dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, measurement error in collecting self-reported dietary intake is inevitable. To minimize this, we used country-specific validated questionnaires for main food groups and nutrients  (14, 15) , although these have not been specifically validated for the intake of flavonoids. Moreover, flavanol and flavonol intake may be underestimated, although our database was mostly complete for these flavonoid subclasses  (11, 12) , and herb/plant supplement intakes were omitted in these analyses (up to 5% in Denmark, the highest consuming country)  (38). Nutritional biomarkers offer an alternative method for estimating dietary intake that is objective rather than subjective, and they provide more accurate measures than self-reported questionnaires. To date, there are few validated biomarkers of flavanol and flavonol intakes, so further research in this field is warranted  (39, 40).
Acknowledgments The authors thank  Nicola Kerrison  ( MRC Epidemiology Unit, Cambridge ) for managing the data for the EPIC-InterAct Project. R. Z. -R. designed the research, analyzed the data, and wrote the manuscript; N. G. F. designed the research and wrote the manuscript; S. J. S. analyzed the data and reviewed and edited the manuscript; C. A. G. designed the research and reviewed and edited the manuscript; B. B. , M. G. , and Y. T. v. d. S. contributed to the discussion and reviewed and edited the manuscript; P. A. , H. B. , L. B. , G. F. , E. J. F. , P. W. F. , S. G. , V. K. , T. J. K. , K. -T. K. , T. K. , G. M. , A. M. , E. M. -M. , P. M. N. , K. O. , F. P. , M. L. R. , F. R. , O. R. , I. R. , N. R. , A. S. , M. S. , N. S. , A. M. W. S. , A. T. , M. J. T. , M. T. , R. T. , D. L. v.
The swarm of schedulers is connected to the legacy systems, namely database and ontology services, enabling the schedulers to retrieve required data from these sources. When considering the design of adaptive scheduling systems, as systems of systems based on a swarm of schedulers, a pertinent question is how to define swarms to scheduler S (1)   scheduler S (2)   scheduler S (4)   scheduler S (k)   scheduler S (3)   balance interests of selfish individuals and groups of individuals. Two distinct strategies can be implemented:  The scheduling problem may be divided into several smaller scheduling problems according to a proper logical dependency, e. g. a workshop scheduler can comprise several station schedulers.  The scheduling problem may be divided in a way that different schedulers can be used to search faster alternative solutions in parallel, e.
Experimental data from literature The numerical model presented here, proposes to study the mechanical behavior of lightweight concrete. In order to compare, and validate this model, experimental data are needed. Experimental works of  [21] [22] [23] , provides data on lightweight concretes, especially on elasticity parameters and compressive strengths. In concretes considered here, Young's modulus of mortar is higher than the modulus of aggregates. groups of aggregates are used, called 430A and 750S, dispersed in the mortar with the compacities g ¼ 12:5% and g ¼ 45%. These aggregates are embedded by two mortars, called by the authors M8 and M10. Young's modulus of these two mortars have been determined experimentally by compression tests. Values of Young's modulus of lightweight aggregates, have been carried out using the phenomenological relationship  (6)  provided by  [23, 29] : E agg ¼ 8000 Â q 2 G ð6Þ where Eagg is Young's modulus of lightweight aggregates and q G the density determined experimentally.
2010) , with the PS1 catalog acting as the astrometric reference catalog. We then used the software packages SExtractor and SCAMP  (Bertin 2006)  to align each individual frame to the frame with the best seeing before constructing composite images for each band by average combining them with SWARP  (Bertin 2006). The resulting BV R image of PSO J174. 0675-10. 8774 is shown in Figure  2  and unequivocally confirms that it is a compact stellar system. We performed the photometry using the daophot/allstar/allframe suite of programs  (Stetson 1994)  as described in  Martin et al. (2013, E. J. Bernard, in preparation). A point-spread-function model was built for each individual frame and the flux was extracted by applying the model to fixed positions of stars as measured from the deep BV R image.
