dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1995). Relative standard deviations are 1% for SiO 2 and b2% for the other major elements, except for low concentrations (b0. 50%) for which the absolute standard deviation is 0. 01%. For trace elements, the relative standard deviation is ca. 5% except for Er (ca. 20%). Calibrations were made using international standards JB2, BEN, ACE, WS-E, PM-S as well as specific reference samples. To determine interlaboratory consistency, some of our samples were analysed at both laboratories yielding results within analytical uncertainties for the ICP-AES and ICP-MS methods. As such, we report the entire composite data set here. All analyses were recalculated on an anhydrous basis with iron expressed as Fe 2 O 3 t = Fe 2 O 3 + 1. 111 FeO. Results of the analyses are given in Table  2  and Supplementary Electronic Material (SEM 2).
Such a scenario, however, conflicts with the dataset presented here. In particular, the Yb content does not correlate to SiO 2 content (Fig. 4 ) as would be expected for contamination by the continental crust. Similarly, both La/Yb and Sr/Y ratios, whose high values are principal adakite fingerprints  (Defant and Drummond, 1990; Martin et al. , 2005) , are not positively correlated to SiO 2 (Fig. 10 ). Assuming the adakitic characteristics are exclusively generated by crustal contamination mechanisms, ratios should be positively correlated to SiO 2 , which is clearly not the case at El Misti. In addition, our results reveal that the Sr/Y ratios at El Misti are anticorrelated to SiO 2. This observation is supported by the fact that the adakitic characteristics are also present in less differentiated magmas (e.
(1 -F) crystallisation rate in %; ∑r 2 sum of the residual squares. Major element compositions of rock-forming minerals from El Misti are also given. For each mineral, the average composition (re-calculated on an anhydrous basis) is derived from several analyses. N is the number of analyses. Mineral abbreviations: Cpx clinopyroxene, Pl plagioclase, Ol olivine, Amp amphibole, ti-mt titano-magnetite, Ap apatite. Trace element compositional data of El Misti series parent and daughter magmas used for fractional crystallisation modelling with Rayleigh (1896) equations. The composition of the calculated daughter magma is given for comparison with the hypothetical daughter magma. Partition coefficients (K D ) for andesites used for trace element modelling are given. K D for amphibole is from  Sisson (1994)  and  Martin (1987).
, 2014; Kiss et al. , 2014). Thus, careful textural and compositional investigation, involving a comparison of the amphibole's textures and compositions with experimental data is recommended before conducting such thermobarometric calculations. Our mineralogical study showed that amphibole crystallisation occurred primarily at equilibrium with the surrounding melt and resulted in a normal zoning. To determine amphibole crystallisation pressures we used the Amp-TB. xls spreadsheet of  Ridolfi et al. (2010). Results indicate that El Misti amphibole crystallised at pressures ranging from 237 ± 26 to 413 ± 45 MPa which, assuming a crustal density in the range of 2700 to 2900 kg/m 3  (Kono et al. , 1989) , suggest crystallisation depths of about 9 ± 1 km to 15 ± 2 km. These estimates concur with those of  Tepley et al.
Acknowledgements This work is part of the first author's PhD and was funded by the  French Institut de Recherche pour le Développement (IRD) ,  Département Soutien et Formation des Communautés Scientifiques du Sud (DSF)  hosted at Laboratoire Magmas et Volcans (LMV) and the  Instituto Geológico Minero y Metalúrgico of Peru (INGEMMET). It has been completed within  IRD's joint research programs 'Jeune Équipe associée à l'IRD'  and the  Laboratoire Mixte International (LMI) 'Séismes et Volcans dans les Andes '. We thank  P. Soler ,  G. Hérail , and  L. Fidel  for administrative and logistical support in France and  Peru ,  J. P. Eissen  and  J. L. Devidal  for sampling and technical help with microprobe analyses and our colleagues at the  Observatorio Vulcanológico at INGEMMET in Arequipa  for continuous support during field campaigns.
