVS). Ancestryinformative principal components were included, where appropriate, using the EIGENSTRAT procedure. 20 Any residual inflation was accounted for by adjusting results by the genomic inflation factor, k.
Evaluation of Regulatory Chromatin States, mRNA Expression, and DNA Methylation To investigate a novel locus, we used existing resources and performed some further analyses to characterize its regulatory potential. We interrogated chromatin states and regulatory motifs from ENCODE and Epigenomics Roadmap using Haploreg v4. 1. 26 We also evaluated whether the associated SNP influences gene expression using the Genotype-Tissue Expression (GTEx) portal. 27 Upon identifying an association between the SNP and expression of a nearby gene, we evaluated the evidence that the association signal for SVS and gene expression derives from the same causal variant using a Bayesian colocalization test. 28 Using the R coloc package (http://cran. r-project. org/web/packages/coloc), we compared five models for SNPs with 50kb of our lead SNP using the approach (H 0 : No association with either trait; H 1 : Association with SVS, not with expression; H 2 : Association with expression, not with SVS; H 3 : Association with SVS and expression, two independent SNPs; H 4 : Association with SVS and expression, one shared SNP).
Regulatory Chromatin States, mRNA Expression, and DNA Methylation Related to 16q24. 2 We used existing databases to assess the functional consequences of SNPs in the 16q24. 2 region. First, we used the Haploreg v4. 1 database to interrogate chromatin states and regulatory motifs from ENCODE and the NIH Roadmap Epigenomics Mapping Consortium. 26, 37, 38 The database showed that our lead SNP influences chromatin states in multiple tissues. The SNP is classified as a genic promoter in nine tissues, an enhancer in 13 tissues, and overlaps DNAse1 hypersensitivity sites in 21 tissues. Second, we used publicly available databases to evaluate the evidence that the lead SNP influences expression of nearby genes using the GTEx portal. 27 The implicated A allele of our lead SNP (rs12445022) was associated with decreased expression of ZCCHC14 in tibial arterial tissue (p 5 9.
Acknowledgment  Matthew Traylor  is funded by the  NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. Hugh Markus  is supported by an  NIHR   Senior Investigator award  and his work is supported by  NIHR Comprehensive Biomedical Research Unit funding  awarded to  Cambridge University Hospitals Trust. Cathryn Lewis  receives salary support from the  National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. Collection of the  UK Young Lacunar Stroke DNA Study (DNA Lacunar ) was primarily supported by the  Wellcome Trust  ( WT072952 ) with additional support from the  Stroke Association  ( TSA 2010/01 ). Genotyping of the DNA Lacunar samples was supported by a  Stroke Association Grant  ( TSA 2013/01 ).
This is the first time that a global ocean model for manganese has been written and assessed. It is a basic model that should give a starting point for further studies. At the moment there is no mechanistic evidence for typical uptake-remineralisation processes as is the case for, e. g. iron. However, as there is of course uptake of Mn by phytoplankton, and sometimes it can even be a limiting nutrient  (Middag et al. , 2013) , we include a biological cycle of Mn in the model. While we perform a model simulation on a global scale, we give more attention to the Mn diss distribution in the Atlantic Ocean. We will compare our simulations in detail, and quantitatively, with the highly accurate dataset from the GEOTRACES programme reported here, namely the GA02 section in the west Atlantic Ocean.
The resulting mixture of carrier solution, ammonium hydroxide and luminol solution had a pH of 10. 2±0. 05 and entered a 3 m length mixing coil placed in a water bath of 25 • C. Hereafter the chemiluminescence was detected with a Hamamatsu HC135 Photon counter. Concentrations of dissolved Mn were calculated in nanomole per litre (nM) from the photon emission peak height of triplicate measurements. Table  1. Compliance with the international GEOTRACES reference samples program. In the left column are the international consensus values. In the right column are the values reported here as part of the GA02 dataset. Mn diss (nmol kg -1 ) Consensus  Middag et al. (2015b)  SAFe D2 0. 35 ± 0. 05 0. 33 ± 0. 01 (n = 24) GEOTRACES S 1. 46 ± 0. 14 1. 47 ± 0. 03 (n = 10) GEOTRACES D 0. 21 ± 0. 03 0. 18 ± 0.
