dataesr/scientific-paragraphs-categorization huggingface dataset:
It is noted that this reaction is neither included in the IUPAC Kinetics nor JPL Data evaluation databases, thus is not necessarily included "as standard" in all atmospheric models of reactive halogen chemistry. BrNO 2 was suggested by von Glasow (2010) to be an important reservoir for Br in the near-downwind plume, based on the assumed formation of BrNO 2 from volcanic NO x and Br radicals at a rate that exceeds BrNO 2 loss via photolysis. Formation of BrNO 2 was not included in previous PlumeChem model studies  (Roberts et al. , 2009; Kelly et al. , 2013). Here, the fate of the products (BrNO 2 but also BrONO) from reaction of Br + NO 2 are investigated in more detail to evaluate the potential of BrNO 2 to influence the plume chemistry.
Aiuppa et al. (2005)  report gas flux data that show summertime variations between 800 and 2000, equivalent to 9-23 kg s -1 SO 2 , with even greater SO 2 flux during eruption periods. Burton et al. (2005)  report 7-day average SO 2 fluxes of 1000-2500 t d -1 (12-25 kg s -1 ). To illustrate the influence of variation gas flux and plume dispersion, simulations were also performed at 20 kg s -1 SO 2 flux, and for a range of dispersion and wind-speed cases. Cases C and B are introduced for more unstable atmospheric conditions involving enhanced plume-air mixing, which occur more readily at lower wind speed (< 6 m s -1 ) (see Supplement). Simulations performed at wind speeds of 10 m s -1 (case D and C), 15 m s -1 (case D and C), 5 m s -1 (case D and C), and 3 m s -1 (case C and B) are shown in Fig.
However, cumulative impacts on ozone are ongoing over the 3 h simulations. The Supplement related to this article is available online at doi:10. 5194/acp-14-11201-2014-supplement. Author contributions. T. J. Roberts, designed and performed the PlumeChem model experiments and HSC calculations and wrote the manuscript. R. S. Martin advised on HSC methodology and contributed to manuscript writing. L. Jourdain advised on scientific scope and contributed to manuscript writing.
Acknowledgements. This study was financed by  LABEX VOLTAIRE (VOLatils-Terre Atmosphère Interactions -Ressources et Environnement )  ANR-10-LABX-100-01  ( 2011-20 ) and an NSINK career development allowance that enabled HSC software purchase. R. S. Martin  acknowledges  Christ's College, Cambridge  for a research fellowship. We are grateful to  R. Sander ,  C. Kern  and an anonymous reviewer, whose feedback helped to improve the manuscript. Edited by:  W. Birmili.
Background: The TGF-β transforming growth factor is the most pleiotropic cytokine controlling a broad range of cellular responses that include proliferation, differentiation and apoptosis. The context-dependent multifunctional nature of TGF-β is associated with complex signaling pathways. Differential models describe the dynamics of the TGF-β canonical pathway, but modeling the non-canonical networks constitutes a major challenge. Here, we propose a qualitative approach to explore all TGF-β-dependent signaling pathways. Results: Using a new formalism, CADBIOM, which is based on guarded transitions and includes temporal parameters, we have built the first discrete model of TGF-β signaling networks by automatically integrating the 137 human signaling maps from the Pathway Interaction Database into a single unified dynamic model.
Temporal property-checking analyses of 15934 trajectories that regulate 145 TGF-β target genes reveal the association of specific pathways with distinct biological processes. We identify 31 different combinations of TGF-β with other extracellular stimuli involved in non-canonical TGF-β pathways that regulate specific gene networks. Extensive analysis of gene expression data further demonstrates that genes sharing CADBIOM trajectories tend to be co-regulated. Conclusions: As applied here to TGF-β signaling, CADBIOM allows, for the first time, a full integration of highly complex signaling pathways into dynamic models that permit to explore cell responses to complex microenvironment stimuli.