The detailed description can be found in the work  [28]. Note that   and  0 can be determined from the relation with the transition length ℓ and the interface energy   or the critical fracture energy   : 𝜎 𝜑 = √ 𝛼 0 𝜔 𝜑 3 √ 2 and ℓ = 𝛼 * √︂ 2𝛼 0 𝜔 𝜑 , or 𝜔 𝜑 = 𝑔 𝑐 2ℓ 𝛼 0 = 𝑔 𝑐 ℓ 2 , ( 11 ) where  * ≈ 2. 94 corresponds to the transition zone taken within the range 0. 05 <  < 0. 95. The electrochemical energy density   (  ,   , ) in Eq. 8 is chosen based on the KKS model  [26] , wherein the transition region is postulated as a mixture of the two phases with different chemical compositions, but equal chemical potentials: 𝜓 𝑐 (𝑐 𝑠 , 𝑐 𝑙 , 𝜑) = ℎ(𝜑) [︁ 𝐴 (𝑐 𝑠 -𝑐 𝑠𝑒 ) 2 ]︁ + [︁ 1 -ℎ(𝜑) ]︁[︁ 𝐴 (𝑐 𝑙 -𝑐 𝑙𝑒 ) 2 ]︁ , ( 12 ) where  is the free energy density curvature which is often identified from thermodynamical databases  [29, 30] ;   and   are the equilibrium concentrations of the coexisting phases; the concentration of solid   and liquid   phases can be expressed from the equilibrium condition at the transition zone as follows: {︂ 𝑐 𝑠 = 𝑐 + [︀ ℎ(𝜑) -1 ]︀(︀ 𝑐 𝑙𝑒 -𝑐 𝑠𝑒 )︀ 𝑐 𝑙 = 𝑐 + ℎ(𝜑) (︀ 𝑐 𝑙𝑒 -𝑐 𝑠𝑒 )︀.
The considered zirconium alloy is composed mostly of zirconium, tin, iron and chromium with a typical mass density of  alloy = 6. 56 g/cm 3. The complete chemical composition is given in Table. 1. The metal concentration is finally obtained  alloy s = 72. 23 mol/ (see the work  [23]  for more details). The diffusion coefficient is assumed to be homogeneous for all chemical compositions, and taken as the value for pure Zr, with   = 1. 3 × 10 -8 m 2 /  [34, 35]. The saturated concentration is taken as in the study  [36] :   = 3 mol/. Hence, the saturated concentration of solid and liquid phase are   = 1,   = 3/72. 23. The free energy density curvature  is identified by fitting the chemical free energies obtained from thermodynamic databases. That gives  = 8. 02 × 10 8 J/mol. The size of transition zone and the interface energy are chosen ℓ = 0.
Acknowledgment: The authors would like to acknowledge the  National French Research Agency (ANR)  for its financial support under contract  MATETPRO ANR-12-RMNP-0020  ( ECCOFIC  project). The authors would like also to thank their partners  Institut de la Corrosion, AREVA NP, Andra, MISTRAS Group and Pierre Combrade  for their participation in the fruitful discussions during this work.
Acknowledgements: The authors are grateful to the patients for their contribution to this research as well as to  Drs Carmela Caballero  ( EORTC Headquarters, Brussels, Belgium ),  Hellmut Samonigg  ( Medical University of Graz, Graz, Austria ),  Eric Joosens  and  An Vandebroek  ( ZNA Middelheim, Antwerp, Belgium ),  Charles Debrigode  ( CHU Hôpital Caremeau, Nimes, France ), and  Jelic Svetislav  ( Institute for Oncology and Radiology of Serbia, Belgrade, Serbia ). Additional investigations were proposed in a second stage to evaluate (1) the prognostic effect of p16/HPV markers; (2) the predictive and prognostic effect of HPV DNA ISH and HPV RNA qPCR. In addition, the same analysis, restricted to oropharynx only, was conducted. The following markers were assessed in these samples: a. Conventional IHC on TMA for p16 expression status (positive/negative), b.
The current literature on methods to quantify WMH burden and progression is large  [13, 14]. Semi-automated or automated methods are thought to be sensitive and reproducible, but very few have been compared directly with each other, or with visual scores. Furthermore, errors in tissue classification, in exclusion or not of artefacts, and in manual correction of computer-generated masks have largely not been assessed but probably contribute to inconsistencies in results of studies of WMH associations  [13]. In most cases, the computational requirements to run the most up-to-date fully automatic segmentation techniques are not specified, nor the processing time, which could be many hours for a single dataset. So far, most large clinical or population studies have used conventional histogram-based thresholding of FLAIR images to assess WMH volume (see Supplementary Table from  [13] ), mainly owed to its simplicity, minimal resource requirements and relative speed, and, perhaps for these reasons, there is a growing tendency to base assessment of new WMH quantification techniques on this approach.
6 years (SD 0. 7) and approximately three years later. Formal written consent from all subjects and ethical approval was acquired from the Lothian Research Ethics Committee (09/S1101/54, LREC/2003/2/29, REC 09/81101/ 54), the NHS Lothian R+D Office (2009/W/NEU/14) and the Multi-Centre Research Ethics Committee for Scotland (MREC/01/0/56) and conducted according to the principles expressed in the Declaration of Helsinki. The selection of the datasets was only based on availability of complete data required for the evaluation of the metric.
