dataesr/scientific-paragraphs-categorization huggingface dataset:
CLiP: Conceptual Lifecycle Process Model CLiP is a comprehensive data model for process engineering  [9]. It is developed with an objective to generalize, extend and integrate different existing models for chemical engineering  [10]. Both interoperability problems are related to a process paradigm. Based on the above conclusions, CLiP seems like a prime candidate for modelling chemical industry processes, since: 1) it generalizes, extends and integrates different existing models; 2) CLiP is also used as a basis for development of OntoCAPE ontological framework.
OntoCAPE: Large-scale ontology for the domain of Computer-Aided Process Engineering (CAPE) OntoCAPE 2  captures consensual knowledge of the process engineering domain in a generic way such that it can be reused and shared. Some possible applications of OntoCAPE include: the systematic management and retrieval of simulation models and design documents; electronic procurement of plant equipment; mathematical modelling; the integration of design data from distributed sources. OntoCAPE can be characterized as a formal, heavyweight ontology, which is represented in the OWL modelling language. OntoCAPE has been subdivided in layers, which separate general knowledge from knowledge about particular domains and applications. Based on the above conclusions, OntoCAPE is a possible candidate for the following reasoning: 1) it has an exhaustive semantic information model for data integration across the chemical process design; 2) it can be a reference for integration and management of distributed design data, namely process designs of the different plants; 3) it is relevant for territorial scale interoperability problem; 4) it is also used as reference ontology for automated decision making related to configuration of the processes (see COGents).
ISO 15926 acts like an interpreter between two otherwise incompatible systems, by translating the descriptions of plant objects from one company's database to that of another. In doing so, the meaning of all the terms is being maintained, independently of the context. Setup for the process industries with large projects involving many parties, and involving plant operations and maintenance could take a long time. Optimising existing processes by replacing an existing component (process-scale interoperability problem) or by adding components which could facilitate energy integration (territorial-scale) presumes procurement of the installation component or at least exchange of the information sufficient to define the requirements for this component. It is clear that establishment of the correspondences between process and equipment models could contribute to facilitating the collaboration between the relevant systems (e.
COGents: Semantic approach to CAPE web service choreography The COGents project proposed an approach to dynamic CAPE services composition  [12] , where a number of software agents collaborate to configure a process model, according to the users' requirements, defined by using OntoCAPE ontology. Namely, agents are used as CAPE web services choreographers. Typical use of this approach is as following: the user defines a Modelling Task Specification (MTS) in OntoCAPE format to describe the unit he/she requires in term of functionality and parameters (of the underlying tool, e. g. HYSYS). Then, library and match maker agents find the appropriate unit operation using the generated MTS file. Based on the above conclusions, the COGents technology is a possible candidate since it provides an automated support for configuration/generation of process model, on demand, based on the user's requirements.
Authors in  [14]  provide the list of CO-compliant CAPE tools. This list is partially exhaustive; more detailed and updated list is available at www. colan. org. The majority of the candidate tools for process modelling and simulation in PFE3 architecture already provide some level of support to CAPE-OPEN integration. However, the following architecture is only a potential architecture that is not a final choice for the project. The description of CAPE objects, by CAPE-OPEN standard, is here provided together with the methodology and illustrations of some interfaces. The proposed architecture descends from the high-level architecture presented in Fig. 2. It elaborates in more detail a Process Integration Module of PFE3 platform, in the context of the possible use of CAPE-OPEN interfaces to exploit the external process modelling and simulation tools.
Plate-form(E)3 User interface It is clear, for the future design phase, that they should be grouped according to the PMC classes they are communicating with. Process modelling and simulation tools being part of PFE3 provide PMC classes which can be used by Process Integration Module, namely respective API modules: Properties API module, Unit operations API module, Numerical solvers API module and Flow sheet Analysis API module. These modules are interfaces which are wrapping the native implementations of the respective relevant functions in the Optimisation module. They have to use CAPE-OPEN objects, such as Thermo, Unit, Numeric and Simulator Executive objects. Two other modules are foreseen, to provide support functions to PIM API. PMC registry functions module facilitates adding, editing and deleting PMCs, available to PFE3 platform.
