dataesr/scientific-paragraphs-categorization huggingface dataset:
Graphical user interface A browser-based GUI has been developed to allow interactive visualization of projects based on Thoughtflow provenance data, and to facilitate simple use of the reporting tools. Software for reporting and visualization R can be used to process extracted provenance information to produce output such as comma separated value (CSV) data files, publication-ready tables (run records, QC summaries, audit trails, as rich text or LaTeX), and visualizations (flow charts and trees at varying levels of detail), and scripts for reexecution of the analysis or parts thereof.
RECOMMENDATIONS AND ANTICIPATED OUTCOMES The concepts and standards we have described here, along with the technical specification and demonstrator tool we have developed, illustrate DDMoRe's proposed Thoughtflow infrastructure. Although the software implementation that has been developed is a technology demonstration rather than a production-ready tool, the source code is open and available under the Affero General Public License, v. 3 (AGPLv3), which allows for its use by third parties provided derivative works are released under a compatible open source license. Implementations are already underway in commercial offerings such as Navigator and Improve. We anticipate that the work already done will provide a solid foundation for further development. The potential for this technology is considerable.
Acknowledgments. The authors thank  Wendy Aartsen ,  Marylore Chenel ,  Marc-Antoine Fabre ,  Mats O. Karlsson ,  Enrique Larios Vargas ,  Peter Milligan ,  C eline Sarr ,  Nadia Terranova , and  Eunice Soek Mun Yuen  for their support of and input into this work. Work performed by  Justin Wilkins  was funded by  Pfizer ,  Eli Lilly & Co. , and  Uppsala University. Work performed by  Michael Beer  and  Christian Flandorfer  was funded by  Leiden University. Work performed by  Roberto Gomeni  was funded by  AstraZeneca. The research leading to these results has received support from the  Innovative Medicines Initiative (IMI) Joint Undertaking  under grant agreement no. 115156 , resources of which are composed of financial contributions from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and  European Federation of Pharmaceutical Industries  and Association's companies in kind contribution.
Turlough water level and volume Turlough water level time series were collected at hourly resolution between June 2014 and June 2015 using Schlumberger Mini-Diver ® DI501 and DI502 pressure transducers. Compensation for the variation in prevailing air pressure was made using a BaroDiver ® (DI500) which was installed at ground level near the centre of the catchment. Depth-area relationships were required to convert turlough water levels into volume data. Thus Luirk and Gortboyheen Turloughs as well as Muckinish Lake were surveyed during summer 2014 using a Trimble 4700 GPS system which provided accuracy of 0. 01 m horizontally and vertically. Depth area relationships were then computed using Surfer 3D software and a Visual Basic script which computed the volume and planar area at given depth intervals.
The ERT profiles were acquired with a 10 channel IRIS Syscal Pro resistivity meter coupled to a 48 electrode multicore cable using a standard 2D approach  (Dahlin and Zhou, 2004)  employing the Dipole-Dipole (DD) array configuration to optimise the detection of near vertical karst features  (Satitpittakul et al. , 2013). Electrode separations of 5 m for R1, 3 m for R2 and 10 m for R3 and R4 achieved depths of investigation of up to 50 m below ground level. The DD array employs combinations of 4 electrodes at varying separations along the profile to determine subsurface bulk resistivity. These are inverted with Res2dinv software (Geotomo Software, 2010) using a finite-element forward model with a robust L 1 -norm optimisation method and a least squares inversion algorithm  (Claerbout and Muir, 1973; Loke and Barker, 1996)  that minimises the absolute difference between observed and calculated resistivities to produce sections of subsurface resistivity values.
Theoretical forward modelling, carried out for R1 and R3 (e. g. Fig. 9c ), using Res2Dmod software  (Geotomo Software, 2002) , was used to estimate outline geological conditions that fit the observed variations in resistivity. The models simulated the field acquisition procedures, with added errors to account for random noise equivalent to 5%, to approximate the observed data. Models were constructed with resistivity blocks ranging from 14 m to 50,000 m, for which the very low values suggest features such as conduits and fault zones with high water and/or clay content and the higher values indicate limestone. Fissured/fractured limestone was modelled using representative resistivity values from 100 to 500 m.
This was carried out by testing a number of hypotheses of how the system is connected based on karst hydraulic principles. The hydraulic model was built using Infoworks ICM version 6. 5 (Innovyze software). This software package incorporates the Hydroworks modelling engine and is designed for management of urban and river drainage networks. The software simulates the hydraulic behaviour of a pipe network under varying conditions of rainfall, land use, inflows etc. As the model is capable of modelling the hydraulic conditions in both open channel and pressurised flow channels, it is highly suitable for modelling a well-developed karst conduit network, as has been proved in a neighbouring karst system  (Gill et al. , 2013). The model represents the main conduit flow system in the Bell Harbour as a complex network of pipes (representing conduits) and tanks (representing turloughs), discharging at an outfall (coastal springs).
