dataesr/scientific-paragraphs-categorization huggingface dataset:
This creates extremely exciting opportunities for proteomics data re-use with specialized tools that can lead to the discovery of new biological information  21. 2-Tools platform. In this platform, the overall aim would be to increase the visibility, quality and sustainability of proteomics software developed following best practices. First of all, more proteomics tools should be effectively included into the ELIXIR tool registry, and highlighted there appropriately. However, it is worth highlighting that there are around 350 tools proteomics tools represented in this resource already. In addition, the development of improved and user-friendly quantification algorithms and tools, along with direct coupling to dedicated and performant statistical analysis (Cluster 5), also connects directly to the ELIXIR tools platform.
Other possible activities would be related to Cluster 4, and would involve the improvement of the description and sharing of proteomics workflows, facilitating the encapsulation of workflows, data and tools into proteomics software containers that could be shared across ELIXIR, taking advantage of existing resources. Finally, the proposed idea in the meeting to create of a repository for tool-related ideas, would also be applicable in the wider ELIXIR context. 3-Interoperability platform. Obviously, the activities of this platform are very relevant in the case of "multi-omics" approaches (Cluster 1), for instance the development of data standards for these type of approaches, e. g. to enable better data integration and visualisation. A close connection can also be made to metabolomics with regards to QC (Cluster 3), as the main technology of choice (mass spectrometry) is shared between these two fields.
4-Compute platform. Workflow analysis pipelines and activities related to the development of cloud infrastructures (Cluster 4) and QC activities (Cluster 3) should be highlighted here. This would involve different pieces of infrastructure supporting the efficient development of such workflows, e. g. the EDAM ontology and the ELIXIR tool registry  15  , open data formats, and workflow management systems, among others. It is important to note that, although originally set up in the Data platform, the proteomics ELIXIR implementation study mentioned in the 'Introduction' section aims to make a first step forward in this direction, for the popular shotgun (MS/MS) approaches. As a proof of concept, these pipelines will be deployed first in the EMBL-EBI "Embassy Cloud", a cloud infrastructure based in the Open Stack operating system, with the idea that in the future they can be made available in other cloud systems (e.
g. Amazon EC2, Google Cloud, Microsoft Azure), so that the developed pipelines can be freely reused by any interested researcher in the community. In the context of the implementation study, the pipelines are connected to the PRIDE database, bringing the analysis tools closer to the data as datasets become larger in size and complexity. This trend is ongoing for other omics technologies in the context of ELIXIR: for instance, the pipelines developed in the context of the Marine metagenomics use case  22. 5-Training platform. ELIXIR is already working actively in coordinating training activities across Europe (e. g. the already mentioned TeSS portal). Although training was not specifically highlighted as one of the main areas for future development during the meeting, the reason is that everyone assumes that this is implicitly a key need in all bioinformatics fields.
Connection to metabolomics and other ESFRIs As mentioned already, metabolomics and proteomics share a common experimental platform: mass spectrometry. A parallel effort is currently ongoing to integrate metabolomics activities in ELIXIR. There are many topics of common interest where both fields could benefit from a closer interaction: e. g. the development of common software (for QC, data visualisation, signal processing, to name but a few) and open data standards. Some initiatives are already working towards this goal, e. g. the computational mass spectrometry group (http://compms. org/), and some recent activities recently carried out by the PSI, but a lot of work remains to be done. One concrete proposal would be to coordinate efforts concerning training activities. ELIXIR would be an ideal platform to enable this coordination effort.
Coming back to the proteomics field, PRIME-XS (http://www. primexs. eu) was a four-year Infrastructure project funded by the EU FP7 Programme (2011-2015), coordinated by Prof. Albert Heck. The main aim of PRIME-XS was to provide funded access to an infrastructure of state-of-the-art proteomics technology to the European biological and biomedical research community. It is anticipated that future tailored EU H2020 infrastructure grant calls might provide an opportunity to create a second iteration of this successful project. The potential synergies with ELIXIR, considering the outlined activities in this white paper, would be obvious at different levels, for instance the provision of a cloud computing infrastructure to store and analyse the acquired data in a scalable and reproducible way, and the development of pipelines to allow data integration between proteomics and other omics data types.
