dataesr/scientific-paragraphs-categorization huggingface dataset:
The clinical behavior of ER-positive and ER-negative breast cancers differ significantly  (36) , and it is plausible that they respond differently to alcohol and its metabolites. Furthermore, any observed association may be confounded by the different risks of the different subtypes. Publication bias should always be considered in a review of published data, but, given the heterogeneity of results with reported effects in both directions, it seems unlikely to be important here. The SEARCH, EPIC, and BCAC case cohort results are considerably less heterogeneous. We used consistent definitions of alcohol intake, and the analysis approach was the same for all three datasets. In particular, we performed separate analyses for ER-positive and ERnegative disease, separately and we adjusted for multiple potential confounders.
For women with ER-positive disease, there was little evidence that pre-or postdiagnosis alcohol consumption is associated with breast cancer-specific mortality, with some evidence of a reduction in all-cause mortality. On the basis of these findings, it is unlikely that alcohol intake has an adverse effect on survival of women with ER-positive breast cancer. For women with ER-negative disease, the SEARCH data suggest that moderate postdiagnosis alcohol intake may be associated with a small reduction in breast cancer-specific mortality, whereas the EPIC and BCAC results suggest no association for prediagnosis intake. These differences may be because if there is a true association with postdiagnosis alcohol intake, it may have been missed when using prediagnosis alcohol intake as a proxy for postdiagnosis intake.
Screening is another potential confounder as screen-detected cases show longer survival. Data on mode of detection (clinical or screening) were available for patients in SEARCH. In the SEARCH study, the effect of reported alcohol intake was the same in women with screen detected and clinically detected cancers (data not shown). Considering the totality of the evidence, moderate post-diagnosis alcohol consumption is unlikely to have a major adverse effect on survival of women with breast cancer. Given the methodologic problems of studying alcohol intake and breast cancer prognosis in simple observational studies, the best approach for resolving the issue would be to embed serial dietary and lifestyle assessment within randomized controlled treatment trials. HRs for prediagnosis alcohol consumption and overall mortality (moderate drinkers vs.
Acknowledgments The authors thank the following individuals without whom this study would not have been possible: the participating patients;  Stephen Kaptoge ,  Robert Luben ,  Amit Bhaniani  (EPIC); the  SEARCH team (SEARCH) ;  Ursula Eilber ,  Sabine Behrens  (GESBC);  Karl von Smitten ,  Sofia Khan ,  Irja Erkkilä ,  Virpi Palola  (HEBCS); and  Maggie Angelakos ,  Judi Maskiell , and  Gillian Dite  (ABCFS). Grant Support:  A. M. G. Ali  was supported by a studentship from the  Citadel Capital Foundation ,  Egypt. M. Garcia-Closas  was funded by the  Breakthrough Breast Cancer Research Centre, London ,  United Kingdom. J. L. Hopper  is a  National Health and Medical Research Council (NHMRC)   Australia Fellow  and a  Victorian Breast Cancer Research Consortium Group Leader.
V. (70492) ;  European Community 's  Seventh Framework Programme  under grant agreement  223175  ( HEALTH-F2-2009-223175 );  FIS Intrasalud  ( PS09/02368 );  Grant-in-Aid for Scientific Research on Priority Areas  and on  Innovative Area ,  Ministry of Education, Science, Sports, Culture and Technology of Japan ;  Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control ,  Ministry of Health, Labour and Welfare, Japan ;  Health Strategic Action Instituto de Salud Carlos III  ( FIS PI12/02125 ); the  Hellenic Health Foundation (Greece) ; the  Helsinki University Central Hospital Research Fund , the  Academy of Finland  ( 132473 ),  Norwegian Research Council  and  Norwegian Cancer Society ; the  Sigrid Juselius Foundation , the  Finnish Cancer Society ;  Herlev Hospital , the  Danish Medical Research Council, Chief Physician Johan Boserup and Lise Boserup's Fund.