These models allow the user to perform a variety of different tasks, such as process simulation or optimisation, using this single model of the process. Interoperation is supported by CAPE middleware, implemented by using Microsoft COM, OMG CORBA or. NET technology. Process Integration Core Plate-Form(E)3 Other candidate Module technologies, approaches and tools CAPE-OPEN Modelica ISO15926 CLiP OntoCAPE OSMOSE COGents [8]E-OPEN[8]is an open industry standard for interoperability of CAPE (Computer Aided Process Engineering) software tools; it is maintained by CAPE-OPEN Laboratories Network (CO-LaN). It was developed in a joint EU initiative Global CAPE-OPEN (1997-99), later also endorsed by IMS (which gave it a global reach). Initiative combined similar efforts of BP (EU project PRIMA) and BASF (German consortium IK-CAPE).
In a recent work, using a similar PN model with SK currents we reproduced the E 1 phase and I phase  [21]. In the present paper, we describe the PN model and its parameters in detail and we consider a realistic ORN input modeled from experimental data. Based on the convergence rate in the moth pheromone system  [35] , we connected 100 ORNs to 1 PN by fast nicotinic cholinergic synapses to form a simple model of the MGC. Our model was built based on three types of experimental data: intracellular, extracellular and patch-clamp data recorded from ORNs, PNs and other neurons in insects obtained from our lab and other labs. Because ORNs do not show triphasic patterns we simply modeled each ORN firing by an inhomogeneous Poisson process. The firing frequency of the ORN model is a function of time and is fitted to the extracellular recorded data in response to pheromone stimulations varying in concentration and duration  [12].
Discussion In this work, using a simple MGC model with ORNs based on the recorded frequency curves under different pheromone stimuli, PN based on patch-clamp data from PN and other neurons and a biophysical model of nACh synapses, we reproduced the triphasic response pattern of PNs at high pheromone stimulation concentrations. We investigated mechanisms generating this triphasic response pattern. Our results show that it can be shaped by intrinsic mechanisms in ORNs and PNs: in our model Ca 2+ -dependent SK current in PN is responsible for the I phase following the E 1 phase and the E 2 phase is due to the long-lasting excitatory response of ORNs. We further investigated how the external stimulation parameters and the parameters of the internal ionic currents in PN and the nACh synaptic currents from ORNs to PN affect the duration of E 1 and I, the firing frequency of E 1 and E 2 and other response characteristics of PN.
Analysis of the PN response characteristics The PN response pattern is quantitatively characterized by duration of E 1 and I phases and frequency of E 1 and E 2 phases. These features were defined in S9 Fig and were calculated as follows and expressed as means ± standard error of the mean. Duration. The E 1 durations were measured from the first spike of the PN response to the spike just preceding the inhibitory phase. The I durations were measured from the last spike of E 1 to the first spike of E 2. Frequency. We first calculated the interspike intervals (ISIs) between successive spikes. Then the ISIs were averaged in 10 spikes around each spike using Matlab function smooth. The frequencies are the inverse of the ISIs.
This work was funded by the  Agence Nationale de la Recherche  within the  French-British ANR BBSRC SysBio 006 01 "Pherosys" initiative , the European  FP7-ICT 2007 STREP Bio-ICT convergence "Neurochem" and the state program  " Investissements d'avenir " managed by the  Agence Nationale de la Recherche  (grant  ANR-10-BINF-05  "Pherotaxis"). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Quantitative Comparison of Induced vs. Genome-Specific Head Shapes Geometric morphometrics  [73]  was used to quantify similarities and differences between head shapes of true species, as well as the experimentally derived pseudo morphologies. In brief, geometric morphometric analysis involves placement of a series of landmarks, which are both biologically significant and reproducible across all samples, removal of non-shape variation (size, rotation, etc. ), and performance of a set of statistical analysis  [74]. Landmarks were chosen based on the common biological landmarks that existed across samples, and semi-landmarks were placed with prescribed relations to these landmarks (Figure  2B ). Landmark data was recorded for n > 60 worms, including GD worms whose head shape had been experimentally perturbed by 8-OH, control GD worms who had regenerated in water for 10 days, and adult wild-type worms from each of the three species.