5256/f1000research. 12693. r23454 Felix Elortza CIC bioGUNE, CIBERehd, ProteoRed-ISCIII, Derio, Spain Proteomics has reached a point in which bioinformatics is of paramount importance for the process to extract the maximum information out of the enormous amount of data obtained in practically any study. The scientific community in general and proteomics in particular is eager to get new and effective solutions that may implement that way. In this white paper manuscript, Vizcaino explain the ELIXIR et al. initiative and the importance of integrating proteomics in this European Research Infrastructure. Authors cover in a detailed way what was covered on the meeting that took place in Tübingen (Germany) on February 2017 under the name "The future of proteomics in ELIXIR". The paper is well written and structured.
doi: 10. 10 5256/f1000research. 12693. r23454 Felix Elortza CIC bioGUNE, CIBERehd, ProteoRed-ISCIII, Derio, Spain Proteomics has reached a point in which bioinformatics is of paramount importance for the process to extract the maximum information out of the enormous amount of data obtained in practically any study. The scientific community in general and proteomics in particular is eager to get new and effective solutions that may implement that way. In this white paper manuscript, Vizcaino explain the ELIXIR et al. initiative and the importance of integrating proteomics in this European Research Infrastructure. Authors cover in a detailed way what was covered on the meeting that took place in Tübingen (Germany) on February 2017 under the name "The future of proteomics in ELIXIR". The paper is well written and structured.
Behavior of the linearized model The state space model was implemented in Matlab 2012. The 2D computational domain L x × L y = 32 × 4 was discretized on a Cartesian grid of nx × ny = 65 × 17 mesh nodes so that x = [0, 64] and y = [-2, 2] with Re = 100. From the different choices for Q and R, smaller cost value could be obtained with Q = q 2 I and R = r 2 I highly  depending on the ratio r q. Results are given in this section for q = 10 and r = 1. -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 -2 - Numerical computation of the matrix A was thereafter possible. First, its eigenvalues were computed. Since they all belonged to the left half complex plane, that means that the system was stable around the desired state, as expected for convectively unstable and a typical noise amplifier flow (disturbances are amplified downstream).
(2010). The MPI implementation based on pencil domain decomposition strategy is described in  Laizet and Li (2011). The 3D computational domain was L x × L y × L z = 256 × 256 × 4. This domain was discretized on a Cartesian grid (stretched in y) of nx×ny×nz = 513× 257 × 8 mesh nodes, so that the grid node in Incompact3d included the grid node in Matlab. The number of nodes in the spanwise direction was as small as possible to reduce computation time. This forced a quasi-2D flow but full 3D flow was not necessary for the purpose of this study (linear Proceedings of the 20th IFAC World Congress Toulouse, France, July 9-14, 2017 control of non-linear flow). Same Reynolds number value of 100 was used. The control signal and the perturbation were imposed upstream using Dirichlet boundary condition.
Stream function (state variable of the linear system, that have to be measured) was computed by integrating the velocity field. -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 -2 - To compare the behavior of both models, we considered the case where the system was forced by an input signal ω (t). The response of the linearized model was then given by the resolution of: Ẋ (t) = AX (t) + B ω (t). ( 29 ) The disturbances ω (t) was formulated as a temporal sinusoidal excitation and was given by: ω (t) = a sin (2πf St t) , ( 30 ) where f St = 0. 1 is approximately the Strouhal frequency (most amplified periodic perturbation, see  Michalke (1965) ) and a = 0. 01 is the amplitude. In figure  5 , we present snapshots of the stream function ψ d from simulations with the linear model (Matlab 2012) and the non linear model (Incompact3d) using co-flow and shear bounded disturbances with sinusoidal amplitude.