Total SVD Fig. 1  Schematic representation of the two basic NAWM and WMH segmentation methods applied to the datasets. 1) (yellow panel) Multispectral segmentation using colour fusion (MCMxxxVI,  [29] ) fuses in the red-green colour space T1-with T2-weighted, and T2*-weighted with FLAIR, applies minimum variance quantisation to the fused image and selects the quantised levels corresponding to WM and WMH. These are, then, combined to separate the intense from the less intense WMH and these from the normal-appearing WM. 2) (blue panel) Multispectral Gaussian clustering  [26]  of the multidimensional array formed by concatenating the normalised and brain-extracted FLAIR, T1-, T2and T2*-weighted images to generate a WM likelihood estimate that generates the priors to threshold the FLAIR image and extract the WMH scores were obtained as per  Staals et al.
Statistical analysis We used analysis of covariance (ANCOVA)  [33, 34]  with the follow-up WMH measurement as the outcome and the baseline measurement and Prins as predictors to evaluate the strength in the relation between quantitative and visual assessments of WMH change, and univariate linear regression, all programmed in SAS 9. 3 (www. sas. com). For completeness, we also analysed the change scores  [35]  of WMH volume, Prins and WM damage. For comparability with other existing literature  [11, 27, 36]  we also evaluated the bootstrapped nonparametric correlations (Spearman ρ) between the WMH volume and the burden of WM disease as assessed visually from each method and compared them with those obtained between the WM damage (i. e. the new metric) and the same visual scores. The correlations were obtained using the MATLAB Robust Correlation Toolbox (http://sourceforge.
5. 35  [-18. 75 29. 45 ](%) for the WMH volume measurements). Similar results were observed using T2 W to quantify the signal intensity. Figure  S2  (Supplementary material) shows the four measurements of WMH volume and WM damage for all the 38 datasets of this sample using FLAIR images to assess the signal intensity. Non-parametric correlations (Spearman ρ) between the WM damage metric and visual rating scores were all significant (p < 0. 001) and slightly stronger than between WMH volume and visual ratings (Table  3  and Supplementary Table  S4 ).
This study was funded by the Row Fogo Charitable Trust (Grant No. BRO-D. FID3668413). The Stroke sample data acquisition and processing was funded by The Wellcome Trust (Ref. No. 088134/ Z/09/A). Funds from the European Union Horizon 2020 BSVDs@target( Ref. PHC-03-15, Project no. 666881), the Innovate UK (Ref. 46917-348,146) and Fondation Leducq (Ref no. 16 CVD 05) are also gratefully acknowledged. The Lothian Birth Cohorts (LBC) data collections were supported by Research Into Ageing and continues as part of The Disconnected Mind project, funded by Age UK, with additional funding from the UK Medical Research Council under grant numbers G0701120, G1001245 and MR/M013111/1. Magnetic resonance image acquisition and analyses were conducted at the Brain Research Imaging Centre, Neuroimaging Sciences, University of Edinburgh (www.
bric. ed. ac. uk) which is part of the SINAPSE (Scottish Imaging Network-A Platform for Scientific Excellence) collaboration (www. sinapse. ac. uk) funded by the Scottish Funding Council and the Chief Scientist Office. Part of this study (LBC data) was undertaken within The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology (www. ccace. ed. ac. uk), part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1) for which funding from the BBSRC and MRC are gratefully acknowledged. Conflict of interest The authors declare that they have no conflict of interest. Ethical approval All procedures performed in this and the studies that provided data involving human participants were in accordance with the ethical standards of the Lothian Research Ethics Committee (09/S1101/ 54, LREC/2003/2/29, REC 09/81101/54), the NHS Lothian R + D Office (2009/W/NEU/14) and the Multi-Centre Research Ethics Committee for Scotland (MREC/01/0/56) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Compliance with ethical standardsFunding This study was funded by the Row Fogo Charitable Trust (Grant No. BRO-D. FID3668413). The Stroke sample data acquisition and processing was funded by The Wellcome Trust (Ref. No. 088134/ Z/09/A). Funds from the European Union Horizon 2020 BSVDs@target( Ref. PHC-03-15, Project no. 666881), the Innovate UK (Ref. 46917-348,146) and Fondation Leducq (Ref no. 16 CVD 05) are also gratefully acknowledged. The Lothian Birth Cohorts (LBC) data collections were supported by Research Into Ageing and continues as part of The Disconnected Mind project, funded by Age UK, with additional funding from the UK Medical Research Council under grant numbers G0701120, G1001245 and MR/M013111/1. Magnetic resonance image acquisition and analyses were conducted at the Brain Research Imaging Centre, Neuroimaging Sciences, University of Edinburgh (www.