1. For both occupations the samples were analysed by ICP-MS by Rob Middag at UCSC. Moreover, the same analyst had done shipboard FIAs during the 2010 cruise. There was no statistical difference within analytical uncertainty between the three datasets in the deep waters  (Middag et al. , 2015b). Small differences within the upper ∼ 500 m between the Netherlands sampling and the US sampling are deemed to be real due to the seasonal variability over the more than 1 year time difference of sampling. Furthermore, these Mn profiles are also consistent with the profiles determined by  Landing et al. (1995). Besides the shipboard FIA, we also analysed samples using mass spectrometer analysis. Figure  2  shows the correlation between the two methods of analysis for the determination of [Mn diss ], shipboard and laboratory measurements.
Model validation In this study we mainly use data from the GEOTRACES programme, but for a worldwide global ocean comparison one also has to rely on data that were collected in the era before the reference samples of SAFe and GEOTRACES were available. Table  5  lists the datasets from GEOTRACES expeditions, as well as several other datasets; Fig. 5  shows the coordinates of the stations. Most of these GEOTRACES datasets are part of the publicly available GEOTRACES Intermediate Data Product (IDP) 2014  (Mawji et al. , 2015) , except for GP16  (Resing et al. , 2015)  that will be released as part of the GEOTRACES IDP 2017. These observations are used for a visual global ocean data-model comparison. Details on the statistical and visual model-data comparison are presented in Appendix A. Of these observations, only the west Atlantic Ocean data (the GEOTRACES GA02 transect, consisting of 64 PE 319, PE 321 and JC 057; 1320 points) and the Zero-Meridian Southern Ocean data (GIPY5; 468 points) have been used for statistical comparison with the model.
Moreover, only the data of the shipboard FIA have been used, which have five stations fewer than the on-shore mass spectrometry determinations (ICP-MS having 120 more measurements, thus 1440 in total). The focus of this study is the west Atlantic Ocean for several reasons. Firstly, recent measurements have been carried out in that region, resulting in a large consistent (one-method) dataset. Other regions generally contain fewer measurements and are mainly based on different methods by different analysts. Secondly, the west Atlantic Ocean is of importance to the Atlantic meridional overturning circulation, and hence the deep ocean cycling of for example the major nutrients. Therefore the west Atlantic Ocean was chosen as a key site for the ∼ 18 000 km long GEOTRACES GA02 transect for the collection of data for dissolved Mn and a suite of other trace elements and isotopes.
Reference simulation After 600 years, both the upper 100 m and the deep ocean relative Mn content changed by less than 25 ppm over a period of 10 years in the Reference simulation. This is about a factor of 4 less compared to the decadal change of 100 years ago. The small and reducing drift suggests that the simulation is practically in a steady state. Model output data are available on PANGAEA  (van Hulten, 2017). Figure  8  shows the modelled and measured dissolved Mn concentrations at four depths; observations are shown as coloured dots (same scale). The dissolved Mn concentration is high in the surface of the Atlantic, Indian and Arctic oceans, mostly consistent with the observations (Fig. 8a ). The model also reproduces the latitudinal gradient of [Mn diss ] in the Atlantic and Indian oceans, reflecting dust deposition patterns (Fig.
, 2011b). The Gakkel Ridge is not in our hydrothermal forcing field (Fig. 4d ). 2014 ) are presented as coloured dots. Again, the general patterns are captured, but the concentration of Mn diss very close to the Mid-Atlantic Ridge is underestimated, while above the ridge (at about 2-3 km depth) it is overestimated. In other words, the modelled hydrothermal Mn diss gradient is not as sharp as in the observations. It is difficult to improve this feature because of the low vertical model resolution at that depth. To more objectively compare the different simulations to each other, we list several goodness-of-fit statistics in Table 6. They compare the model simulations to observational data from the GEOTRACES GA02 transect. The model-data [Mn diss ] Pearson correlation coefficient r has a value of 0.