)  and the probability distribution functions of photometric redshifts  (Kodra et al. 2016 in prep. ). Since the first observations  (Rhodes et al. 2000 , PI Groth 1994) , the EGS has been the site of several surveys, notably the All-wavelength Extended Groth Strip International Survey (AEGIS,  Davis et al. 2007)  and the DEEP2+3 spectroscopic survey  (Coil et al. 2004; Cooper et al. 2006; Newman et al. 2013; Cooper et al. 2011 Cooper et al. , 2012)). The catalog is complemented by measurements of the photometric redshifts and stellar population parameters. This paper is organised as follows. Section 2 describes the datasets used to construct our multi-wavelength cat-alog. Section 3 discusses the World Coordinate System (WCS) for the HST mosaics. Section 4 describes the procedures for the flux measurements over the wide wavelength range, while Section 5 presents the validation tests of the multi-wavelength catalog.
CFHTLS The catalog incorporates flux measurements from the broad-band u * , g ′ , r ′ , i ′ , and z ′ images obtained by the For this work, we adopted the mosaics generated by the MegaPipe pipeline  (Gwyn 2008)  as described by  Gwyn (2012). The sensitivity limits corresponding to the 50% completeness are u * = 27. 5, g ′ = 27. 9, r ′ = 27. 6, i ′ = 27. 3, and z ′ = 26. 4 mag, for the five bands respectively  (Gwyn 2012). They were obtained by adding artificial point sources to the image after replacing the pixels of all the detected objects with a realisation of noise and recovering sources using SExtractor  (Bertin & Arnouts 1996). The image quality is homogeneous across the bands, with the PSF FWHM average values of ∼ 0. ′′ 7 -0. ′′ 9. The astrometric calibration was based on the Naval Observatory Merged Astrometric Dataset (NOMAD 35  ), and was refined using the Sloan Digital Sky Survey.
PHOTOMETRY For the flux measurements in the CANDELS EGS photometric catalog we adopted two different approaches, depending on the angular resolution of the images, as parameterised by the FWHM of the PSF charactersic of each band. For the high-resolution images, i. e. , all the HST bands, the photometry was performed using SExtractor (Bertin & Arnouts 1996) in dual-mode with a detection on the F160W band and measurements on the PSF-matched ACS/WFC3 images matched to the F160W resolution, the lowest in our HST dataset. For the low-resolution images, i. e. , all the ground-based data and the Spitzer /IRAC images, the photometry was performed using the TFIT software  (Laidler et al. 2007 ), which uses a morphological template fitting technique. These procedures have been extensively described by  Galametz et al.
1. f tot,b = f iso,b × f auto,F160W f iso,F160W (1) Figure  5  presents the distribution of the 1σ depth in each HST band. The limiting magnitudes in the WFC3 bands roughly follow a tri-modal distribution, which is due to the different degrees of tile overlapping within the mosaics: the heavily overlapped regions among adjacent tiles have higher sensitivities than what is typical, while the boundary regions of less coverage have lower sensitivities. The distribution for ACS limiting magnitudes is broader and more complex than that of the WFC3 ones. One possible explanation for this is that the ACS mosaics are the result of two different datasets being combined together (AEGIS and CANDELS), each one likely with its own tri-modal distribution. Another possible explanation could be the different native pixel scale of the ACS camera compared to WFC3.
Number counts The distribution of detected objects as a function of their apparent flux densities, the source number counts, is one of the most basic tests for the assessment of a sensitivity-limited catalog. The WFC3 F160W band number counts of our EGS catalog are presented in  Figure 11. For magnitudes brighter than ∼ 25 mag (which roughly corresponds to the completeness limit for our catalog when extended objects are taken into account), the number counts can be fitted by a power-law with slope γ = 0. 700 ± 0. 006. However, a better description of the data can be obtained by considering two power-laws: one in the range 17 mag F160W 20 mag and a second for 20 mag F160W 25 mag. In this case, the measured slopes are γ = 0. 89 ± 0. 08 and γ = 0. 668 ± 0. 008, for the brighter and fainter regimes, respectively.