All measurement were performed using the highest possible mass resolution (R=1. 4•10 7 at m/z 200). Mass calibration was performed daily for the m/z range <500 using sodium acetate, as described in Rossignol et al. 4  Data were processed and evaluated by XCalibur 2. 2 (Thermo, USA). 8] [39]  Formula assignments of the identified signals were achieved using a mass tolerance of 2 ppm and the following restrictions for elemental counts: C (1-50), N (0-5), H (0-100), O (0-40), S (0-2). Moreover, element count heuristics were applied, 38 and ring double bond equivalents were restricted to 0-25. Further details on LC-MS data processing are given in the supplementary information (Table  S -3).
Acknowledgements This study was supported by the  European Research Council  under the European Union's  Framework Program  ( FP/2007-2013) / ERC Grant Agreement 290852 -AIRSEA. This  project has also received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 730997  ( EUROCHAMP2020 ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  690958. The authors are grateful to  Antoinette Boréave  and  Nicolas Charbonnel  for the technical support provided by the  Institut de Recherches sur la Catalyse et l'Environnement de Lyon (IRCELYON).
Heterozygous mutations have been detected in familial forms of pulmonary fibrosis involving TERT (∼15%), RTEL1 (5-10%), PARN (∼5%) and TERC (∼3%). Mutations in DKC1, NAF1 and TINF2 are much rarer (figure  1 )  [9, 23, [28] [29] [30] [31] [32] [33] [34] [35]. TERT or TERC mutations may be found in about 1-3% of sporadic IPF cases  [28]. None of these genes is the site of a frequent mutation, and new genetic variants are continually being identified (e. g. as reported in the Telomerase Database, http://telomerase. asu. edu/diseases. html). The penetrance (risk of pulmonary fibrosis developing in a carrier of TERT and TERC mutations) depends on several factors, including environmental exposure  [28, 36]. The gene-environment interaction reported for carriers of TERT or TERC mutations may also be implicated for carriers of RTEL1 mutations [9].
The combination of one moderate criterion and at least four supporting criteria for pathogenicity leads to a conclusion of "likely pathogenic"  [115]. Some laboratories may choose to have a subclassification within the group of VUS. For instance, finding a variant leading to a stop codon within the TERT gene allows for concluding a pathogenic variant. However, a never-reported missense variant, absent from the control database, must be considered a VUS in the absence of functional and segregation data. Indeed, genetic testing for poorly selected patients will greatly increase the rate of VUS detected  [107].
Previous ChIP sequencing (ChIP-seq) studies have suggested that transcriptional enhancers are associated with high levels of H3K4me1  (Heintzman et al. , 2009). Pekowska et al. (2011)  further demonstrated that H3K4me1 is not indicative of enhancer activity but that there is a functional link between enhancer activity and H3K4me3 enrichment. Interestingly, the presence of LSD1 positively correlates with a strong increase in the activation mark H3K4me3 (Figure  2B ) in that region after 72 hr in DM. Consistently, by analyzing two published ChIP-seq datasets  (Asp et al. , 2011; Mousavi et al. , 2012) , we observed an enrichment of H3K4me3 in the CE region during myoblast differentiation (data not shown). Altogether, these results point to a central role of LSD1 in the activation of the CE region.
Chromatin IP and INPUT were extracted and dissolved in 10 mM TrisHCl (pH 8). Three sites, CE, negative [NEG], and TSS, were tested for real-time qPCR amplification. Real-time qPCR data analysis for LSD1 and RNApolII IPs has been performed by calculating the percentage of input for each genomic region; data are shown as the relative enrichment to the control genomic region (NEG region) that does not interact with the protein of interest. Real-time qPCR data analysis for H3, H3K9me2, H3K9me3, H3K4me1, and H3K4me3 IPs has been performed as described previously. Data were also normalized to the occupancy of H3 in each genomic region and shown as the relative enrichment to the control genomic region (NEG region). For oligonucleotides details, see the Supplemental Experimental Procedures.
This study was funded by grant  Agence Nationale de la Recherche  ( ANR-11-BSV2-017-01  to L. S. and F. R. ) by grants from the  European Research Council  ( ERC AdGrant 322844 ) and the  Deutsche Forschungsgemeinschaft  ( SFB 992 ,  850 ,  746 , and  Schu688/12-1  to R. S. ). I. S. was funded by  AFM.