For a tank (turlough) to flood, the flow must be constricted in the downstream conduit. This is facilitated using a 'throttle' pipe which can be altered in size to create the required pressure to flood the turlough (see Fig. 4  for a conceptual diagram of the model). The only means of calibrating the model was using the water levels in the turloughs, particularly the peak water level and its subsequent recession  (Gill et al. , 2013). Water level data from Gortboyheen and Luirk turloughs between summer 2014 and summer 2015 was used for this purpose. The aim of the modelling exercise was to establish which conduit network combinations could best replicate the water levels observed in the turloughs over the 2014-2015 period. The hypothetical network combinations are discussed further in Section 4.
Acknowledgments This research was funded by the  Griffiths Geoscience Award  established in Ireland as part of the National Geoscience Programme 2007-2013 and administered by the  Geological Survey of Ireland. The views and recommendations contained in this study reflect the views of the authors and do not necessarily reflect the views and opinions of the  Irish Minister for Communications, Climate Action and Environment. We also wish to thank  David Drew ,  Colin Bunce ,  Colin Brown ,  Owen Naughton ,  Philip Schuler ,  Pat Veale ,  Fiona Stapleton  and the  Swiss Institute for Speleology and Karst Studies (SISKA)  for advice and fieldwork support. Finally, we wish to thank the landowners for access to their property.
Only present in colon cancer, a feedback loop on EGFR activation leading to the activation of the mitogen-activated protein kinases (MAPK) pathway, through RAS, may be responsible for the poor outcome. This example highlights the importance of accurately describing the networks regulating these signaling pathways and their crosstalks. To ensure efficiency of the drug treatments, a good knowledge of these complex interactions and how patient mutations affect the cellular fate is necessary. The regulatory networks used in these models are generally extracted from databases and the literature. These sources represent the current knowledge available about interactions involving the proteins of interest. However, little information is known about which regulations are specific to a given cell type or a particular biological context.
Another complementary approach to sketching the network from literature knowledge is to use database information. There exist some pathway databases that depict, in great detail, the literature curated signaling pathways. Among these databases, KEGG,  59  Reactome,  60, 61  BioCarta,  62  Wikipathways,  63  Signor,  64  Signalink,  65  ACSN, 66 etc. , can be cited. Retrieving information from them is faster than the tedious process of reviewing the literature. However, the modeler should keep in mind the limits of such resources: because they are not always up-to-date, specific but important information not included in the databases might be missed. To facilitate the retrieval of information from all these databases, we use Omnipath, 48 a comprehensive collection of existing manually curated pathway databases.
Omnipath comes with a Python tool called pypath, developed to query its content, and manipulate and filter it easily. This allows us to extract the interactions that concern only the predefined list of proteins, and execute complex queries such as retrieving all possible paths between two proteins. There are different ways in which the network can be represented. 67 For simplicity, we present our example with a type of diagram referred to as "entity relations" in the standard format SBGN,  67  where nodes correspond to genes, proteins, modified proteins, complexes, or phenotypes, and edges are directed arrows representing activation or inhibition of one node over the other. This representation provides a simplified mechanistic view of the processes involved in the disease where the details of the reactions (degradation, synthesis, phosphorylation, etc.
The second network suggests some possible links between the androgen receptor (AR) and the PI3K signaling pathway, RPS6, or cell cycle targets. Using Omnipath, signed interactions between the complete list of proteins used in the two initial networks were retrieved in order to connect them into a single network. For instance, the query related to an interaction between PI3K (PIK3CA) and AKT (AKT1) in Omnipath returns an activation of AKT1 by PIK3CA from four databases (SignaL-ink3, Laudanna_effects, Wang, Signor), and an inhibition of AKT1 by PIK3CA from one database (Laudanna_effects). We can therefore include in the network an activation of AKT by PI3K as the most confident interaction. Looking back at the four literature references associated with the interaction also confirms the sign and direction.
A detailed description of the network is reported in the Supplementary Materials, with the python code used with pypath. In general, one or more phenotypes of interest will be modeled as additional nodes (e. g. , survival, apoptosis, proliferation, etc. ) and linked to the rest of the network by appropriate edges, to facilitate model predictions. In our case study, we predict cell survival, available along with phosphorylation data,  57  as a phenotypic outcome of the model. Therefore, the resulting network was extended to describe roughly the regulation of the cell cycle and the apoptotic pathway, both of which influence an output node called "Survival. " Aiming at simplicity for this tutorial, we chose a few prominent components among the high number of possible apoptotic and proliferative factors that could be included in the PKN.