3D-HST The 3D-HST multi-wavelength photometric catalog is described by  Skelton et al. (2014). For the HST data, the measurements were done using SExtractor on the mosaics PSF-matched to the F160W band. The measurements for the ground based data and the Spitzer data were done by using MOPHONGO  (Labbé et al. 2005 (Labbé et al. , 2006 (Labbé et al. , 2013)) , a procedure similar to TFIT but with the difference that the photometry is done with a circular aperture after the target source is cleaned of its neighbours. The measured fluxes of  Skelton et al. (2014)  were subject to a number of corrections with the aim of providing accurate measurements of the total flux. Based on  Labbé et al. (2003 ; see e. g. , their Figure  5 ) the SExtractor FLUX AUTO flux for faint sources can be systematically lower than the intrinsic total flux.
Photometric redshifts The multi-band photometric data were independently analysed by ten different groups within the CANDELS collaboration. Each group adopted a different code and/or set of SED templates 39. The NMBS J1, J2, J3, H1, and H2 data were excluded because of their relatively shallow depth and the limited overlap with the F160W footprint. The WIRCAM J and H data were also excluded because these two bands are similar to the WFC3 F125W and F160W, respectively, but the data are much shallower than the latter two. As a training set, 840 spectroscopic redshifts from the DEEP3 program were also provided to each team. As a common practice, the photometric zeropoints were fine-tuned by each group during the process in order to minimise the overall residuals between the measured flux densities and those expected from the best-fit templates.
Colored Petri Nets are an extension of Petri Nets based on a functional language where the notion of typing is fundamental. They allow the association with a value named color to differentiate tokens. Color of tokens can be transformed or tested during their passage in arcs and transitions. The formal definition of a Colored Petri Net is given below  [10]. Definition 1 A Colored Petri Net is a tuple CPN = (Σ, P, T, A, N, C, G, E, I) satisfying the following requirements: (i) Σ is a finite set of non-empty types, called colour sets. (ii) P is a finite set of places. (iii) T is a finite set of transitions. (iv) A is a finite set of arcs such that: P ∩ T = P ∩ A = T ∩ A = Ø. (v) N is a node function. It is defined from A into P × T ∪ T × P. (vi) C is a colour function. It is defined from P into Σ.
(vii) G is a guard function. It is defined from T into expres- sions such that: ∀t ϵ T : [Type (G(t)) = Bool ∧ Type (Var (G(t))) ⊆ Σ]. (viii) E is an arc expression function. It is defined from A into expressions such that: ∀a ϵ A : Type E a ð Þ ð Þ¼C p a ð Þ ð Þ MS ∧ Type Var E a ð Þ ð Þ ð Þ ⊆Σ Â Ã Where p(a) is the place of N(a). (ix) I is an initialization function. It is defined from P into closed expressions such that: ∀p ϵ P : Type I p ð Þ ð Þ ¼ C p ð Þ MS Â Ã : To have more details on the above definition, the reader can refer to  [10]. In this context some typical cases are modeled using the CPN-tools software platform. The main architects behind the tool are Kurt Jensen, Soren Christensen, Lars M. Kristensen, and Michael Westergaard  [11, 12]. This platform combines Petri Nets with the functional programming language Standard ML.
Standard ML provides the possibilities of defining data types and describing data manipulation. This software has been adopted by the PERFECT project, especially for its extensive use in the previous contribution works in rail systems assessment  [13] , and for its perfect match with Colored Petri Nets requirements. A parallel modeling work within PERFECT, using this tool, is focused on railway scenario CPN modeling including infrastructure, regulation, interlocking and human factors. Safety properties within these models are intended to be verified using the methodology of the present paper. Figure  1  illustrates an example of a Colored Petri Net model for a simple railway movement from one track circuit to the next. Later in this paper, an academic railway example of trains' safe movements is introduced and modeled using the CPNtool.
Description of the transformation methodology This section describes the proposed conversion algorithm to translate CPN models to B abstract machines. First of all, a general survey of previous contributions will be presented to point out the difficulties and limitations of the establishment of a complete and applicable transformation method. Then, the translation approach consists in defining, firstly, the transformation rules of a CPN's generic structural properties. Yet, the conversion of CPN model to B language will be complemented by the integration of the behavioral specification and dynamic properties.