To this end, a cylinder was inserted into the wind tunnel such that the formation of a turbulent Kármán vortex street was caused. The current paper considers this configuration. Water droplets were injected into a Göttingen-type wind tunnel with a closed test section. In between the injection point and the test section, the cylinder was inserted. Experimental data of both phases, e. g. , velocities of air and droplets, were determined with non-intrusive measurement techniques. These time-averaged data were used as boundary conditions for the model and for comparison with the results from the numerical simulations. All measurement data are collected in an online database accessible at http://www. ovgu. de/isut/lss/metstroem. The considered process can be found as M3 in this database. The raw measurement results were further post-processed, so that all required data are in a suitable form for comparisons and validation.
38 10 -23 J /K is the Boltzmann constant and µ = 18. 15 • 10 -6 kg /ms is the dynamic viscosity. The model parameters C brown and C shear were unknown and they will be determined by calibrating experimental and numerical data. Also equation (2) for the DSD has to be equipped with appropriate boundary and initial conditions. The choice of the boundary condition for the droplets at the inlet of Ω DSD was based on experimental data. Measurements were performed at a grid of nodes at the first measurement plane x = 0 m. For a detailed description of the conversion of the experimental data to values for the DSD in these nodes, denoted by f in,exp (x, d), and the corresponding standard deviation σ f (x, d), it is referred to  Bordás et al. (2012). The values in the nodes were interpolated to the whole measurement plane and the boundary condition was set to be f (t, x, d) = f in,exp (x, d) + rand normal (t, x)σ f (x, d), for d ∈ [d min , d max ], 0, for d ∈ [d min,art , d min ), (4 ) with x = (0, y, z), t ∈ [0, t e ], and rand normal denotes a normally distributed random variable.
(2013b). The properties of the disperse phase (water spray) were then measured separately in the three vertical planes at x = 0 m, x = 0. 2 m, and x = 0. 4 m, using the same measurement grid as previously. Velocities measured by PDA are based on the same principles as LDV. However, using PDA, the simultaneous measurement of the diameter and the velocity values was possible. Calculating the droplet Stokes number St from the droplet properties, the Kolmogorov length scale and the RMS velocity fluctuations, the most frequent value of St = 2•10 -3 in a range of St = 10 -3. 10 2 was found. At the outlet boundary (x = 0. 4 m), experimental data were needed as well for comparison purposes. Therefore, a corresponding postprocessing of the PDA measurements was necessary to obtain values for the number density or droplet concentration.
Altogether, there were 4 086 650 degrees of freedom for the TVD-ENO and CN-GFCT discretizations of the DSD equation. The length of the time step was always set to be ∆t = 10 -3 s. The simulations were performed with the research code MooNMD  John and Matthies (2004). First, the flow field will be considered. Behind the cylinder, a turbulent Kármán vortex street is developing, see Fig. 4. As already mentioned above, experimental data for the time-averaged velocity component in flow direction were available at the plane x = 0 m, see Fig. 2. In Fig. 5 , the time-averaged velocity that was obtained in the simulations at x = 0 m is shown. One can observe a very good agreement. The values are in the same range and the slight asymmetry observed experimentally is well reproduced. Thus, one can conclude that the choice of the inlet condition as described in Section 3 was appropriate and also that the simulation of the turbulent flow field with the VMS method was accurate, at least with respect to the time-averaged velocity.
Summary and Outlook This paper studied the behavior of a disperse droplet population in a turbulent air flow. Inserting a cylinder into a wind tunnel, a turbulent vortex street was generated. Data of the velocities of air and droplets were obtained by nonintrusive measurement techniques. The process was modeled with a population balance system. Simulations of this model were performed by using modern numerical methods. Two unknown model parameters had to be calibrated. Very good agreements between experimental and numerical data could be observed for the mean velocity in a cut plane and for the space-time averaged droplet size distribution at the outlet. A possible improvement of the collision kernel for the coalescence integrals should be considered in future. The calibration of one of the parameters resulted in a different order of magnitude for the considered vortex street and a channel flow in previous simulations.