The software CellNOpt 49 provides the necessary tools to transform a logic model into a logic-based ODE model and find a set of good parameters by fitting the model to the available data. MaBoSS is a C11 software that simulates continuous time Markov processes on a Boolean network. 50 In contrast to deterministic continuous approaches, such as logic-based ODEs, this formalism handles the asynchronous updates of the states of the nodes in a stochastic way and generates a population of trajectories as sequences of Boolean states. Transition rates can be associated with each node, and probabilities of network states can be estimated given a set of initial probabilities. The main result consists of the Boolean state distribution after reaching an asymptotic behavior, and the implicit time provided by sequence steps allows considering transient dynamics.
Training with data The purpose of using data to train a network is to obtain a better representation of our system, since information available in the literature and databases may be incomplete, and specify a generic model to a particular biological system. For example, different cell types may share the same signaling networks but with some differences in their wiring, and this may not be reported in the literature. That way, networks can be refined to be cell line-or context-specific by comparing model simulations with experimental data and refining model parameters until simulations are able to reproduce the measurements. This process is named "model training" and corresponds to solving an optimization problem where an objective function that captures how well the model can describe the data is minimized.
This optimization can be performed automatically using different algorithms. The most useful data to train a model are measurements of the states of model variables under different conditions, if possible tracing their dynamic behavior. In networks, nodes typically represent proteins or complexes and a commonly used measurable proxy of their activation or deactivation are posttranslational modifications such as phosphorylation. Perturbations of the system by stimulating the pathway with ligands and/or inhibiting the proteins with targeted drugs or antibodies provide information about the dynamics, which is not available from static data. Protein phosphorylation upon perturbation is commonly measured using mass-spectrometry or antibody-based techniques. For a detailed description of the advantages and disadvantages of these techniques, we refer to Saez-Rodrigues et al.
70 n the data considered for our example,  57  the LNCaP prostate cancer cell line was perturbed with combinations of ligands (EGF, IGF1, IL6, TNF, DHT; in green in Figure  2 ) and kinase inhibitors targeting nodes in the network (PI3K, MEK, IKK, mTOR, p38; in red in Figure  2 ) for a total of 44 different conditions. The study also included data from perturbation with docetaxel, which we did not consider, as little variation in the phosphoproteome was reported in the docetaxel condition as compared to controls. Docetaxel is known to target btubulin, which is related to the cytoskeleton and has no clear effect on our network of interest. However, it is known that cytotoxic treatments can also rewire cell signaling networks. 71 More measurements in the docetaxel condition could allow studying cell signaling rewiring with logic models through network optimization.
Training the logic-based ODE model with CellNOpt First, the PKN is interpreted as a logic-based ODE as described in Wittmann et al. 32  and implemented in the R package CellNOptR. 49 The dynamic of each node is described by an ODE as a function of its regulators. For example, for the rate of change of the variable representing AKTactivity the corresponding ODE is: dAKT =dt5s AKT ðf ðPI3K ; k PI3K !AKT Þ2AKT Þ where parameter s AKT represents the life-time of the node AKT. When s AKT 5 0, dAKT/dt will also be 5 0, meaning that AKT remains at the basal value, i. e. , its initial condition. The transfer function f(PI3K, k PI3K ! AKT ) represents the regulation exerted by PI3K, and is a monotonically increasing sigmoid in the variable PI3K (in the range {0,1}). The increase rate depends on the parameter k PI3K ! AKT defining the strength of the regulatory interaction.
73 As the optimization problem is nonconvex, we solved the problem 10 times with different initial random guesses for the parameter values in order to further reduce our chances of ending the solution process in a bad local optimum (the local minima reached were actually very similar; coefficient of variation across the 10 runs 5 0. 009). Fifth, we consider as the best model the one that better fits the experimental data in terms of minimum RSS across the 10 runs (Figure  3a ) and verify that experimental data are well described (Figure  3b ) (best model is available as Supplementary Materials). If the fit is not good, the initial PKN has to be refined, as there might be missing interactions that are supported by the experimental data. This refinement can be performed with a revision of information from literature or databases, or using a combination of data-driven approaches.
Simulation of a logical model in different conditions We have just shown how it is possible to use experimental data to and refine our PKN modeled using logic-based ODEs. Some nodes and interactions, however, were omitted because no data were available to determine their underlying parameters. In this section, we will use continuous time Markov processes on a Boolean network to predict the effect of perturbations in a semiquantitative manner, including those that we could not include in the logic-based ODE model. In particular, we will focus on the probability of survival that can be predicted and compared to experimental observations. We use the software MaBoSS 50 to compute time trajectories in this graph: continuous time Markov processes generate a population of sequences (1,000 in this case) of asynchronous transitions between states of the system.
