dataesr/scientific-paragraphs-categorization huggingface dataset:
with a vanadium sphere served for detector efficiency corrections and to determine the energy resolution. The measured energy resolution can be described by a Gaussian function with a full width at half maximum FWHM = 0. 077 meV. The measured spectra were monitor normalized, corrected for detector efficiency including the energy efficiency changes of the Helium detectors, and then the measured background has been subtracted. After conversion from time of flight into energy transfer a constant energy binning ( E = 0. 01 meV) was applied and the spectra were sorted for constant Q values with a Q bin size of Q = 0. 1 Å-1. All these steps have been performed with the Lamp-software  [28]. Self-diffusion on long time and length scales can be described with the diffusion equation for the density correlation function of a tagged particle.
In solid silicon a surprising large mobility of nickel was recently found  [32] , perhaps related to mobile interstitial Ni. Wang et al. measured the surface tension in a liquid and undercooled Ni5%wtSi alloy. From these measurements viscosity and through the Stokes Einstein relation diffusion coefficients have been calculated  [14]. The temperature dependence of D was described by an Arrhenius law from the 200 K undercooled state up to 400 K above the liquidus temperature. Nevertheless, it appears that toward the lower temperature the data points deviate from the Arrhenius fit. Beyond these self-evident conclusions our data can be put into the wider context of the predictions from the MD simulations. The MD simulations of the Ni 75 Si 25 composition predicted a power-law behavior up to about twice the glass temperature T g , far above the melting temperature  [15].
Objectives: To systematically assess registration details of ongoing randomised controlled trials (RCTs) targeting 10 common chronic conditions and registered at ClinicalTrials. gov and to determine the prevalence of (1) trial records excluding patients with concomitant chronic condition(s) and (2) those specifically targeting patients with concomitant chronic conditions. Design: Systematic review of trial registration records. Data sources: ClinicalTrials. gov register. Study selection: All ongoing RCTs registered from 1 January 2014 to 31 January 2015 that assessed an intervention targeting adults with coronary heart disease (CHD), hypertension, heart failure, stroke/transient ischaemic attack, atrial fibrillation, type 2 diabetes, chronic obstructive pulmonary disease, painful condition, depression and dementia with a target sample size ≥100.
Strengths and limitations of this study ▪ This is the first study assessing the exclusion of patients with concomitant chronic conditions from ongoing randomised controlled trials (RCTs) registered in ClinicalTrials. gov and assessing interventions for patients with common chronic conditions. ▪ We systematically assessed exclusion criteria reported in trial registration records of RCTs assessing interventions for patients with common chronic conditions and registered in ClinicalTrials. gov according to the prevalence of the co-occurrence of chronic conditions. ▪ Results may not be completely consistent with the exclusion criteria reported in the trial protocol and may underestimate the prevalence of excluding patients with concomitant chronic conditions in trials. ▪ The characteristics of patients actually included in these trials were not assessed.
Search strategy We searched ClinicalTrials. gov on 4 March 2015, using the key words 'coronary heart disease', 'hypertension', 'heart failure', 'stroke', 'transient ischemic attack', 'atrial fibrillation', 'diabetes', 'chronic obstructive pulmonary disease', 'chronic pain', 'depression' and 'dementia' in the Conditions field; 'recruiting' in the Recruitment field; 'Interventional studies' for Study types; 'adult (18-65)' and 'senior (66+)' for the Age group; with no restriction on country; and 'from 1 January 2014 to 31 January 2015' in the First received field (ie, the date that summary clinical study protocol information was first submitted to the ClinicalTrials. gov registry). We exported all xml records from ClinicalTrials. gov and managed them by using R V. 3. 0. 1 (Team RC. R: a language and environment for statistical computing.
General characteristics of the studies From the records downloaded, we obtained the type of intervention (ie, drugs, device, surgery, behaviour, other), funding source, number of arms and target sample size. The primary outcomes were collected from the Primary Outcome Measures section of ClinicalTrials. gov and were classified as patient-important, surrogate and physiological or laboratory outcomes according to Gandhi et al. 12  A patient-important outcome was defined as 'a characteristic or variable that reflects how a patient feels, functions, or survives' (ie, all outcomes leading to important changes for patient life). 13.