Considerable effort has gone into mining the literature to build signaling databases and provide a view of cell signaling pathways through descriptive graphbased representations (KEGG  [25] , Ingenuity Pathway  [26] , Biocarta, Reactome  [27] , Pathway Interaction Database (PID)  [28] ). While all of these approaches improve graph-based analyses of signaling pathways, the translation schemes used to translate biological data into discrete variables and the parameterization of reaction times relative to each other to obtain dynamic models raise many difficulties. Within a given database, distributed knowledge blends various biological concepts such as biochemical reactions and functional processes and includes heterogeneous levels of details that do not permit automatic translation into discrete models.
More recent databases such as Reactome  [27]  and the Pathway Interaction Database (PID)  [28]  use a homogeneous concept of biological reactions based on mechanistic information to facilitate further formal interpretations and development of large-scale systems, and a Boolean framework was recently proposed for modeling cellular signaling from the whole Reactome database. In this case, however, parameterization of time remains to be achieved  [29]  as discrete models do not easily lend themselves to the handling of logical time based on partial ordering of events. In synchronous time models, all components change simultaneously while only one component of the state is allowed to change at each step of model evolution in asynchronous time models. While mixed-rule dating has been proposed to obtain a more realistic view of biological events  [30, 31] , these approaches still fall short of successfully modeling signaling reactions.
To address these daunting difficulties, we have developed a new non-ambiguous formal interpretation of signaling pathways as discrete dynamic models. This interpretation differs from previous approaches in that it models the signal rather than the molecular biochemical network that transmits the signal. The resulting language, Computer-Aided Design for BIOlogical Models (CADBIOM), is based on a simplified version of guarded transitions  [32]  in which we introduced temporal parameters for each transition. This leads to high degree of flexibility in signal distribution and allows, for the first time, the construction of a fully integrative discrete dynamic model of TGF-β signaling pathways. To do so, we integrated the whole PID database into a single unified model containing 9177 biomolecules that include all TGF-β related signaling networks.
Building a TGF-β CADBIOM model To translate and integrate any kind of biological reaction involved in TGF-β signaling pathways, we developed a new language, CADBIOM, based on guarded-transition formalism (see Methods and Additional file 1: Figures  S1  and S2 ). Briefly, a biological reaction is formalized as a transition from input biomolecules to output biomolecules under some conditions or guards. Conditions involve biomolecules as activators or inhibitors and the biological reaction takes place only if the inputs are present and the condition is true. We then consider that the occurrence of a biological reaction induces information propagation from each input to each output. Next, all the biological reactions are integrated by connecting the biomolecules: the target of a transition becomes the origin of the next one.
The resulting network is turned into a dynamic system according to the choice of transitions that are fired at each step. To overcome the limitations of synchronous or asynchronous models, we introduce events, discrete signals that guide or restrain the choice of fireable transitions. The potential firing of reactions is dependent on the presence or absence of events that is formalized within the guard. By default, events are initially attributed to each transition, except for the formation or dissociation of complexes that share the same event since both components are simultaneously present. Taking advantage of the mechanistic view of the PID database, we extracted TGF-β signaling-pathway knowledge by developing a program that automatically translates XML files from PID into CADBIOM language.
To overcome this obstacle and identify all components related to TGF-β, we first built a single CADBIOM model for the complete PID database. As illustrated in Figure  2 , all 9248 reactions from 137 PID pathways were integrated into 9264 CADBIOM transitions. Note that transitions and reactions are not equivalent since a reaction for the formation of a complex between two biomolecules must be translated into two transitions. In contrast, two different reactions described in PID and sharing the same inputs and outputs can be translated into one or several CADBIOM transitions. The efficiency of integration is demonstrated by the reduction of the 27876 biomolecules that are part of the 137 PID pathways to 9177 non-redundant places in the CADBIOM model. Indeed, one PID biomolecule can be implicated in several reactions and/or pathways, while a CADBIOM biomolecule is represented by a unique place and all of its reactions are either incoming or outgoing transitions.