Principal components analyses (data not shown) and canonical variate analyses were run on the data set. This enabled visualization of mean shape changes between wild-type species morphologies, and between experimentally derived head shapes. Both analyses resulted in the separation of pseudo morphologies from the wild-type G. dorotocephala morphology. Procrustes distances between each of the groups were calculated, in order to produce a quantified metric for comparison of shape differences. The quantification was used to determine whether the shapes that looked like other species objectively resembled those species, and to suggest a continuous morphospace within which octanol-induced shape change can be visualized along a continuum (from normal to that of a different species). Canonical variate analysis supported the statistical significance of the given pre-defined morphological groupings (in this case, groupings were based on experimental treatment and morphology) (Figure  2A ).
As recapitulation of wild-type head shape after ten days of regeneration in water had been confirmed by geometric morphometrics, we chose to compare pseudo morphologies to adult worms of other species, in order to minimize confounding data due to variability of regeneration time between species. Overall shape differences were captured by measurement of the brain length/width ratio. These calculations were used to quantify shape differences between species, and to quantify brain remodeling in "pseudo" worms (Figure  4G ). Strikingly, we found that pseudo worms possessed brain morphologies that look like the brain morphologies of wild-type worms whose head shapes they resembled (Figure  4D-F ) (ANOVA, p < 0. 001). We conclude that the patterning processes that are disrupted after gap junction communication perturbation are also responsible for producing the morphology of the brain, and that the altered shapes are not limited to the overall head geometry but include the patterning of the central nervous system within.
Int. J. Mol. Sci. 2015, 16, page-page 8 analysis. Thus, we focused on G. dorotocephala, D. japonica, and S. mediterranea. We performed immunostaining using an anti-synapsin antibody, in order to visualize both the brain, and ventral nerve cords of pseudo and wild-type worms. As recapitulation of wild-type head shape after ten days of regeneration in water had been confirmed by geometric morphometrics, we chose to compare pseudo morphologies to adult worms of other species, in order to minimize confounding data due to variability of regeneration time between species. Overall shape differences were captured by measurement of the brain length/width ratio. These calculations were used to quantify shape differences between species, and to quantify brain remodeling in "pseudo" worms (Figure  4G ).
Perturbation of Bioelectric Networks Produces Inter-Species Morphological Change Our data show that in the planarian flatworm G. dorotocephala, outcomes of morphogenesis can be modified by the perturbation of physiological networks. Brief exposure to a well-characterized disruptor of electrical synapses (gap junction blocker) resulted in regenerated head shapes that quantitatively resembled those of another planarian species. We observed head shapes (and internal structures) resembling D. japonica, S. mediterranea, and P. felina. Note that other species exist with head shapes similar to each (for example, 2 other species are known that exhibit P. felina-like flattened heads); because these three types are good representatives of each shape class and are available as living specimens for analysis (unlike other members of the same morphological head types), we refer to our phenotypes as pseudo japonica, pseudo mediterranea, and pseudo felina respectively.
Thus, the model exhibits the two main properties of the experimental dataset: multiple possible outcomes (due to different patterns of GJC-blocked static cells, possibly from physiological noise in pharmacological drug-target interaction), and discrete specific outcomes (resulting from the deterministic signaling of static "black" cells and consequent behavior of migratory "red" cells). It should be noted that our model is also readily applied to an organ that grows from an initial cell position to its final position: if we allow division of cells B, cell migration proceeds in the same way as shown: they will leave behind them deactivated (differentiated) cells B that do not move and do not divide. Future work will test the applicability of this class of models to other regenerating systems.