Within Model Driven Engineering, input Petri net models conform to the metamodel presented in  [19] , which is based on Jensen's formal definition. A recent previous work with the same goal and using a mapping approach is presented by BON. P in  [20]. However, that conversion method was tested and it was found that the resulting machines are not accepted by the B tools due to the use of a large amount of definitions, added to their unpractical heaviness. Indeed, this method tries to define both the structural and behavioral properties of CPNs thanks to heavy definitions, and copes with these CPN modeling as a rigid graphical formalism. Consequently, the obtained machines are very hard to express or simulate and cannot be verified using B tools. Another recent and important work to refer to is the work presented in  [21] , where a very close issue was considered, but this time for a mapping from Place/Transition Petri Nets to the B-language.
Furthermore, this new method intends to provide a transformation able to be encoded in order to develop an interactive software platform for automatic CPN conversion into B abstract machines. So, this work describes in detail the principles of the transformation and its application and will constitute the base framework for the establishment of formal representations and proof of the transformation. The next sections detail the transformation theory, starting with basic Multisets definition, structural properties transformation rules, and finally, the description of CPN behavioral characteristics transformation according to the spirit of the B method. Afterward, the B machines behavior equivalence to the original CPN models is demonstrated, which constitute a first step toward a formal proof of the correctness of the method.
Extensions in the field of joint modeling of correlated data and dynamic predictions improve the development of prognosis research. The R package frailtypack provides estimations of various joint models for longitudinal data and survival events. In particular, it fits models for recurrent events and a terminal event (frailtyPenal), models for two survival outcomes for clustered data (frailtyPenal), models for two types of recurrent events and a terminal event (multivPenal), models for a longitudinal biomarker and a terminal event (longiPenal) and models for a longitudinal biomarker, recurrent events and a terminal event (trivPenal). The estimators are obtained using a standard and penalized maximum likelihood approach, each model function allows to evaluate goodness-of-fit analyses and plots of baseline hazard functions.
Software for joint models Together with the theoretical development of the joint models, the increase of appropriate software is observed, mostly for standard frameworks. Among the R (R Development Core Team 2016) packages, the joint analysis of a single longitudinal outcome and a terminal event can be performed using JM  (Rizopoulos 2016a ) based on the likelihood maximization approach using expectation-maximization (EM) algorithm, JMbayes  (Rizopoulos 2016b ) built in the Bayesian context, and joineR  (Philipson, Sousa, Diggle, Williamson, Kolamunnage-Dona, and Henderson 2012) , a frequentist approach that allows a flexible formulation of the dependence structure. For the approach based on the joint latent class models, i. e. , joint models that consider homogeneous latent subgroups of individuals sharing the same biomarker trajectory and risk of the terminal event, an extensive package lccm (Proust-Lima, Philipps, Diakite, and Liquet 2016) provides the estimations based on the frequentist approach.
Apart from the R packages, a stjm package  (Crowther 2013)  in STATA uses maximum likelihood and provides flexible methods for modeling the longitudinal outcome based on polynomials or splines. Another possibility for the analysis of the joint models for a longitudinal outcome and a terminal event is a SAS macro JMFit  (Zhang, Chen, and Ibrahim 2014). Joint models using nonlinear mixed-effects models can be estimated using MONOLIX software  (Mbogning, Bleakley, and Lavielle 2015). Among the packages JM, JMbayes and lcmm provide predictive tools and predictive accuracy measures: EPOCE estimator in lcmm, ROC and AUC (area under the curve) measures and prediction error in JM and JMbayes. For joint models for correlated events (recurrent event or clustered event data) and a terminal event the available statistical software is limited.
Among the R packages, joint. Cox  (Emura 2016)  provides the estimations using penalized likelihood under the copula-based joint Cox models for time to clustered events (progression) and a terminal event. Trivariate joint models proposed by  Liu and Huang (2009)  were performed in SAS using the procedure NLMIXED. The R package frailtypack  (Rondeau, Gonzalez, Mazroui, Mauguen, Krol, Diakite, and Laurent 2016)  fits several types of joint models. Originally developed for the shared frailty models for correlated outcomes it has been extended into the direction of joint models. These extensions include the joint model for one or two types of recurrent events and a terminal event, the joint model for a longitudinal biomarker and a terminal event and the trivariate model for a longitudinal biomarker, recurrent events and a terminal event.