Study selection and characteristics Among the 1612 records retrieved from ClinicalTrials. gov, 319 for ongoing RCTs were selected (figure  1 ). These trials represented a total target sample size of 237 544 patients. The RCTs targeted CHD in 36 (11%) trials, hypertension in 38 (12%), heart failure in 36 (11%), stroke/TIA in 22 (7%), atrial fibrillation in 25 (8%), diabetes in 109 (34%), COPD in 34 (11%), chronic pain in 12 (4%), depression in 40 (13%) and dementia in 7 (2%) (table  1 ). Interventions assessed were pharmacological in 147 trials (46. 1), and 107 (34%) were funded by industry. Outcomes were classified as patient-important outcomes in 199 (62%) trials.
17 Every individual recommendation included in a guideline may be rational and evidence-based, but the sum of all recommendations for patients with multimorbidity may not be. 11 Indeed, the inappropriate generalisation of trials results may lead to unintended harm. Research is particularly needed on the clustering of conditions in patients with multimorbidity. 18 Few trials are specifically targeting patients with concomitant chronic conditions. Our study has some limitations. First, we focused on only ongoing trials registered at ClinicalTrials. gov. Although ClinicalTrials. gov is the most commonly used register,  19  these records may be not representative of all ongoing trial records. Second, our results relied on exclusion criteria listed in trial records at ClinicalTrials. gov, which may not be complete or may have changed.
Conclusion Despite the high prevalence of multimorbidity, more than three-quarters of ongoing research registered at ClinicalTrials. gov and assessing interventions for patients with chronic health conditions excluded patients with concomitant chronic conditions. This finding could lead to a questionable generalisability of these results to the real-world population. Primary care research should be conducted according to the prevalence of multimorbidity in the real world. The research agenda should be revised to better account for the prevalence of concomitant chronic conditions. Figure  4  For each chronic condition targeted, the proportion of trials excluding patients with 1 of the 10 common chronic conditions selected according to the prevalence of the associations. For example, 61% of patients with stroke or transient ischaemic attack (TIA) also had hypertension, but 9% of the trials targeting patients with stroke/TIA excluded patients with hypertension.
Phenolic Compounds Analysis by HPLC-Fluorescence and HPLC-UV/VIS/ESI-MS(TOF) HPLC-fluorescence analyses were carried out using a binary HPLC pump (Waters 125) coupled to a scanning fluorescence detector (Waters 474) as in  Fourcroy et al. (2014). Separations were performed using an analytical HPLC column (Symmetry R C 18 , 15 cm × 2. 1 mm i. d. , 5 µm spherical particle size, Waters) protected by a guard column (Symmetry R C 18 , 10 mm × 2. 1 mm i. d. , 3. 5 µm spherical particle size, Waters) and a gradient mobile phase built with 0. 1% (v/v) formic acid in water and 0. 1% (v/v) formic acid in methanol (Elution program 1; Supplementary Table  S2 ). The flow rate and injection volume were 0. 2 ml min -1 and 20 µl, respectively. Phenolic compounds were detected using λ exc 365 and λ em 460 nm.
HPLC-UV/VIS/ESI-MS(TOF) analysis was carried out with an Alliance 2795 HPLC system (Waters) coupled to a UV/VIS (Waters PDA 2996) detector and a time-of-flight mass spectrometer [MS(TOF); MicrOTOF, Bruker Daltonics, Bremen, Germany] equipped with an electrospray (ESI) source. Two HPLC protocols were used, the one described above and a second one with a different elution program (Elution program 2; Supplementary Table  S2 ) designed to improve the separation of the phenolic compounds of interest. The ESI-MS(TOF) operating conditions and software used were as described in  Fourcroy et al. (2014). Mass spectra were acquired in positive and negative ion mode in the range of 50-1000 mass-to-charge ratio (m/z) units. The mass axis was calibrated externally and internally using Li-formate adducts [10 mM LiOH, 0.
0 bar, 4. 0 L min -1 and 200 • C, respectively. The mass axis was calibrated externally and internally as indicated above for the HPLC/ESI-MS(TOF) analysis. Molecular formulae for the product ions were assigned based on exact molecular mass with errors <5 ppm  (Bristow, 2006). HPLC/ESI-MS(ion trap) analysis was carried out with an Alliance 2795 HPLC system (Waters) coupled to an ion-trap mass spectrometer (HCT Ultra, BrukerDaltonics) equipped with an ESI source. The HPLC conditions were as described in  Fourcroy et al. (2014)  and Supplementary Table  S2  (Elution program 2). ESI-ion trap-MS analysis was carried out in positive and/or negative ion mode, the MS spectra were acquired in the standard mass range mode and the mass axis was externally calibrated with a tuning mix (Agilent). The HCT Ultra was operated with settings shown in Supplementary Table  S4.