The new ultraclean sampling methods  (Rijkenberg et al. , 2015) , rigorous calibrations and excellent accuracy at the Bermuda crossover station for 12 trace metals  (Middag et al. , 2015b)  now yield a much larger database of uniformly very low Mn ∼ 0. 1 nM in deep waters. Given these very uniform and much lower background Mn concentrations, the hydrothermal Mn plumes are better discernible and much more widespread than realised in the 1990s. Exactly over or very near the ridge crest this has recently been shown dramatically in a plume more than 500 km wide over the Gakkel Ridge in the high-latitude Arctic Ocean  (Middag et al. , 2011b) , and in the Antarctic Ocean with a plume that extends more than 1000 km, at a site near the Bouvet triple junction where three ocean ridges meet  (Middag et al.
Figure10presents [Mn diss ] of the GA03 west to east transect. Observations from Wu et al. (2014)  are presented as coloured dots. Again, the general patterns are captured, but the concentration of Mn diss very close to the Mid-Atlantic Ridge is underestimated, while above the ridge (at about 2-3 km depth) it is overestimated. In other words, the modelled hydrothermal Mn diss gradient is not as sharp as in the observations. It is difficult to improve this feature because of the low vertical model resolution at that depth. To more objectively compare the different simulations to each other, we list several goodness-of-fit statistics in Table 6. They compare the model simulations to observational data from the GEOTRACES GA02 transect. The model-data [Mn diss ] Pearson correlation coefficient r has a value of 0.
Acknowledgements. We would like to thank several people in particular who helped in different ways in this study. Angela Milne ,  William Landing  and  Joseph Resing  kindly provided their manganese data from the Pacific Ocean. We thank  Caroline Slomp ,  Catherine Jeandel  and  Micha Rijkenberg  for the useful discussion. Loes Gerringa  has kindly provided speciation calculations on manganese. Loes Gerringa  and  Micha Rijkenberg  organised and served as expedition leaders of the three cruises of the GA02 section. This research was funded by the  NWO  (grant  839. 08. 410 : GEOTRACES, Global Change and Microbial Oceanography in the West Atlantic Ocean , and grant  820. 01. 014 : GEOTRACES Netherlands-USA Joint Effort on Trace Metals in the Atlantic Ocean ). This study was partly supported by a  Swedish Research Council  grant ( 349-2012-6287 ) in the framework of the French-Swedish cooperation in the common research training programme in the climate, environment and energy agreement between VR and LSCE, for the project "Particle transport derived from isotope tracers and its impact on ocean biogeochemistry: a  GEOTRACES project in the Arctic Ocean ".
(2014) 91 GA03 2011 US GT11 North Atlantic Wu et al. (2014) 578 GP16 2013 US EPZT South Pacific Resing et al. (2015) 874 Other expeditions and datasets - 1983 VERTEX-IV North Pacific Ocean Landing and Bruland (1987) 27 - 2006 EUCFe (RV Kilo Moana) Pacific Ocean Slemons et al. (2010) 349 - 2005/2006 CLIVAR P16 Pacific Ocean Milne and Landing (unpublished data) 174 - 2007 CoFeMUG South Atlantic Noble et al. (2012) 429 Total number of dissolved Mn measurements: 5742.
e. the first event in an AOP), as well as subsequent key events. Aldert H. Piersma (Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), The Netherlands) took the example of embryonic stem cell neural differentiation to highlight important aspects to consider when establishing best practices for 'omics studies. He highlighted that a scientifically sound study design is pivotal for a successful study outcome. Further, it should be ensured that the statistical analysis of 'omics data does not override the evaluation of their biological relevance since the magnitude and/or variance of the gene expression response does not provide useful information about the dynamic range or the dose-response relationship of the measured response. He cautioned that in mixed cell type tissues, individual genes might show limited responses, whereas their expression changes might be highly significant when related to specific cell types.