dataesr/scientific-paragraphs-categorization huggingface dataset:
The calculated slope value defines the limit for the maximum intensity that can be analyzed using the SpIDA software. A slope value of 37. 49 Ϯ 0. 081 intensity units (IU) (PMT shot noise) was applied to all image sets loaded into the SpIDA program. The white noise background signal was determined by measuring the pixel intensity values for an image acquired when the 488-nm laser line was switched off. SpIDA analysis was performed using a stand-alone MATLAB Graphical User Interface program, available at the Neurophotonics Web site  (8). Images were imported into the SpIDA software, and the laser beam waist size and image pixel size were input. The fitting of super-Poissonian distribution curves to fluorescence intensity histogram plots created from carefully selected regions of interest (RoIs) (to obtain good super-Poissonian fitting, fluorescent moieties defined within the analysis RoIs must have a homogeneous distribution) was performed on the imported input image using the SpIDA software.
For the initial determination of the QB value of monomeric P-M-A206K eGFP, the program was used in the one-population mode, obtaining a value of (12. 71 Ϯ 0. 29 IU). Subsequent measurements using the receptor-eGFP constructs used the one-or two-population fit model modes with the previously determined QB value of 12. 71 IU. SpIDA monomer to dimer oligomeric analysis of the EGFR-A206K eGFP-expressing cells in response to epidermal growth factor treatment was quantified specifically using the two-population monomer-dimer model. Due to the oligomerization state complexity of the 5-HT 2C -A206K eGFP receptor (i. e. initial analysis indicated that there were more than two populations present, so none of the two-population fit models could be used to yield accurate quantification), we used the one-population SpIDA model, which can accurately quantify average QB and density values in the presence of higher-order oligomeric populations.
4 Using one-population SpIDA, the quantified QB value of monomeric P-M-A206K eGFP was used to quantify the oligomeric status of the 5-HT 2C -A206K eGFP receptor. The SpIDA software also reports the mean fluorescent intensity for each RoI analyzed. The number of 5-HT 2C -A206K eGFP receptors per m 2 (density) was measured by dividing this mean fluorescent intensity by the quantified monomeric QB value. Differences between the mean values of each experimental group data set were statistically quantified using one-way analysis of variance (GraphPad Prism version 5).
The properties of the observed Higgs boson with mass around 125 GeV can be affected in a variety of ways by new physics beyond the Standard Model (SM). The wealth of experimental results, targeting the different combinations for the production and decay of a Higgs boson, makes it a non-trivial task to assess the compatibility of a non-SM-like Higgs boson with all available results. In this paper we present Lilith, a new public tool for constraining new physics from signal strength measurements performed at the LHC and the Tevatron. Lilith is a Python library that can also be used in C and C++/ROOT programs. The Higgs likelihood is based on experimental results stored in an easily extensible XML database, and is evaluated from the user input, given in XML format in terms of reduced couplings or signal strengths.
Moreover, recently a public tool, HiggsSignals  [32] , became available. HiggsSignals is a FORTRAN code that uses the signal strengths for individual measurements, taking into account the associated efficiencies. In this paper, we present a new public tool, Lilith. 1  Lilith is a library written in Python, that can easily be used in any Python script as well as in C and C++/ROOT codes, and for which we also provide a command-line interface. It follows a different approach than HiggsSignals in that it uses as a primary input results in which the fundamental production and decay modes are unfolded from experimental categories. The experimental results are stored in XML files, making it easy to modify and extend. The user input can be given in terms of reduced couplings or signal strengths for one or multiple Higgs states, and is also specified in an XML format.
In Section 2, we present the signal strength framework used to encode deviations from the SM at the LHC, as well as the experimental results that we use as input in Lilith. The parametrization of new physics effects on the observed Higgs boson, as well as derivation of signal strengths, are presented in Section 3. All technical details on how to use Lilith and the XML formats that we use are then given in Section 4. Constraints derived from Lilith are validated in Section 5, and two concrete examples of its capabilities are given in Section 6. Finally, prospects for Run II of the LHC are discussed in Section 7, and conclusions are given in Section 8.