PROPERTIES OF THE STELLAR SYSTEM A search for possible RR Lyrae stars varying in the multi-epoch PS1 photometry revealed no good candidate. However, although the CMD of PSO J174. 0675-10. 8774 is only sparsely populated, the presence of a well defined (red) HB allows for an accurate determination of the heliocentric distance to the system. We therefore measure the median apparent magnitude of the HB through a Monte Carlo resampling of the magnitude of stars in this part of the CMD, yielding m gP1 = 21. 41±0. 06. To determine the absolute magnitude of the red horizontal branch in this band, we extract from the PS1 stacked photometry database the photometry of four GCs that share the morphological properties and CMD of PSO J174. 0675-10. 8774 (Pal 3, Pal 4, Pal 5, and Pal 14). We repeat the same procedure on the red HB of these systems, shifted by their distance modulus as listed in  Harris (2010).
(2008) , updated to allow for a full Markov Chain Monte Carlo treatment. Briefly, the algorithm uses the location of every single star in the WFI data set to calculate the likelihood of a family of radial profiles with flattening and a constant background. The parameters are: the centroid of the system, its ellipticity 8  , the position angle of its major axis from N to E, the number of stars in the system, and one or two scale parameters. We use three different families of radial density models (exponential, Plummer, and King), for which the scale parameters are the half-light radius, the Plummer radius, and the core and King radii, respectively. The resulting structural parameters are listed in Table  1.
We would like to thank the editor and referee for their celerity. We wish to thank  P. Bianchini  and  M. A. Norris  for fruitful discussions about globular clusters and  R. A. Ibata  for a careful reading of the manuscript. B. P. M. L. acknowledges funding through a  2012 Strasbourg IDEX (Initiative d'Excellence ) grant, awarded by the  French ministry of education. N. F. M. and B. P. M. L. gratefully acknowledges the  CNRS  for support through  PICS  project  PICS06183. N. F. M. , H. -W. R. & E. F. S. acknowledge support by the  DFG  through the  SFB 881 (A3 ). E. F. B. and C. T. S. acknowledge support from  NSF  grant  AST 1008342. The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , the  Johns Hopkins University, Durham University , the  University of Edinburgh, Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.
, v k as embeddings, and then utilise k-means to cluster the points. From the section 2. 1 we've introduced our subregion CS-LBP methods to extract the eye appearance feature descriptor. Here we'd like to at first obtain eye manifold distribution by using Laplacian Eigenmaps, and then we apply the normalized spectral clustering. The Fig. 2 (a, b ) shows eye samples of the subject's eye movements when the subject follows the visual pattern(green points) shown in the screen. The Fig. 2  (c, d ) demonstrate the distribution of embeddings in the subspace. (c) gives the distribution of a dataset of 240 points which contains only the eye samples on the 8 points in the screen , while (d) contains only 120 eye samples from the 4 points in the corner(up right, up left, down left, down right). For a given number C of visual patterns, generally we can get l clusters U = {U 1 ,U 2 ,.
The MYH7 p. L1723P mutation was found in the three tested affected individuals but in none of the healthy family members or in databases listing human genome variations as dbSNP, 1000 Genomes or exome variant server. The missense mutation is predicted to be damaging by sorting tolerant from intolerant (SIFT) and PolyPhen. Exome sequence analysis did not reveal any other potentially pathogenic variation in known muscle disorder genes.
Acknowledgements We are grateful to patients attending the  Institute of Myology (Paris)  and the  Neurology service (Poitiers). We would like to thank  G Brochier ,  MT Viou ,  M Beuvin ,  A Madelaine  and  F Levy-Borsato  ( Institut de Myologie, Unité de Morphologie Neuromusculaire ) for their excellent technical assistance. Contributors NBR: Study design, performed clinical and muscle biopsies data and analysis, and wrote the manuscript. TX, US: Performed genetic data analysis. EM: Performed clinical data analysis and wrote the manuscript. JB, BW, FX: Contributed genetic data analysis. SB: Contributed muscle MRI analysis. SM, J-PN: Contributed clinical data. NM: Performed RYR1 study exclusion. MF: Performed clinical and muscle pathology data and wrote the manuscript. JL: Study design, performed genetic data analysis and wrote the manuscript.