Limitations and Future Approaches Our study faced several limitations. First, we have not yet produced cell-resolution spatio-temporal datasets on voltage and GJ connectivity during remodeling, of the kind used to extract semantics of bioelectrical states in brain activity  [80, 127]. Second, while electric current is a very likely messenger transferred through the gap junctional paths  [19, 167] , involvement of other small molecules such as neurotransmitters, IP 3 , and calcium are also possible mediators of the signaling that regulates cell behavior during head morphogenesis  [35, 119, 120, 122, 168, 169]. All of these questions can be investigated once again-of-function technology (misexpression) becomes available in the planarian model system. In addition, future experiments that could reveal differences among pseudo morphologies would target non-coding small RNAs and chromatin modifications.
Morphometrics Worms used for morphometric analysis were imaged using a Nikon SMZ1500 microscope (Melville, NY, USA) with a Retiga 2000R camera (Surrey, BC, Canada) and Q-Capture imaging software (Surrey, BC, Canada). Care was taken to image worms when their heads were most relaxed, in order to capture morphology as accurately as possible (all worms were imaged prior to fixation, which can distort the heads' external edges). Landmark data were then recorded using ImageJ (Bethesda, MD, USA)  [172]. Landmarks for morphometric analysis were chosen based on biological relevance, and reproducibility across worms with varying head morphologies. Landmarks are: (1) the anterior tip of the head; (2) and (3) along the lateral edges at a distance of 1/6th of the length of the worm from the anterior tip of the worm; (4) and (  5 ) the plane of the eyes; (  6 ) and  (7)  the widest point of the head (often the auricles, if present); (  8 ) and (  9 ) halfway between the eyes and the anterior tip of the head; and  (10)  and  (11)  the thinnest point of the head posterior to landmarks 6 and 7.
n = 10 WT (wild-type) G. dorotocephala, n = 9 WT D. japonica, n = 6 WT P. felina, n = 8 WT S. mediterranea, n = 7 pseudo G. dorotocephala, n = 13 pseudo D. japonica, n = 5 pseudo P. felina, and n = 6 pseudo S. mediterranea flatworms were measured. For measurements of brain in immunohistochemistry, the posterior edge of the brain was consistently defined as the place where nervous tissue enlarged relative to the ventral nerve cord. MorphoJ software (Manchester, UK)  [173]  was used for Canonical Variate Analysis, in order to quantify and graphically represent changes in head morphology. MorphoJ software was also used to calculate Procrustes distances, and perform statistical analyses.
Visualization of Relative Membrane Potentials with DiBAC DiBAC4(3) (bis-[1,3-dibarbituric acid]-trimethine oxanol) (Invitrogen, Carlsbad, CA, USA) was used as previously  [79, 83]. A stock solution (1. 9 mM) was diluted 1:1000 (0. 19 uM) in Poland Springs water, and worms were soaked in the DiBAC solution for >30 min before imaging. Worms were then immobilized in 2% low-meting point agarose, using custom-fabricated Planarian Immobilization Chips as in  [174]. Images of the ventral side of immobilized planaria were captured with the Nikon AZ100 Stereomicroscope, Melville, NY, USA, using epifluorescence optics, and NIS-Elements imaging software (Melville, NY, USA). Data were neither altered nor removed.
Voltage Dye Image Analysis In order to estimate the degree of gap junctional connectivity, we processed voltage dye images to determine the number of isopotential cell fields in MATLAB (MathWorks Inc. , Natick, MA, USA). Images were flat-field corrected in Image J (Bethesda, MD, USA). For each image, the area of the flatworm was found by taking the average intensity of all of the pixels in the entire image. The flatworm pixel area was used to calculate five intensity clusters of isopotential for the worm body. Each pixel of the flatworm was categorized into the closest of the five clusters by Euclidean distance. For each image, all of the pixels categorized into one intensity cluster were isolated by thresholding, regions of size less than 15 pixels were removed, and any image holes were filled.