This will be presented in Section 5 for the experimental results included in the database of Lilith. Finally, note that generalization of the previous equations is trivial should higher-dimensional signal strength measurements be published by the experimental collaborations. A database of up-to-date experimental results is shipped with Lilith, along with recommended sets of results to use for computing the likelihood (in the form of list files; all technical details will be given in Section 4). The default set of results, latest. list, includes the latest measurements from the LHC. Its content as of February 2015 is displayed in Table  1. The CMS 2D results are taken from the combination of Ref. [5] , but correspond to the results from Refs. [34, 35, [53] [54] [55]. We also take Collaboration Analysis Type Reference ATLAS H → γγ 2D contour  [46]  H → ZZ * 2D contour  [36]  H → W W * 2D contour  [47]  H → τ τ 2D contour  [37]  VH, H → b b 2D contour  [48]  ZH, H → invisible full 1D  [49]  ttH, H → b b 1D interval  [50]  ttH, H → γγ 1D interval  [46]  CMS H → γγ, ZZ * , W W * , b b, τ τ 2D contours [5] ttH, H → γγ, τ τ 1D interval  [5]  ttH, H → leptons 1D interval  [35]  ZH + VBF, H → invisible full 1D  [51]  CDF & D0 VH, H → b b 1D interval  [52]  Table  1 : Recommended set of experimental results, for the database of Lilith version 15.
02. This set corresponds to the file latest. list, and is used by default when running Lilith. into account all available searches on production in association with a top-quark pair, as well as searches for invisible decays of the Higgs boson from both ATLAS and CMS. Note the presence of the CDF and D0 combined result for VH, H → b b  [52] ; only in this channel the precision of the Tevatron result is comparable with the one of the LHC at Run I. All considered experimental results are given at a fixed Higgs mass (that can be read from the database, see Section 4. 5) in the [125, 125. 6] GeV range. Variations of the experimental results within this narrow interval are expected to be small, hence limiting the inconsistencies when combining the results. However, it would be desirable to take into account the variation of the results with mass, as we will argue in Section 7.
Getting started Lilith is a library written in Python for constraining model of new physics against the LHC results. The code is distributed under the GNU General Public License v3. 0. The latest version of Lilith and of the database of experimental results (as of February 2015, Lilith 1. 1 and database version 15. 02) as well as all necessary information can be found at http://lpsc. in2p3. fr/projects-th/lilith The archive of Lilith can be unpacked in any directory. It contains a root directory called Lilith-1. 1/ where the following directories can be found: • lilith/: the Python package itself. The Lilith application programming interface (API) will be presented in Section 4. 2. It also contains the Python/C API that will be presented in Section 4. 4. • data/: contains the database of experimental results in XML format, as well as *.
Lilith requires Python 2. 6  [63]  or more recent, but not the 3. X series. The standard Python scientific libraries, SciPy and NumPy  [64] , should furthermore be installed. We require SciPy 0. 9. 0 or more recent, and NumPy 1. 6. 1 or more recent. Python, SciPy and NumPy are available for the major platforms, including GNU/Linux, Mac OS X, and Microsoft Windows. The easiest way to check if all dependencies of Lilith are correctly installed is to try to compute the likelihood from an example file. This can be achieved by typing to the shell (with current directory Lilith-1. where YY and MM correspond to the year and to the month, respectively. If two or more updates of the experimental database are provided the same month, from the second release onwards versions will be numbered YY. MM. n (with n starting from 1).
The Lilith API Lilith provides an API from which all tasks (reading the user and the experimental input, compute the likelihood, print the results in a file, etc. ) can be performed, using the methods described below. This is the recommended way of using Lilith. In order to be used in any Python code (or in an interactive session of Python), the package of Lilith, called lilith, first needs to be imported. However, Python needs to know the location of the lilith package. This can be achieved in at least three ways: 1. create the Python script importing lilith in the directory Lilith-1. 1/ (or, in an interactive session, having Lilith-1. 1/ as the current directory). Read the user input located at filepath and call readuserinput().
Lilith. readdbversion() Read the version of the database of experimental results from the file data/version, and store the information in dbversion. Lilith. compute exp ndf() Compute the number of measurements from exp mu and store the information in exp ndf. Lilith. computelikelihood(userinput=None, exp filepath=None, userfilepath=None) Evaluate the likelihood function from signal strengths derived from the user input (user mu tot) and the experimental results (exp mu) and store the results in the attribute results. If the arguments userinput and userfilepath are not specified, user mu tot will be assumed to have been filled already. Else, all information will be read from the XML input given in userinput, or from the file located at userfilepath, and user mu tot will be computed. If the exp filepath argument is not specified, the experimental results will be read from the default list file unless exp mu is already filled.
from In the example above, any error (corresponding to an exception in Python) will interrupt the code. This may not be the desired behavior. In particular, if several user inputs are successively given to Lilith (as in the case of a scan of a parameter space), it may be preferable to store the error and move on to the next user input instead of stopping the execution of the code. In Python, the handling of errors can be achieved with try. except blocks. We provide below a simple example. print the following error occured: , e Here, any error raised by Lilith (of type lilith. LilithError or derived from it) will be catched, in which case the error message will be printed on the screen and the script will continue normally. It is of course also possible to store the error message in a file, or simply replace the last line with the pass statement in order to ignore all errors raised by Lilith and continue the execution of the script.