Statistical Analyses Statistical analysis was carried out with SPSS for PC (v. 23. 0, IBM, Armonk, NY, USA), using ANOVA or non-parametric tests (p ≤ 0. 05), and a Levene test for checking homogeneity of variances. Post hoc multiple comparisons of means corresponding to each one the four different treatments were carried out (p ≤ 0. 05) using Duncan test when variances were equal and Games-Howell's test when variances were unequal.
Identification of Phenolic Compounds Induced by Fe Deficiency as Coumarins, Coumarin Precursors and Coumarinolignans To identify the compounds found in the A. thaliana root extracts and growth media, samples were analyzed using four different HPLC-UV/VIS/ESI-MS(TOF) protocols, including two Elution programs (1 and 2; Supplementary Table  S2 ) and two electrospray (ESI) ionization modes (positive and negative). The newly designed Elution program 2 led to a better separation of phenolic compounds than that obtained with the original Elution program 1 used in  Fourcroy et al. (2014). With the new elution program, RTs for a selected set of phenolics standards ranged from 8. 4 (for esculin, the glucoside form of the coumarin esculetin) to 51. 7 min (for the flavone apigenin) (Supplementary Figures  S1  and S2 ).
These HPLC/ESI-MS(TOF) analyses provided highly accurate (error below 5 ppm) measurements of the mass-tocharge (m/z) ratio of the detected ions, therefore allowing for accurate elemental formulae assignments  (Bristow, 2006). Raw MS(TOF) datasets (time, m/z and ion intensity) from the root extracts and nutrient solutions from Fe-deficient and Fe-sufficient plants were first analyzed with the DISSECT algorithm (Data Analysis 4. 0; Bruker) to obtain mass spectral features attributable to individual compounds. From a total of approximately 180 possible mass spectral features analyzed per run and sample, only 18 complied with the following two requirements: (i) occurring at chromatographic RTs where absorbance at 320 nm was observed, and (ii) showing peak area increases (or appearing) with Fe-deficiency.
Coumarins and Coumarin-Precursor Hexosides Not Previously Reported in Arabidopsis upon Fe-Deficiency Three of the compounds (10, 12, and 13) were identified as ferulic acid, coniferyl aldehyde and sinapyl aldehyde (three phenylpropanoid precursors; Figure  1B ), respectively, by comparing the MS spectra of the analytes and those of standards: there was a good match of the RT values and exact m/z ratios of the [M+H] + and [M-H] -ions (Tables  1  and 2 ) as well as of the MS 2 spectra of the [M+H] + ions (Tables  2  and 3 ). 2014 ): plants were pre-grown for 29 days in the presence of 45 µM Fe (III)-EDTA at pH 5. 5, and then grown for 7 days in a medium with 0 (-Fe) or 45 µM Fe (III)-EDTA (+Fe) (the pH was not readjusted to 5. 5, with the final pH being c. 7. 0 in all pots). Chromatograms were obtained using Elution program 1.
Cleomiscosins C and D (regioisomers -also called constitutional isomers-arising from the fusion of fraxetin and the monolignol sinapyl alcohol through a dioxane bridge; Figure  1C ) have a formula identical to that of 15-16 (C 21 H 20 O 9 ), cleomiscosins A and B (regioisomers arising from the fusion of fraxetin and the monolignol coniferyl alcohol through a dioxane bridge; Figure  1C ) have a formula identical to that of 17-18 (C H 18 O 8 ), whereas 5 -hydroxycleomiscosins A and B (regioisomers arising Numbers in italics (Compound #) refer to the labels used for each compound in Table  1. All data were taken from the HPLC-ESI-MS/MS(ion trap) analysis. The major ion of the MS 2 and MS 3 spectra is also indicated in bold. from the fusion of fraxetin and the monolignol hydroxyconiferyl alcohol, Cheng and Chen, 2000, Figure  1C ), have a formula identical to that of compound 14 (C 20 H 18 O 9 ).
The structural differences among these coumarinolignans -corresponding to the monolignol moiety (Figure  1B )are identical to those found among the elemental formulae of 14-18: (i) a methoxy group differentiates coniferyl from sinapyl alcohols and the elemental formula of 17-18 from that of 15-16; (ii) a hydroxyl group differentiates hydroxyconiferyl from coniferyl alcohols and the elemental formula of 14 from that of 17-18; and (iii) a methyl group differentiates hydroxyconiferyl and sinapyl alcohols and the formula of 14 from those of 15-16. To confirm the identification of 15-18 as cleomiscosins, we isolated coumarinolignans from C. viscosa seeds. The seed isolate was analyzed by both HPLC-UV/VIS/ESI-MS(TOF) and HPLC/ESI-MS(ion trap) using Elution program 2 and positive ESI ionization. The HPLC/ESI-MS(TOF) chromatogram for m/z 417.