Using a cutoff threshold of 0. 6 for pairwise Pearson correlation coefficients in the public expression database ATTED-II, a total of 251 genes with a frequency of occurrence of 1059 times were identified as coexpressed with the 16 "guide genes," among them, 43. 8% (110/251) genes appeared only once with one guide gene, while 98 genes were present more than four times with 16 guide genes (Figure  1C ). Furthermore, their expression pattern was confirmed as consistent with the guide genes using the Arabidopsis eFP browser (Figure  1B ). Additionally, 11 genes with functional relevance, i. e. , b-Ketoacyl-CoA synthases (KCSs; KCS7, 15, and 21), Lipid Transfer Protein (LTP), Extra Cellular Lipases (EXLs; EXL4, 5, and 6), Anther-specific protein6 (A6), CYP450 encoding genes (CYP703A2), AMS, and AtbHLH089, were further validated by quantitative RT-PCR (qRT-PCR) as highly expressed during stage 9 of anther development (Figure  1D ).
Coexpression Analysis in Arabidopsis thaliana and Cluster Analysis The Arabidopsis genes in the manually curated abstracts/full text articles that contain experimental evidence for pollen wall biosynthesis genes were used as bait genes to query the ATTED-II database  (Obayashi et al. , 2009). The expression pattern analysis was performed using the Arabidopsis eFP browser (http://bar. utoronto. ca/efp/cgi-bin/efpWeb. cgi)  (Winter et al. , 2007). Microarray data analysis employed Genesis (1. 7. 5) software  (Sturn et al. , 2002). The Web-based Classification SuperViewer program (http://bar. utoronto. ca/ntools/cgi-bin/ntools_classification_superviewer. cgi) was used to search for differential distributions of Gene Ontology and biological terms within the correlated genes.
ACKNOWLEDGMENTS We acknowledge the  Nottingham Arabidopsis Stock Centre  for supply of seed stocks, and the bioinformatics supports by  Jie Zong  ( Novel Bioinformatics ). This work was supported by funds from the  National Natural Science Foundation of China  ( 31110103915 ,  31000593 , and  31370026 ); the  Shanghai Committee of Science and Technology, China  ( 11JC1404900 ); the  China Innovative Research Team, Ministry of Education , and  111 Project (B14016 ); the  National Key Basic Research Developments Program, MOST, China  ( 2013CB126902  and  2009CB941500 ) to D. Z. by the  Biotechnology and Biological Science Research Council  to Z. A. W. , and by the  Deutsche Forschungsgemeinschaft  to L. S.
Acknowledgments A. P. thanks the  F. R. S. -FNRS  for her postdoctoral researcher position. This work has been supported by funds from the  CNRS , the  Région Aquitaine , the  F. R. S. -FNRS , the  AUL (ARC TinTin ), and by the  Belgian Government  (IUAP No. P7/5 ). The calculations were performed on the computing facilities of the  Consortium des Équipements de Calcul Intensif (CÉCI , http://www. ceci-hpc. be), in particular those of the  Plateforme Technologique de Calcul Intensif (PTCI)  installed in the  University of Namur , for which we gratefully acknowledge financial support of the  FNRS-FRFC  (Convention No. 2. 4. 617. 07. F and 2. 5020. 11 )and of the  University of Namur.
Statistical analyses Data are expressed as median with inter-quartile range (IQR) or as frequencies and percentages or otherwise specified. Continuous variables were assessed with the Wilcoxon signed-rank test and the Mann-Whitney test for dependent and independent data, respectively. Categorical data were compared by use of the Fisher's exact test. Differences between baseline and 48-week data were assessed with paired t test. The level of significance was established at 0. 05 and all reported P values are two-sided. All analyses were performed with the SAS version 9. 2 software (SAS Institute Inc. , Cary, NC). A logistic regression analysis was used to examine the association of baseline parameters with limb fat gain $800 grams over 48 weeks; variables associated with a P,0. 1 in the bivariate analyses were included in the multivariate stepwise analysis.