Funding This work was supported by the  Agence Nationale de la Recherche  ( ANR-11-BSV1-026 ) and the  Assistance Publique-Hôpitaux de Paris  ( AP-HP ), the  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Centre National de la Recherche Scientifique (CNRS) ,  Strasbourg University, Collège de France ,  Association Française contre les Myopathies (AFM).
Experimental analysis of residues in the hot spot sub-network To test experimentally if the key hot spot residues identified above (linking elements within each community) contribute to allosteric signalling within PapC, we constructed a set of single alanine substitution mutations (Table  4 ). Each of the mutants was present at a similar level in the OM compared to the wild-type PapC usher, and the mutations did not affect the ability of the usher to form a stable b-barrel in the OM (data not shown). The functionality of the PapC substitution mutants was assessed by ability to assemble P pili on the bacterial surface. P pili bind to receptors on human red blood cells, and assembly of functional P pili was determined using a hemagglutination assay (HA). Seven (D249A, T331A, R442A, S444A, Y260A, K427A, T437A) of the 14 tested mutants exhibited greater than twofold defects in agglutination titers compared with wild-type PapC, with 4 of the mutants (D249A, R442A, Y260A, and K427A) exhibiting no agglutination activity (HA titer = 0) (Table  4 ).
Systems modelling We built four model systems based on the X-ray structure of the TD (residues 1-492) of PapC at 3. 2 A ˚resolution (PDB ID: 2vqi  [Remaut et al. , 2008] ). The starting model for the simulation of the native TD (sim1, native) was generated by adding the missing loops to the X-ray structure using the dope_loopmodel method  (Shen and Sali, 2006)  in MODELLER-9v7  (Sali and Blundell, 1993). Additionally, three mutant model systems were constructed based on the native model: a mutant lacking the bhairpin (D233-240) (sim2, hairpin mutant), a mutant lacking the a-helix (D447-460) (sim3, helix mutant), and a mutant lacking both (D233-240 and D447-460) (sim4, helix-hairpin mutant). Each of the systems was oriented with respect to the membrane normal (the Z axis by definition) using the database  (Lomize et al.
Sequence conservation analysis The sequence corresponding to the X-ray structures of PapC TD (PDB id: 2vqi; Uniprot id: P07110) was used as input to psiBlast resulting in a set of unique related sequences from the non-redundant NCBI data set  (Altschul et al. , 1997). The E-value threshold was set as 10 À3 and sequences with id >90% and <30% sequence identity were excluded. A structure-based multiple sequence alignment was calculated using Expresso (3DCoffee)  (Armougom et al. , 2006). Finally, an evolutionary conservation score was calculated for each residue an empirical Baysian inference method  (Mayrose et al. , 2004)  as implemented in the ConSurf web server  (Ashkenazy et al. , 2010).
5 ml of buffer T. Gentle stirring was applied to promote spontaneous insertions of the protein into the bilayer. Channel activity was monitored by measuring current under voltage clamp conditions using an Axopatch 1D amplifier with a CV4B headstage or an Axopatch 200B amplifier (Axon Instruments). The current was digitized (ITC-18; Instrutech) and stored on a PC computer using the Acquire software (Bruxton). 10-min long traces were sampled at 1. 25 ms intervals and filtered at 500 Hz. Both chambers contained buffer T and Ag/AgCl electrodes with pellet. The trans side of the bilayer was set as ground. Insertions were typically performed at À90 mV. Data display and analysis were done with pCLAMP software (Axon Instruments).
Acknowledgements The authors are grateful to  Dr David Houldershaw  for computing support, and  Drs Tsjerk Wassenaar  and  Paula Petrone  for useful discussions. The authors acknowledge the use of the  UCL Legion High Performance Computing facility , and associated services, in the completion of this work. The work was supported in part with  MRC  grant  018434  (to GW), a  Wellcome Trust PhD Training Programme  ( 086711/z/08/z ) (to IF),  MRC  ( G0600084 ) and  BBSRC  ( BB/K01692X/1 ) (to MT) as well as  National Institutes of Health  grant  GM062987  (to DGT).