Datasets In the present study, we collected long daily precipitation time series maintained by the governmental hydrological services of Algeria [Agence Nationale des Ressources Hydrauliques (ANRH)], Morocco [Direction de la Recherche et de la Planification de l'Eau (DRPE)], and Tunisia [Direction Générale des Ressources en Eau (DGRE)]. The daily data of the Melilla station located in northern Morocco were obtained from the European Climate Assessment and Dataset (ECAD; http://eca. knmi. nl). The data from each station were carefully scrutinized, in particular to look for shifts, absurd values, and missing data  (Tramblay et al. 2013). The stations that were subsequently selected had less than 5% missing days between September and May. The years with more than 5% missing days during this period were discarded.
The gridded NCEP-NCAR data (six grid cells covering the study area) were interpolated by the inverse distance method to the station locations in order to provide individual descriptor sets for each station. The selected variables include the following: d geopotential height at 500 and 850 hPa (geopot_500 and geopot_850), d vertical velocity at 500 and 850 hPa and at surface (omega_500, omega _850, and omega _surf), d potential temperature at surface (ptemp_surf), d precipitable water content (pwater_surf), d relative humidity at surface (rhum_surf), d specific humidity at 500 and 850 hPa (shum_500 and shum_850), d mean sea level pressure (slp_surf), d surface temperature (temp_surf), d zonal wind at surface (uwind_surf), and d meridional wind at surface (vwind_surf). The homogeneity of these covariates has been assessed by following  Pettitt (1979)  and by using the modified version of the standard normal homogeneity test (SNHT) by  Khaliq and Ouarda (2007).
However, the stationarity assumption is often not met for observed hydroclimatic datasets  (Khaliq et al. 2006). In this case, the distribution parameters and the distribution itself could be changing in time. Therefore, it is essential to develop the GEV model in the multivariate space, where extreme events can be associated with other variables. To model the relationship between the covariates and the extreme variable of interest, we can use the GEV B-spline approach  (Nasri et al. 2013). This approach has been developed to describe the association of an external covariate with the variable of interest. The estimation of the parameters of the GEV B-Spline model is done in a Bayesian framework to obtain the posterior distribution by applying Markov chain Monte Carlo (MCMC) algorithms.
b. Predictors from reanalysis data We selected the 14 NCEP-NCAR covariates extracted from reanalysis (see section 2) and tested our models with these covariates considering the four time scales (cases 1-4). The negative log likelihood and deviance between the GEV B-spline model and the stationary GEV model are analyzed to detect the influence of NCEP-NCAR predictors on extreme rainfall for each of the four cases. To avoid overfitting, each covariate is considered separately in the GEV B-spline model. This allows evaluating whether each covariate leads to a better fit than the stationary GEV model. As there are 14 covariates for each case, the results are presented in Table  2  only for the significant covariates on MAP at each station at the 5% and 10% significance levels, according to the test of deviance.
c. Principal analysis of components for NCEP-NCAR predictors After the analysis of the dependence of MAP with individual covariates, the possible relationships are also investigated in a multivariate context. Principal component analysis (PCA; Preisendorfer 1988) is used to that end. The reason for using PCA is to take into consideration the common signals in multivariate datasets. PCA represents a method for dimensionality reduction. PCA has been used for this purpose in many other studies (e. g. ,  Wetterhall et al. 2005; Maraun et al. 2010a ). The objective of this analysis is to summarize as much information as possible by transforming interrelated variables into new components (principal components) that are uncorrelated with each other. In this study, we first applied PCA on the 14 covariates for each station.
Consequently, an alternative to this type of model is quantile regression methods  (Buchinsky 1998 ). Unlike linear regression, which results in the estimation of the conditional mean for the response variable given certain values of predictor variables, quantile regression aims at estimating either the conditional median or other quantiles of the response variable. Quantile regression was considered by  Jagger and James (2006)  for wind speed and by  Friederichs and Andreas (2008)  for precipitation, based on several climatic covariates. Future work can focus on the comparison of extreme value models and the quantile regression approach to distinguish the relative benefits of the use of these two types of models for downscaling purposes. Acknowledgments. The datasets were provided by the Agence Nationale des Ressources Hydrauliques (Algeria), Direction de la Recherche et de la Planification de l'Eau (Morocco), Direction Générale des Ressources en Eau (Tunisia), and European Climate Assessment and Dataset.