It is important to note that the translation and integration of the 137 PID pathways into a single CADBIOM model does not alter the distribution of the ontology terms associated with biomolecules, ruling out loss of information resulting from integration (Additional file 1: Figure  S3 ). Based on this complete signaling model, we next sought to delineate parts of the network that influence TGF-β signaling pathways. We did this by creating an activation graph from all PID information using biomolecules as nodes and the dependencies between biomolecules as edges (Figure  3A ). The dependency relationships include transitions and the influence of conditions on the output of transitions. The resulting influence graph illustrates all the relations between the biomolecules of the database, not only in terms of information propagation, but also in terms of regulation of reactions.
Quite spectacularly, all the information from PID is highly connected with a major component containing 8986 nodes (98% of the total PID content) included TGF-β, indicating that integration of TGF-β signaling amounts to compiling the entire database. Most importantly, this integration is achieved without discarding non-canonical pathways that are not described as TGF-β-related in PID. Based on the distribution of the connectivity and modularity analysis, we showed that the network exhibits scale-free behavior (Figure  3B ) and clustering of connected components identifies a TGF-β related associated module (Figure  3C , Additional file 2: Table  S1 ). Taken together, our data provide the first dynamic discrete model of the TGF-β signaling network, including both Smad and non-Smaddependent pathways, which integrates all networks that influence the cellular response to TGF-β.
Four of these genes, DAPK1, COX_2/PTGS2, JUN and NOS2, are indeed functionally associated within the "cancer pathways" from the KEGG pathway database. CADBIOM also identified a much larger number of TGFβ-dependent genes that were reachable in the absence of Smad proteins, resulting in a set of 122 genes with non-Smad-dependent trajectories (See Additional file 4: Table  S3 ). Importantly, all trajectories containing Smad proteins are associated with genes regulated by TGF-β as their unique extracellular stimulus, while trajectories induced by TGF-β associated with other extracellular stimuli never contained Smad proteins (Additional file 5: Table  S4 ). To assess the functional association of target genes within each group, we performed gene ontology annotation using the Database for Annotation, Visualization and Integrated Discovery, DAVID (Figure  4C ).
Indeed CCR5 is a beta-chemokine receptor that binds HIV, and Mip1A and B are major HIV-suppressive factors that bind CCR5. Additionally, Granzyme A and B are serine proteases that mediate apoptosis of virus-infected (See figure on previous page. ) Figure  1  CADBIOM model for the canonical TGF-β pathway. (A) Schematic representation of the canonical pathway. Binding of TGF-β to the receptor induces recruitment and phosphorylation of Smad2/3. Activated Smad2/3 heterodimerizes with Smad4 and the complex migrates to nucleus where it regulates gene transcription. (B) Two examples of translations from the PID database (left) into the CADBIOM representation (right). Top: interaction of Smad2/3 with Smad4. The CADBIOM representation uses two transitions from a component toward the complex, conditioned by the presence of the other component and leading to the synchronization of the transition.
These functional links revealed by CADBIOM analysis have not been previously reported using other modeling approaches. Taken together, these data are in accordance with and strengthen the known concept of Smad-and non-Smad-dependent TGF-β pathways and provide for the first time trajectories for regulatory ligands. Of note also is the identification, through CADBIOM analyses of trajectories for gene regulation, of the 31 combinations that associate TGF-β with other extracellular stimuli to drive gene regulation through Smad-independent pathways. In addition, CADBIOM identifies the TGF-β/IL12 association as a novel basic module for signaling networks involved in the regulation of 9 genes implicated in the response to viral infection. Because such complex associations cannot be evaluated by classical experimental approaches using in vitro cell stimulation, we investigated the biological relevance of these combinations by analyzing co-expression of genes sharing extracellular stimuli.
CADBIOM trajectories are associated with co-expression of genes The cellular microenvironment induces numerous simultaneous stimuli and cells respond according to the integration of all of these signals. For a given set of stimuli, signaling pathways activate gene expression profiles that can be identified using transcriptomic methods. Based on this supposition, we expected that two genes regulated by TGF-β together with other extracellular stimuli might be found to be co-regulated in biological samples. To test this hypothesis, we analyzed gene expression of each pair of genes sharing extracellular stimuli by using experimental data from Gemma, a database for the meta-analysis of gene expression profiles  [33]. Based on the available set of 2110 Gemma human profiling expression studies, we determined whether two genes sharing a combination of TGF-β and another stimulus are co-expressed or not.