Modeling Numerical simulations were carried out with an agent-based model where cells are considered as mass points interacting with each other. Some cells have fixed positions and some cells can move. The forces acting between cells depend on the distance between them. Cell motion is calculated as the motion of their centers according to Newton's second law. The forces acting on cells include friction/adhesion and pressure/chemotaxis from other cells. Cells can be added, moved, and activated/deactivated by the user to create the initial configuration and during a simulation, so any initial cell configuration can be established. The membrane configuration is fixed during a simulation. Figure  S2  contains additional details about the modeling. Numerical modeling was carried out with original C++ code developed for this work.
Acknowledgments: We thank  Joshua Kritzer  for manuscript suggestions;  Eva Jablonka  and  Douglas Blackiston  for informative discussions;  M. Matsumoto & H. Nodono  for images of P. felina planaria; and  Marta Riutort , for helpful information regarding planarian evolution. We also thank  Aziz Aboobaker ,  Natalia Pouchkina-Stantcheva , and  Jochen Rink  for their kind gift of live worms of various species. Maya Emmons-Bell  thanks  Chalon Emmons ,  Gregory Bell , and  Avi Kertesz  for their support. This work was supported by the  G. Harold and Leila Y. Mathers Charitable Foundation , and the  Templeton World Charity Foundation  ( TWCF0089/AB55 ).
Functional properties can be calculated in many cases through an static analysis of the transformation code but non-functional properties may require subjective quality metrics or manual analysis in order to be determined. We demonstrate the feasibility of our approach by developing a prototype implementation specially tailored for ATL  [8] , including a process to store and query transformations annotated with our DSL in a public GitHub repository. However, we would like to remark that this prototype could be easily extended to deal with other similar rule-based transformation languages like QVT  [17] , ETL  [13]  or RubyTL  [4]  as our approach remains language-independent. The rest of the paper is structured as follows. Section 2 describes our solution approach. In Section 3, our DSL for describing model transformations is detailed while Section 4 defines the process to annotate existing transformations and constitute repositories with rich search capabilities.
3 in Figure  4 ). We can also obtain some information about functional and non-functional properties along with information represented by the transformation models (e. g. , metamodels coverage or rule structure). Therefore, many different queries can be performed in order to obtain: rules with a specific value (or value range) of a requested property, modules that have some annotations related to an application domain, catalogue properties which are used more often than others, etc. For example, the following OCL query could be used for obtaining the transformation units (modules in this example) in the index model (TUAIndex) that transform UML class diagram models to relational database models. Then, over this collection, it is possible to find which of these selected transformation units have annotations about the ratioOfTables property with a low value and about the understandability property with a high value.
DSL and editors The metamodel shown in Section 3 is adapted to the case of ATL in the following way: 1) Module elements are linked to the Global Model Management metamodel for ATL  [3] , in order to store information about input/output metamodels, input/output models, etc. 2) Rule elements are connected with the ATL metamodel to represent the internal structure of each rule (type, input/output patterns, conditions, OCL expressions, etc. ). As discussed in Section 3, we have provided textual and graphical syntaxes for our DSL. Editors for these syntaxes are provided by using the Eclipse Xtext 4  and Sirius 5  tools, respectively. The default generated editors have been modified to assist in the definition of property and annotation values. This helps to create and visualize together the catalogue property definitions, the property annotations, and the relations between properties and property definitions (see Figure  5 ).
Using a GitHub repository for storing the annotated model transformations has some remarkable benefits: (a) it offers a well-known environment that makes very easy to upload or modify transformations, independently of the transformation language, via pull requests; (b) it provides an API to execute basic queries about the stored files, about the contributing users or about other metadata; (c) it gives a tracking system of the problems that may arise in the development of model transformations (through the use of "issues"); (d) it includes the possibility of reviewing the code by adding annotations anywhere in the transformation files; (e) it offers a display of the branches to check the progress and versions of model transformations; and (f) it gathers a lot of information about each user's participation in the development and improvement of the transformations.