Acknowledgements. We thank the referee for providing very valuable comments which significantly improved the quality of the manuscript. MK acknowledges support from  DLR  grant  50OR1802. GRT acknowledges support from the  NASA  through  Einstein Postdoctoral Fellowship Award Number   PF-150128 , issued by the Chandra X-ray Observatory Center, which is operated by the  Smithsonian Astrophysical Observatory  for and on behalf of  NASA  under contract  NAS8-03060. MG is supported by the  Lyman Spitzer Jr. Fellowship (Princeton University ) and by  NASA Chandra  grants  GO7-18121X/GO8-19104X. SMC acknowledges support from the  Australian Research Council  ( DP190102714 ). We thank  Alex Markowitz  for helpful discussions on the RGS data in the context of warm absorbers. The work of SAB, CPO and MS was supported by a generous grant from the  Natural Sciences and Engineering Research Council of Canada.
N. parvum The N. parvum isolate (''Cou 02") was selected from the INRA-UMR 1065 SAVE collection, Bordeaux. This strain was originally obtained in 2008 from a Cabernet Sauvignon cultivar in an experimental INRA vineyard near Bordeaux and was characterized as highly aggressive in previous preliminary studies at INRA. The GenBank accession number for rDNA (ITS, 5. 8S and ITS2) of N. parvum strain is KT306957. The strain was subcultured on MA and incubated at 22 °C (12 h light/12 h dark) for 1 week before being used for artificial inoculation in bioassays with berries and cuttings.
Bacterial strains A total of 46 bacterial strains, all isolated from grapevine, including 35 strains from wood tissue and 11 from the grape berry surface, were tested (Table  1 ). The GenBank accession number for 16S rDNA of all the strains are provided in Table  1. The 11 strains isolated from the grape berry surface originated from ''Biological Resources Center for Enology" (University of Bordeaux and Bordeaux Polytechnic Institute). Strains were maintained on Viabank cryogenic storage beads at À20 °C. Before being inoculated inside the plant host, and depending on the bioassay, bacteria were grown in glass tubes (40 mL) or in Corning cell culture flasks for 24 h at 28 °C in the dark in tryptic soy broth (TSB, Difco) or Luria-Bertani (LB) liquid medium (Sigma) depending on the assay. Bacterial growth was determined by comparing the turbidity with a McFarland standard solution (bioMérieux Ò SA), which is equivalent to 10 8 -10 9 CFU/mL (4 on the McFarland scale).
Acknowledgments In Bordeaux , this study was carried out in the framework of the Cluster of Excellence COTE. The authors are grateful to the  Syrian government  for a Ph. D. grant to  Rana HAIDAR  and for financial support through the  CASDAR  project ( V1302 ). We thank the  ''Biological Resources Center for Enology" (University of Bordeaux  and  Bordeaux Polytechnic Institute ), particularly  Prof. A. Lonvaud , for providing some of the bacterial strains. We are also indebted to the  INRA UMR-Save  staff:  G. Comont ,  J. Jolivet  and  S. Gambier  for their valuable help with strains furniture, laboratory and greenhouse assays. The manuscript was checked for English with the Elsevier web shop service before submission.
Test code Bacterial species Bacterial origin Symptomatic/asymptomatic plant Wood tissues GenBank accession number S1 Pantoea agglomerans Grape berries NA Surface HE648133. 1 S2 Pantoea agglomerans Grape berries NA Surface HE648432. 1 S3 Pantoea agglomerans Grape berries NA Surface HE648134. 1 S4 Pantoea agglomerans Grape berries NA Surface HE588003. 1 S5 Pantoea agglomerans Grapevine wood Asymptomatic Necrotic cordon KT363714 S6 Pantoea agglomerans Grapevine wood Asymptomatic Non-necrotic cordon KT363715 S7 Bacillus pumilus Grapevine wood Asymptomatic Necrotic cordon KT363710 S8 Pantoea agglomerans Grapevine wood Asymptomatic Necrotic cordon KT363691 S9 Paenibacillus turicensis Grape berries NA Surface HE587983. 1 S10 Paenibacillus turicensis Grapevine wood Symptomatic Non-necrotic cordon KT363708 S11 Paenibacillus turicensis Grapevine wood Symptomatic Non-necrotic Cordon KT363709 S12 Paenibacillus illinoisensis Grape berries NA Surface HE587985.