A number of options are provided to control the information printed on the screen and to print the results of Lilith in output files. They are listed in Table  2. Initialization of Lilith and reading of the database of experimental results is done at each execution of run lilith. py. When computing the Higgs constraints in the context of the scan of a model, this only needs to be done once. Therefore, successive calls to the CLI will be much slower than direct calls to the methods of the API (see previous section), even more so as internal information is stored to optimize successive computations of the results when using the API. Whenever performance can be an issue, we highly recommend using the API instead of the CLI.
Interface to C and C++/ROOT Above, we have presented how to use Lilith from the API written in Python and from the CLI. In order to use Lilith within a C, C++, or ROOT code, a first possibility is to call the CLI. However, this will suffer from the performance issues explained at the end of the previous section. Fortunately, Python provides a Python/C API  [66]  from which each method of the API of Lilith can be called, and each attribute of Lilith can be manipulated, in both C and C++. However, direct use of the functions of the Python/C API can be quite tedious. For that reason, we have also written a C API for Lilith, consisting in a series of functions using the Python/C API for performing all usual tasks, following closely the methods of the API presented in Section 4. 2. The C API for Lilith is contained in the directory lilith/c-api.
XML format Every single experimental result (1D or 2D) is stored in a different XML file. In this way, modifying and updating the database is an easy process. We now present the format of the experimental results files in Lilith. The root tag of each file is <expmu>. It has two mandatory attributes, dim and type, that specify the type of signal strength result (1D interval, full 1D, 2D contour, or full 2D, see Section 2. 3). Possible values for the attributes are given in Table  3. In addition, the <expmu> tag has two optional attributes: prod and decay. They can be given a value listed in Table  3  if the analysis under consideration is only sensitive to one production mode (e. g. , ttH) or to one decay mode (e. g. , γγ) of the Higgs boson. In the general case, the prod and decay attributes can be skipped.
Each <analysis> tag contains a <l> tag, whose value is -2 log L from this experimental result alone, and an <expmu> tag that follows the syntax of the experimental input (see Section 4. 5) except that it only provides information on the efficiencies (in <eff> tags). Finally, <lilithresults> contains tags summarizing the information: <ltot>, whose value is the sum of the -2 log L values from all experimental results, and <exp ndf>, the number of measurements. We also provide the version of Lilith in a <lilithversion> tag and the version of the experimental database of results in a <dbversion> tag. To summarize, we give a complete example of output considering only two experimental results. <lilithresults> <lilithversion>1. 1</lilithversion> <dbversion>15. 02</dbversion> <analysis experiment="ATLAS" source="HIGG-2013-08"> <expmu decay="gammagamma" dim="2" type="n"> <eff axis="x" prod="ggH">1.
Care should also be taken in order to include all available correlations between systematic uncertainties. To this aim, we provide a new public tool, Lilith. Lilith is a library written in Python, and for which we provide an API as well as a command-line interface and a basic interface to C and C++/ROOT. The experimental results are read from a database in XML format that is shipped with the code and which is easy to modify and extend. Lilith uses as a primary input results in which the fundamental production and decay modes are unfolded from experimental categories. New physics can be parametrized in terms of reduced couplings, or signal strengths directly, which are given as input to Lilith in XML format. If needed, scaling factors for the loopinduced processes and VBF production are computed taking into account QCD corrections.
CP-violating Higgs couplings can also be given as input to Lilith. The likelihood is evaluated from a set of experimental results and given as output; detailed results can moreover be stored in XML or SLHA-like format. For convenience, Lilith is provided with several applications of the code where constraints on effective or explicit models of new physics are derived. They include extensive comments. The Higgs likelihood of Lilith obtained from the latest measurements at the LHC has been thoroughly validated against ATLAS and CMS results and can be used to constrain new physics. Future measurements at Run II of the LHC will, however, call for new ways of presenting results in order to derive a good approximation to the Higgs likelihood. In particular, further disentanglement of the different production and decay modes will become necessary.
Everything is correctly installed if basic information as well as the value of the likelihood is printed on the screen. Note that the version number of Python can be obtained by typing the command python --version to the shell, while the presence of SciPy and NumPy and their version numbers can be checked by typing in an interactive session of Python (started by typing python to the shell) the following commands:Note that every version of Lilith is shipped with the latest version for the database of experimental results at the time of release. However, as new experimental results usually do not require any modification to Lilith, we do not release a new version of the code every time new experimental results come out. Instead, we provide separately an update of the database of experimental results.