Participants The Human Urine Proteome Database available at Mosaiques Diagnostics (Hannover, Germany)  17  includes anonymized clinical information on participants enrolled in several studies, as well as urinary proteomic signatures, including CKD273. These studies comply with the Declaration of Helsinki  18  and received ethical approval from the pertinent institutional review boards. All participants gave informed written consent. The Ethics Committee of the University of Glasgow authorized the current analysis (approval number 3115-2016). To be eligible for analysis, the following criteria had to be fulfilled: (i) eGFR at baseline of 60 ml/min per 1. 73 m 2 or higher; (ii) repeat assessment of eGFR during a follow-up of at least 2 years; and (iii) information available on clinically relevant covariables, including sex, age, systolic and diastolic blood pressure, serum creatinine, and albuminuria.
The number of participants in the Human Urine Proteome Database complying with the aforementioned eligibility criteria totaled 2087. Participants were: (i) patients enrolled in the Diabetes Retinopathy Candesartan Trials with type 1 (DIRECT1; n ¼ 740)  19  and type 2 (DIRECT2; n ¼ 618)  20  diabetes; (ii) patients with type 2 diabetes recruited into a Dutch study (PREvention of DIabetic ComplicaTIONS [PREDICTIONS])  21  aimed at identifying disease pathway-specific biomarkers (n ¼ 96), (iii) and patients with diabetes recruited from clinics in Australia (n ¼ 47)  22  and Hannover, Germany (n ¼ 22). 16 The remaining 564 analyzed individuals were enrolled in the Flemish Study on Environment, Genes and Health Outcomes (FLEMENGHO). 23 sessment of Renal Function Estimated GFR was calculated from serum creatinine by the Chronic Kidney Disease Epidemiology Collaboration equation.
Measurement of CKD273 Detailed information about urine sample preparation, proteome analysis by capillary-electrophoresis online coupled to electrospray micro time-of-flight mass spectrometry, data processing, and sequencing is available in previous publications. 27, 28 CKD273, measured at baseline, is a multidimensional classifier based on 273 urinary peptides derived by support vector machine modeling. 10, 13 For normalization of analytical and urine dilution variances, the mass spectrometric signals were normalized by using 29 internal standard peptides present in at least 90% of all urine samples with small SDs. 29 In previous studies,  11, 30  investigators proposed cutoff thresholds for CKD273 of 0. 154 and 0. 343 to predict early and advanced CKD.
Statistical Analyses For database management and statistical analysis, we used the SAS system, version 9. 4 (SAS Institute Inc. , Cary, NC). Means were compared by using the largesample z-test, and proportions, by the Fisher exact test. We rank normalized the distributions of albuminuria by sorting measurements from the smallest to the highest and then applying the inverse cumulative normal function. 31 hanges in eGFR during follow-up, as predicted by baseline UAE and CKD273, were assessed from mixed models adjusted for sex, age, mean arterial pressure (i. e. , diastolic blood pressure plus one-third of the difference between systolic and diastolic blood pressure), and history of diabetes mellitus (categorical variable coded 0 or 1). These mixed models accounted for the varying numbers of follow-up eGFR measurements and clustering of the observations within individuals and study centers.
The discrimination slope is the difference in predicted probabilities (%) between subjects with and without endpoint. To calculate NRI, we predicted in each participant the 5-year risk of a renal event from a Cox model with and without the biomarker. 32 If P (up/event) is the percentage of subjects with events whose predicted probability is increased by adding the biomarker to the model and if P (up/nonevent) is the percentage of subjects without events whose predicted probability is increased, then NRI ¼ 2 Â (P [up/event] -P [up/nonevent] ). Finally, we assessed the performance of published thresholds of UAE  26  and CKD273 11,30 from 2-Â-2 tables and applied the McNemar test for the pairwise comparison of proportions. Running a published SAS macro  33  over our current data, we determined thresholds for UAE and CKD273, yielding a specificity of 90%, and we compared the sensitivity of the so derived UAE and CKD273 thresholds.
Baseline UAE and CKD273 as Categorical Predictors In the next step of our analysis, we evaluated established thresholds of UAE  26  and CKD273  11, 30  as predictors of CKD progression. In all participants (Table  5 ), the UAE cutoff value of 20 mg/min and the CKD273 thresholds of 0. 154 and 0. 343 yielded sensitivities of 30%, 33%, and 42% and specificities of 82%, 83%, and 83%, respectively. The corresponding proportions in patients with diabetes were 8%, 23%, and 13% for sensitivity and 93%, 86%, and 94% for specificity (Table  5 ). In all participants and in patients with diabetes, differences between the UAE threshold and both CKD273 thresholds in the proportion of correctly versus incorrectly classified individuals were significant (P < 0. 0001). With specificity set at 90%, thresholds derived from the current dataset were 14 mg/min for UAE and 0.