, 2008; Trofimovs et al. , 2006 Trofimovs et al. , , 2010 Trofimovs et al. , , 2012 Trofimovs et al. , , 2013;; Cassidy et al. , 2013 , 2014 , Cassidy et al. , submitted manuscript, 2014]. This correlation has strengthened the stratigraphy of this area and added a spatial dimension to this data set that will enhance the ability to interpret sites U1394 and U1395 in future research. The primary goal of this contribution is to establish a robust chronology for the upper 7 m of Site U1396 and to determine the number and ages of visible marine tephra fall deposits and density-current deposits within this interval. The completeness of the record is also assessed by identifying major hiatuses. A secondary goal is to understand the origin of eruptive fall deposits and density-current deposits and the type of event that these deposits record.
AMS Radiocarbon Dating Three samples in the upper 1. 52 m of U1396C were selected for Accelerator Mass Spectrometry (AMS) 14 C dating. Analyses were run for samples at 0. 455, 0. 905, and 1. 515 m core depth, situated within the upper extent of hemipelagic sediment. Approximately 1000 specimens (17 mg) of sonically cleaned G. ruber (white and pink) of a size >150 lm were analyzed for each sample. Specimens showing any visual signs of diagenesis or reworking were not used for analysis. Analysis was carried out by Beta Analytic Inc. (Florida, USA) using their in-house protocols (www. radiocarbon. com). Results are reported as conventional radiocarbon years BP, expressed at the 61r level for overall analytical confidence. The AMS dates for 0. 455 and 0. 905 m have been calibrated against the Marine13 data set using CALIB 7.
Oxygen Isotope Stratigraphy and AMS Radiocarbon Dates Despite the fragmented record, the isotope profile for Hole U1396C (Figure  3 ) compares well to published data for this area  [Le Friant et al. , 2008; Trofimovs et al. , 2010 Trofimovs et al. , , 2013]] , as well as the standard planktic oxygen isotope curve constructed from  Imbrie et al. [1984] ,  Prell et al. [1986] , and  Martinson et al. [1987]. Oxygen isotope ratios differ slightly between Hole U1396C and CAR-MON 2, which is likely to be an artifact of differing methodologies. While isotope ratios for Hole U1396C were measured from a single species of planktic foraminifera, isotope ratios for CAR-MON 2 were measured using homogenized <63 lm sediment  [Le Friant et al. , 2008] , which contains a high proportion of calcareous nannofossils, as well as juvenile planktic and benthic foraminifera.
Data sources This study was conducted with the UK Clinical Practice Research Datalink (CPRD) and hospital episodes statistics (HES) database. The CPRD has medical information on more than 13 million individuals across 680 general practices, with similar age and sex distributions to those reported by the UK National Population Census. 26 Information collected includes demographic characteristics, diagnoses, and drug prescriptions issued by the general practitioners. Diagnoses and procedures are coded with the Read classification and prescriptions are coded according to the UK Prescription Pricing Authority Dictionary. Data collected in the CPRD have been validated and deemed to be of high quality. 27 Since 1997, the HES database collects information on dates of hospital admissions, discharge diagnoses (coded with ICD-10 (international classification of diseases, 10th revision)), and procedures (coded using the Office of Population Censuses and Surveys classification of interventions and procedures, 4th version).
All patients meeting the study inclusion criteria were followed until a diagnosis of acute pancreatitis (recorded in either the CPRD (on the basis of Read codes) or HES database (ICD-10 codes: K85. 0, K85. 1, K85. 2, K85. 3, K85. 8, K85. 9 in primary or secondary position)), death from any cause, end of registration with the general practice, or the end of the study period (31  March 2013), whichever came first.
We estimated the empirical covariates from five data dimensions (drug prescriptions, procedures, diagnoses, disease history, and administrative information), while the predefined covariates consisted of age, sex, duration of treated diabetes (defined as the time between a first ever non-insulin prescription and cohort entry), HbA1c, excessive alcohol use (based on diagnoses for alcohol related disorders, such as alcoholism, alcoholic cirrhosis of the liver, alcoholic hepatitis, and hepatic failure), body mass index (BMI), smoking status, history of gallstones, and use of antidiabetes drugs in the year before cohort entry (metformin, sulfonylureas, thiazolidinediones, insulins, and other agents). In addition to the tenths of hdPS, we adjusted the models for calendar year of cohort entry. Variables with missing data were coded with an "unknown" category.