This work has been partially funded by  Fondo de Investigaciones Sanitarias  ( FIS 08/00256  and  PI11/1512 ),  Fundacion para la Investigacion y Prevencion del SIDA en Espan ˜a (FIPSE 36610 ,  36572/06 ,  36697/07 ),  Red de Investigacion en SIDA  ( RIS RD12/0017/0014 ,  RD06/0006/0005 ), and an unrestricted grant from  Merck, Sharp & Dohme ,  Spain. Pere Domingo  is supported by a grant from the  Programa de Intensificacion de Investigadores, Instituto de Salud Carlos III  ( INT12/ 383  and  INT13/232. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This study was partly funded by an unrestricted grant from  Merck, Sharp & Dohme, Spain , the producer of raltegravir. There are no further patents, products in development or marketed products to declare.
MadAnalysis 5 is a new Python/C++ package facilitating phenomenological analyses that can be performed in the framework of Monte Carlo simulations of collisions to be produced in high-energy physics experiments. It allows one, by means of a user-friendly interpreter, to perform professional physics analyses in a very simple way. Starting from event samples as generated by any Monte Carlo event generator, large classes of selections can be implemented through intuitive commands, many standard kinematical distributions can be automatically represented by histograms and all results are eventually gathered into detailed Html and latex reports. In this work, we briefly report on the latest developments of the code, focusing on the interface to the FastJet program dedicated to jet reconstruction.
Phenomenological analyses can then be performed at several levels, from a purely partonic description of the simulated event final states to a more complex description including hadronization and possibly a fast simulation of the detector response. The MadAnalysis 5 package  [1]  offers a unique framework to perform such analyses, in an easy and efficient way. While about to be fully embedded within the Monte Carlo event generators MadGraph 5  [2]  and aMC@NLO  [3] , MadAnalysis 5 can also be used as a standalone program and outside the MadGraph environment. In this paper, we describe some of the novel features of the version 1. 1. 8 of the code and provide an update of the original user guide. After a brief overview of MadAnalysis 5 in Section 2, we illustrate how to use it to design a phenomenological analysis in Section 3.
MadAnalysis 5 in a nutshell MadAnalysis 5 is a framework designed to facilitate the implementation of phenomenological studies based on Monte Carlo simulations of collisions describing both new physics signal and Standard Model background processes. It allows one, on the one hand, to investigate the characteristics of a particular process, and, on the other hand, to carry out an event selection aiming to extract a given signal from the background. It supports event files encoded under the StdHep [4], HepMc  [5] , Lhe  [6, 7] , Lhco [8] and Delphes Root  [9]  formats. Properties of the event final states can be probed by means of a small set of intuitive Python commands (see below) acting on particles modeled by user-defined labels and several observables. They can then be represented by histograms whose layout can be tuned according to the preferences of the user.
In the same way, event selection criteria can be implemented via dedicated commands and equally applied to a full event (i. e. , to select or reject it under a given condition) or to a particle (i. e. , to include it or not in the analysis under a specific condition). MadAnalysis 5 can also further compute signal over background ratios whose definition can be adjusted by the user. Flexible, MadAnalysis 5 also offers sophisticated functionalities allowing one to, e. g. , combine particle four-momenta (sums, differences, etc. ) before the computation of any observable, use the event history (mother-to-daughter relations linking a particle to its decay products) or identify a given particle according to its hardness. All the results are collected into reports available under the Html, Postscript (associated with latex) or Pdf (associated with pdflatex) formats.
Python console Once downloaded from the Internet (see Ref. [11] ) and unpacked, the MadAnalysis 5 package does not require any compilation and can be started by issuing bin/ma5 from the directory where it has been stored. By default, MadAnalysis 5 is configured for the analysis of events at the parton level, although events at the hadron or at the reconstructed level can be analyzed by launching the program with the -H and -R arguments, respectively. The user interacts with MadAnalysis 5 through a console implemented in the Python language that invites him/her to define his/her analysis through a set of commands that can also possibly be collected into a script. The MadAnalysis 5 metalanguage is designed to be simple and concise, as illustrated in the examples presented in the next sections, and the user can be helped in two ways.
Structure of the program Particle labels can be either directly defined in the Python console or imported from a Universal FeynRules Output (UFO) model containing names and labels for all the particles of a given model  [12]. Additionally, the list of event samples to analyze has to be specified. MadAnalysis 5 then takes care of automatically exporting the analysis under the form of a C++ code to be linked to a library called SampleAnalyzer, shipped with the package, and to any other optional tools such as FastJet or Delphes according to the needs of the user. The C++ program is further compiled and executed, and the results are encapsulated into an Xml structure stored in a so-called Saf (standing for SampleAnalyzer Format) file. The Python console is eventually automatically taking care of rendering the information human-readable, under the form of PostScript, Pdf and Html reports including the defined histograms and the computed selection efficiencies.