In this case, 19 out of identified 31 combinations were found to regulate at least two genes. As shown in Figure  6 , gene pairs activated by similar combinations were found to be significantly co-expressed, validating the biological relevance of trajectory analyses. As illustrated in Figure  7  for CCR5 and MIP1B, CADBIOM analysis can lead to the identification of genes that are co-expressed and share combinations, thus uncovering new and highly complex common trajectory networks. To generalize this key observation to any genes from the model, we randomly generated 649 pairs of genes present in both the CADBIOM model and the Gemma database (Additional file 6: Table  S5 ). Extracting all gene pairs that share signaling trajectories from the CADBIOM model and the corresponding co-expression data from Gemma reveals that 81% of co-expressed gene pairs in Gemma share regulatory signaling trajectories in the CADBIOM model.
Conclusions Signaling pathway networks orchestrate cell life through a uniquely connected complex molecular circuitry for propagation of information. Because of the huge number of biological reactions that are implicated, modeling cell signaling to predict cell responses to this huge flow of information is a major challenge that requires new approaches if all information is to be dynamically integrated. In the present work, we have developed a new formalism, CADBIOM, based on guarded transitions and combined discrete abstraction to propose, for the first time, a fully integrated model for TGF-β signaling pathways. Major issues in modeling biological large-scale phenomena are the collection of information from the literature. While cell signaling pathways are described in numerous databases, a recent report demonstrated a high degree of inconsistencies when different databases are compared  [34].
Based on the Jaccard similarity coefficient, the authors compared four well understood pathways that involve the cytokines EGF (Epidermal growth factor), TGF-β (Transforming growth factor), TNFα (Tumor necrosis factor) and the signaling protein WNT (winglesstype) described in six databases, including GeneGo (www. genego. com), KEGG  [25] , NCI-PID  [28] , NetPath  [35] , PANTHER  [36]  and Reactome  [27]. Only 10% similarity was found, suggesting that the description of each pathway is database-or even curator-specific. To circumvent such potential pitfalls, we extracted instead all relevant information from TGF-β pathways using a single database, PID, integrating them into a single unified model. The choice of PID is fully justified by the observation that, unlike other databases, PID formalizes signal propagation instead of describing biochemical reactions.
This means that an interaction is described as a biological event that includes its participating molecules and conditions, an interaction which consumes its inputs and produces its outputs. This overall description is close to that used for guarded-transition systems  [32] , a state/event formalism that can represent both flow circulation with transitions and natural composition rules, and remote influences with transition guards. The direct translation of the complete XML-formatted database content into CADBIOM formalism then allows for the integration of information and automatically creates the first dynamic model of the TGFβ signaling network. Transient receptor potential cation channel, subfamily V, member 1 The Table  lists  genes with SMAD-dependent trajectories. Gene names (ID) are given according to their nomenclature in the PID and HUGO databases, respectively, and are followed by their description.
Asterisks denote genes that are also found with non-SMAD-dependent trajectories (see Text and Additional file 4: Table  S3 ). Three TGF-β signaling pathways are described in PID: TGF-β receptor signaling, regulation of cytoplasmic and nuclear Smad2/3 signaling and regulation of nuclear Smad2/ 3 signaling (see Figure  1 ) and summarize the canonical TGF-β pathway. We compiled these into CADBIOM and, importantly, also non-Smad TGF-β pathways and all their influencing components (see Figure  2 ). The resulting single unified CADBIOM model is a highly connected graph containing 9177 places where 98% of nodes directly or indirectly influence TGF-β-dependent signals. Note that this high connected component contained also other receptor/factors. An important point to consider in automatic translation from a database is the continuous upgrading of the generated model.