Acknowledgments We are deeply indebted to  Sabine Kraml  for her support at all stages of the project and for comments on the manuscript. We would also like to thank  Geneviève Bélanger ,  Ulrich Ellwanger ,  John F. Gunion  and  Sabine Kraml  for the fruitful collaboration on Higgs coupling fits in 2012 and 2013, from which the idea of Lilith originated. This work was supported in part by the  ANR  project  DMAstroLHC , the " Investissements d'avenir, Labex ENIGMASS ", and by the  IBS  under Project Code  IBS-R018-D1. JB thanks the  CTPU-IBS  for hospitality and financial support for a research stay during which this work was finished.
For the definition of all the exceptions, see lilith/errors. py. Read the string in XML format given as argument and fill the attribute couplings (if the user input is given in terms of reduced couplings) or user mu and user mu tot (if the user input is given in terms of signal strengths). User input formats are presented in Section 4. 6. results list of results after computation of the likelihood for each individual measurement l value of -2 log L exception lilith. LilithError 1 Base exception of Lilith. readuserinput(userinput) import sys 2 sys. path. append( /path/to/Lilith-1. 1 ) before importing lilith. dbversion version number for the database of experimental results couplings list of reduced couplings for each Higgs particle contributing to the signal as read from the user input user mu list of signal strengths for each Higgs particle contributing to the signal as read (or derived from) the user input user mu tot signal strengths for the sum of the Higgs particles present in user mu 1 export PYTHONPATH=/path/to/Lilith-1.
1:$PYTHONPATH in bash shell or 1 setenv PYTHONPATH /path/to/Lilith-1. 1:$PYTHONPATH in csh/tcsh shell. class lilith. Lilith(verbose=False, timer=False) Instanciate the Lilith class. The following public attributes are initialized: verbose if True, information will be printed on the screen timer if True, each operation will be timed and the results will be printed on the screen exp mu list of experimental results read from the database exp ndf number of measurements (n-dimensional results count for n measurements) Lilith. readuserinputfile(filepath).
Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields.
INTRODUCTION Understanding of tumorigenesis has drastically improved recently owing to extensive studies of the implicated molecular mechanisms. 1  This knowledge mostly exists in a human-readable form, fragmented in scientific publications. This circumstance hampers the delineation of a global picture of cancer biology and prevents from efficient integration of high-throughput data and application of bioinformatics and systems biology approaches. 2  The effort towards systematic collection and formalization of the knowledge about molecular interactions in a computer-readable form gave birth to pathway databases. 3 Nevertheless, there is no up-to-date pathway database that systematically depicts molecular processes specifically implicated in cancer. Most existing pathway databases provide a view on molecular mechanisms as disconnected processes, splitting their content into 'canonical pathway' representations.
However, cancerogenesis affects simultaneously multiple cellular processes and their crosstalk. Therefore, cancer research can benefit from the reconstruction of cellular signalling in the form of a comprehensive map, representing the complexity of pathway crosstalk  4  manifested by co-participation, interaction or co-regulation of molecular entities in several cell signalling processes. Understanding connections between molecular mechanisms is important for determining potential therapeutic intervention points. 5, 6 o enable reconstruction of comprehensive cell signalling maps, the common standard for depicting biological knowledge, the Systems Biology Graphical Notation has been developed. 7 In addition, handful software tools for charting the large biological maps, such as CellDesigner,  8  CellIllustrator 9 and others, stimulated the community to create large comprehensive maps of molecular interactions implicated in various biological processes.
Therefore, large maps of signalling pathways,  [10] [11] [12] [13] [14] [15] [16]  of complex metabolic processes,  17  as well as maps specific for certain cell types  18, 19  or non-mammalian organisms  [19] [20] [21]  have appeared. Comprehensive maps representing biological mechanisms that are implicated in particular diseases such as Alzheimer disease or rheumatoid arthritis have also been charted recently. 22, 23 everal attempts have been undertaken to connect cancerdriver genes into relatively large networks or to represent them in pathway databases. 24, 25 Cancer Cell Map database was created and included into the Pathway Commons network biology resource. 26 However, those resources are far from being comprehensive representations of cancer-related processes. Therefore, there is the need for a global and detailed representation of molecular mechanisms involved in cancer, depicting the latest discoveries in this rapidly evolving field.
ACSN concept and features ACSN is an interconnected modular 'geographical-like' global map of cancer signalling. ACSN graphically depicts molecular mechanisms that occur in normal, healthy mammalian cells but that are frequently modified in cancer, underlying the hallmarks of cancer. 1  Currently, ACSN covers signalling pathways involved in DNA repair, cell cycle, cell survival, cell death, epithelial-tomesenchymal transition (EMT) and cell motility. The information included in ACSN is organized in three hierarchical levels: (i) the seamless global map with all molecular interactions, (ii) containing five maps of biological processes, and (iii) in turn comprising 52 functional modules (Figure  1 ). By analogy with geographical maps, ACSN can be viewed as a global map of cancer signalling divided into 'continents' (that is, maps), each representing a particular biological process.