The full code structure is summarized on Figure  1. On different footings, users can also directly implement their analysis in the C++ language, bypassing the Python console and using all built-in functions available within the SampleAnalyzer library. This way of using MadAnalysis 5 is called the expert mode of running, in contrast to the normal mode using the Python console, and allows one to implement any analysis which a user can dream of, the only limitation being his/her imagination and programming skills.
or a more recent version (although not a 3. x version), • the Gnu Gcc compiler together with the associated Standard Template Libraries, • the Root package version 5. 27 or more recent  [13]. Some specific functionalities of MadAnalysis 5 may require the use of other external packages and are thus deactivated if necessary. Those consist of • the latex and pdflatex compilers (analysis reports in the PostScript and Pdf formats, respectively), • the zlib package (reading and writing compressed files), • the FastJet package (to switch on the module dedicated to jet reconstruction, see Section 4), • the Delphes package (reading Root files as outputted by Delphes 3). • a few representative event samples useful to learn how to use MadAnalysis 5. With the exception of the latex and pdflatex compilers (that must be installed independently by the user), each optional package that MadAnalysis 5 may require can be installed automatically by using the command install from the Python console.
Basic concepts First, the event samples that must be read and analyzed are specified through the command import. By default, all imported samples are collected within a single dataset called defaultset, although the user can define as many datasets as necessary by using the command import together with the keyword as. In general, it is recommended to have one single dataset for each physics process. The wildcard characters * and ? can also be used to import several files simultaneously. Second, in order to treat the different particle species all along the analysis, MadAnalysis 5 employs labels referring either to one given type of particle or to several types of particles (the term of multiparticle is used in this case). When MadAnalysis 5 starts, collections of standard labels for the SM and Minimal Supersymmetric Standard Model particles are loaded.
Illustrative example We provide, for the sake of the example, a toy analysis based on the events samples provided by installing the optional package samples. The analysis commands are given by (1) ma5> import samples/zz. lhe. gz as mySignal (2) ma5> import samples/ttbar_sl_?. lhe. gz as myBkg (3) ma5> set myBkg. type = background (4) ma5> define mu = mu+ mu-(5) ma5> plot MET (6) ma5> plot PT(mu) [logY] (7) ma5> reject (mu) PT < 20 (8) ma5> select 80 < M (mu+ mu-) < 100 (9) ma5> plot M(mu+ mu-) On the first line, a dataset called mySignal is created and linked to the event file zz. lhe. gz. On the second line, one collects the two samples ttbar sl 1. lhe. gz and ttbar sl 2. lhe. gz into a unique dataset named myBkg, which is further defined as containing background events on the third line.
In contrast, the type of the mySignal dataset is left to its default value, i. e. , the related events being considered as signal events. A new multiparticle label is defined on the fourth line by making use of the command define and is associated with both muons and antimuons. This label is then used in the rest of the analysis to define selections and histograms. Two histograms are hence defined on the fifth and sixth lines of the analysis, the first one representing the missing transverse energy distribution extracted from each dataset and the second one describing the transverse momentum spectrum obtained after considering all muons and antimuons of each event. By the commands of the seventh and eighth lines, we demand to consider, in the rest of the analysis, all (anti)muons with a transverse momentum smaller than 20 GeV and to keep events containing at least one muonantimuon pair with an invariant mass included in the range [80, 100] GeV.
Submitting and executing the analysis The user can execute his/her analysis by typing in, in the Python console, the command submit Behind the scenes, a C++ code corresponding to the implemented analysis is generated, compiled and linked to the SampleAnalyzer library. The executable is then run over all datasets and the results are displayed via the different reports. The Html report can always be open directly from the Python interface by means of the command open A representative histogram that can be found in the report associated with the analysis above is shown in Figure  2.