Formalisms used in UML (Unified Modeling Language) or in state-charts  [38]  include an event in the transition guard but lack an operation for combining events into new events. In contrast, using CAD-BIOM formalism, the timing of model evolution is directly linked to the biological reactions by associating an event to each reaction. Consequently and although the transition systems use a Boolean expression for the guard, CADBIOM formalism differ from classical Boolean approaches  [29, [39] [40] [41] [42]  because the temporal order of the reaction is not forced (synchronous or asynchronous) but included in the guard. CADBIOM modeling permits simultaneous changes to several reactions and the exploration of all potential behaviors. The resulting modeling framework is much richer since simultaneous reactions are neither excluded nor imposed.
CADBIOM CADBIOM software is covered by a GNU-public license that permits the conception, simulation and questioning of the discrete dynamic models describe in this article. All these features can be reached using the graphical user interface or through its application programming interface (API). Automatic conception of models is made possible through the PID database translation scheme (XML format). Resulting models can be stored in Cadlang, a text representation of CADBIOM models. CADBIOM is freely available at http://cadbiom. genouest. org/.
To handle integrated models containing large amount of data, we combine events and states. Operations on events are well known in computer science. The two basic operators are a merge that corresponds to multiplexing and a selection of occurrences corresponds to under-sampling. CADBIOM borrows the default operator and the when operator from the Signal language  [53]. The default operator merges the two events h 1 and h 2. The event h = (h 1 default h 2 ) is present when either the event h 1 or the event h 2 is present, h is absent otherwise. The when operator is an operator between events and logical combinations of state variables and selects occurrences of an event when the propositional formula evaluates to True on its righthand side. The event h = (h 1 when B) is present when h 1 is present and B is True, it is absent otherwise.
The h out event is present if at least one of the out-transitions is fired. The mathematical formalization of the rules is given by: A' ¼ h in default Àjh out ð Þ default A where A' is the value of A at next step. The initial condition for simulation analyses requires the design of activated places (called scenario) and a specific timing. CADBIOM proposes either a manual selection or an automatic selection of "frontier" places. Frontier is the set of places which cannot be activated from inside the model. In guarded transition models, places without input transitions belong to the frontier. The script for events timing is provided during reachability analyses as a txt file that can be loaded in simulation tool.
Compilers CADBIOM software includes several compilers sharing the same back-end. The front-ends compile PID XML files, CADBIOM XML files and Cadlang files into an intermediate representation of guarded-transition models. The common back-end generates logical constraints in propositional clause form. During this step, constant propagation and common sub-expression elimination optimizations are performed. Combined with many peephole optimizations, these techniques allow the back-end to generate reduced sets of constraints that facilitate the work of the SAT solver.
Resources The CADBIOM model is a translation of the PID database content (http://pid. nci. nih. gov/). See Additional file 1: Figure  S1  for the complete translation scheme. Modelchecking analysis is performed using the cryptominisat SAT solver (http://www. msoos. org/cryptominisat2/). Graph representations of solutions are displayed using Gephi, an open-source platform for graph visualization (https://gephi. org/).
Acknowledgements This work was supported by the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Ligue Nationale Contre le Cancer  and the  National Research Agency (ANR). The authors would like to thank  Dr. Emmanuel Käs  ( CNRS UMR 5099, Toulouse, France ) for help in writing this manuscript and the  GenOuest Bioinformatics Platform  for hosting the CADBIOM website.
Indeed, one of the issues related to FPGA-based SDR is the design flow, essentially based on hardware description languages (HDLs), which are used to program the applications. These languages, namely Verilog or VHSIC hardware description language (VHDL), represent an important burden and http://asp. eurasipjournals. com/content/2014/1/164 limit dramatically the programmability of the SDR platforms. In order tackle this issue, abstraction has been raised through software languages which were proposed to target FPGAs. High-level languages (HLLs) are software languages that generate hardware description (RTL) from abstracted software specifications  [7]  for FPGA or ASIC targets. They raise the level of abstraction and bridge the gap between high-level algorithm designers and low-level circuit architecture designers.