Strengths and limitations of this study The strengths of our study include the use of a large population based cohort of patients with type 2 diabetes followed for up to 25 years in CPRD, a database that has been shown to be representative of the UK general population. Moreover, the CPRD allowed us to consider important potential confounders (such as BMI, excessive alcohol use, and HbA1c), which were included along with over 500 variables in the hdPS model. 44 In contrast with standard propensity score methods, the hdPS method emperically selects variables based on their prevalence and potential for confounding. 29 This particularity allows for the inclusion of potential confounders that might not been considered a priori. Finally, using a primary cohort of patients newly treated with non-insulin drugs allowed us to accurately calculate the duration of treated diabetes before cohort entry, a potentially major confounder.
This study does have some limitations. Drug exposures recorded in the CPRD are from prescriptions written by general practitioners, and the database does not record prescriptions written by specialists. We do not believe, however, that such missing information was differential between patients exposed to incretin based drugs and the active comparator group. Moreover, we do not know if patients complied with the treatment, although renewal of prescriptions is probably a good indicator of drug use. To our knowledge, the validity of the recording of acute pancreatitis in the CPRD has not been formerly validated, although it has been used previously in another unrelated study. 42 For this reason, we used the HES database as an additional data source to capture this outcome, which probably maximised the validity of the case ascertainment.
icmje. org/coi_disclosure. pdf (available on request from the corresponding author) and declare: database acquisitions were funded by the Canadian Institute of Health Research (CIHR) and the Canada Foundation for Innovation; SS has received research grants, participated in advisory board meetings and/or as a speaker at conferences for AstraZeneca, Bayer, Boehringer-Ingelheim, Bristol-Myers-Squibb, Merck, and Novartis, all of which except Bayer are manufacturers of incretin based drugs, though none of these activities were related to diabetes or incretins, with the exception of a single participation in a mock advisory panel for Bristol-Myers-Squibb. Either acute or chronic pancreatitis. †Measured in year before cohort entry.
Competing interests: All authors have completed the ICMJE uniform disclosure form at www. icmje. org/coi_disclosure. pdf (available on request from the corresponding author) and declare: database acquisitions were funded by the Canadian Institute of Health Research (CIHR) and the Canada Foundation for Innovation; SS has received research grants, participated in advisory board meetings and/or as a speaker at conferences for AstraZeneca, Bayer, Boehringer-Ingelheim, Bristol-Myers-Squibb, Merck, and Novartis, all of which except Bayer are manufacturers of incretin based drugs, though none of these activities were related to diabetes or incretins, with the exception of a single participation in a mock advisory panel for Bristol-Myers-Squibb. Either acute or chronic pancreatitis. †Measured in year before cohort entry.
This requires a sound understanding not only of the conceptual and methodological issues that might limit our ability to make the best use Figures of current observational dataset (see Fig. 2 ), but also in-depth understanding of the processes and structures of the models under investigation. The aim of this review is to assess and list key-issues and challenges that need to be taken into consideration if the uncertainties in future carbon-climate projections are to be reduced; with particular focus on the terrestrial and marine ecosystems component of ESMs. We begin by discussing selected process uncertainties (Sects. 2 and 3). Key issues which impact the use of observations to constrain models are discussed in Sect. 4. Next, we discuss different levels of uncertainty that impact our ability to infer future reliability of models based on their past/present-day performances (Sect.
Constraining future projections based on observations: data and metrics Given the divergence of model outcomes for the contemporary period  (Anav et al. , 2013)  and in the future (e. g. Bopp et al. , 2013; Jones et al. , 2013) , it is imperative to have a good understanding of the available, appropriate datasets and methodology to evaluate models  (Foley et al. , 2013). The development of a constraint based on observations should benefit from the richness of available datasets. Nevertheless, uncertainties of the data set and their use as a constraint may limit the application of a particular data set as a constraint. Some of these uncertainties and/or limits are also common to the general model evaluation and calibration problems, such as data uncertainties (see the review of  Foley et al.