CONCLUSION In this tutorial, we outlined a methodology using logic modeling to better understand and predict the way a system responds to different perturbations. The workflow we present is generic and flexible enough to be adapted to many different other cases. Once we have chosen a system to model, the first step is to build a network based on what is known about the system (PKN). Here, public databases and resources like Omnipath help to gather and relate known information. When appropriate experimental data are available, they can be used to refine the PKN. Tools like CellNOpt help us in this process. Finally, with a functional model, using tools like MaBoSS, we can simulate different cellular and experimental conditions and predict the effect of pharmacological interventions. Red triangles represent gain of function alterations and green glyphs represent loss of function alterations.
Edges between two alterations show that a combined alteration has a drastic decreasing (in blue) or increasing (in green) effect on the Survival probability when compared to single alterations. In all these processes, the construction of the network is crucial in accurately predicting drug effects. Although not all the tools used here work under the same programming environment, the use of standard formats, in particular SBML-qual,  55  facilitate the communication between them. To get the most out of Omnipath, the use of its related Pypath python package is recommended. While the advanced features of CellNOpt are available in R (a wrapper for python is also available), MaBoSS is a command line software and Cytoscape is mainly a standalone application. To help with finding the correct network structures, experimental data coming from technology such as antibodybased arrays and/or mass spectrometry is of invaluable help.
Figure1Workflow suggested when applying logic modeling to the study of a biological question. In this tutorial we use Omnipath 48 for signaling database mining, CellNOpt 49 for model fitting, MaBoSS 50 for simulations, and Cytoscape 51 for visualization and network analysis. Different steps of the pipeline include 1) selecting a system and a question of interest and building a first version of the network, 2) choosing a modeling formalism and improving the model with data, and 3) analyzing the model, making predictions and comparing them to experimental data. The dashed arrow indicates a comparison between the results of the analysis and the experimental data. Dotted arrows represent feedback of the results into the modeling pipeline. Rounded boxes represent elements that can be considered part of the different types of the analysis.
Some of the most prominent research groups in proteomics are active in Europe, such as Prof. Matthias Mann (Martinsried, Munich, Germany), Prof. Ruedi Aebersold (Zurich, Switzerland), Prof. Albert Heck (Utrecht, the Netherlands) and Prof. Mathias Uhlén (Stockholm, Sweden). In addition, Europe also hosts worldwide renowned groups that are focused on the development and application of widely-used bioinformatics tools and resources, including MaxQuant 4 , the OpenMS framework 5 , CompOmics 6 tools, such as PeptideShaker  7  , the PRIDE database, as the worldleading proteomics repository  8  (also coordinating the global ProteomeXchange Consortium of proteomics resources  9  ), and a collection of open data standards and related software 10 , emphasizing the European leading role in the activities of the HUPO (Human Proteome Organisation) Proteomics Standards Initiative (PSI).
Each platform and use case is led by a group of senior scientists from across the ELIXIR nodes. In addition to the funding available in each national ELIXIR node, the main source of financial support for ELIXIR activities comes from the ELIXIR-EXCELERATE EU H2020 project. Additional activities are funded through other complementary grants as well as 'Implementation studies' supported by the ELIXIR Hub. The ELIXIR platforms. The Data platform focuses on sustaining Europe's life science data infrastructure. This platform is working on guidelines and indicators for data resources to improve their impact and sustainability  17. It also works on improving links between curated data resources and literature data. The Tools platform is dedicated to services and connectors to drive access and exploitation of bioinformatics research software.
The main key activities within this platform are centred to facilitate the discovery, benchmarking and interoperability of software. It does also focus on software development best practices, as well as on a strategy for workflows and software containers. The Interoperability platform supports the discovery, integration and analysis of biological data. Activities driven by this platform are organised in projects around identifiers, metadata standards and linked data. It also works on the description of interoperability services as well as specialised workshops named BYOD (Bring Your Own Data)  18  to improve the "FAIRness" (Findable, Accessible, Interoperable and Re-usable) of data resources  19. The Compute platform is dedicated to the compute, storage, transfer, authentication and authorization of biological data relying on services provided by ELIXIR nodes and e-infrastructures.
Cluster 5. Protein quantification and statistics. This topic encompasses the improvement of protein inference in shotgun proteomics approaches, the use of peptides that match to more than one protein precursor, and enhanced data integration/harmonisation for quantitative proteomics. It was perceived by many attendees that, although many of these issues are often considered solved, there are still many improvements possible in this area. Cluster 6. Metadata, standardisation, annotation, and data management. This topic also includes the improvement in the annotation of proteomics datasets, in particular in data repositories like PRIDE (to facilitate public data reuse by third parties), the development and/or extension of existing Laboratory Information Management Systems (LIMS), standard data formats, and guidelines summarising best practises for data management, following FAIR principles.