dataesr/scientific-paragraphs-categorization huggingface dataset:
Navigation of ACSN-ReconMap 2. 0 resource using NaviCell platform ACSN and ReconMap 2. 0 maps were linked via the common players. Now that proteins had been displayed on top of the ReconMap 2. 0 network canvas, those common with ACSN will be used as links to shuttle through both maps. Exploration and shuttling between the two map layers is possible using the NaviCell Google Maps-like features  [5]. By clicking on a protein existing in both maps, a window with annotations will appear and a 'globe' icon will be clickable in the list seen in the 'Maps_and_Modules' section. A new window will be opened and the corresponding protein will be shown on this called map. In addition, the exploration of the ACSN-ReconMap 2. 0 resource is facilitated by the semantic zooming principle of NaviCell platform.
As navigating large geographical maps, the semantic zooming on the molecular networks consists in hiding low-level invisible details at a less detailed level of zoom with simultaneous transforming and changing the scale of the representation of the essential objects by creating their abstractions. This principle can be used for browsing large comprehensive maps of molecular mechanisms such as ACSN  [3]  and ReconMap 2. 0  [1]  thanks to the existing open code of Google Maps API.
Discussion and conclusions A systems biology approach involving integration of signalling and metabolic networks permits characterisation of cross-links between the two types of molecular mechanisms in different conditions (e. g. healthy and disease). The integrated ACSN-ReconMap 2. 0 resource provided under the NaviCell platform opens an opportunity for a full exploitation of multi-omics data using visualisation features of NaviCell  [4]. NaviCell allows to visualise and analyse data based on hierarchical structure of ACSN modules and ReconMap 2. 0 subsystems respectively, evaluating 'activity' of modules and subsystems thanks to the map staining techniques  [5]. ACSN and ReconMap resources are constantly extended with new discoveries in the corresponding fields. Future modifications of these networks will be maintained and the workflow described in this manuscript will be reused to allow updates of the integrated ACSN-ReconMap 2.
0 resource. The developed networks integration methodology and the suggested workflow is a generic mechanism and can be easily applied for integration of other comprehensive maps. The robustness of the method, computational speed and memory usage allows it to be used on any computer with Python and Java installed. Scripts used are open-source and accessible on GitHub (https://github. com/sysbio-curie/CellDesigner_networks_map_integration_procedure). In this manuscript, we have shown that the merging of metabolic and signalling networks can be achieved and it provides many possibilities for data analyses and comprehension of implicated processes across both maps. In addition, the integrated resource allows to find gaps in connectively between signalling and metabolic processes and suggests exploration of potential links.
Map entity annotation with NaviCell format The annotation panel followed the NaviCell annotation format of each entity and reaction of the maps includes sections 'Identifiers', 'Maps_Modules', 'References' and 'Confidence' as detailed in  [3]. 'Identifiers' section provides standard identifiers and links to the corresponding entity descriptions in HGNC, UniProt, Entrez, SBO, GeneCards and cross-references in REACTOME, KEGG, Wiki Pathways and other databases. 'Maps_Modules' section includes tags of modules in ACSN and metabolic pathways in RecoMap 2, in which the entity is implicated. 'References' section contains links to related publications. Each entity annotation is represented as a post with extended information on the entity.
Generation of NaviCell map with NaviCell factory NaviCell Factory (https://github. com/sysbio-curie/NaviCell) is a package allowing to convert a CellDesigner map annotated in the NaviCell format into NaviCell Google Maps-based environment. This result in a set of HTML pages with integrated JavaScript code that can be launched in a web browser for online use. HUGO identifiers in the annotation form allow using NaviCell tool for visualisation of omics data  [5]. The detailed guidelines for the usage of NaviCell factory, embedded in the BiNoM Cytoscape plugin, is provided at https://navicell. curie. fr/doc/NaviCellMapperAdminGuide. pdf.
Acknowledgments This work has been supported by the  COLOSYS  grant  ANR-15-CMED-0001-04 , provided by the  Agence Nationale de la Recherche  under the frame of ERACoSysMed-1, the  ERA-Net  for Systems Medicine in clinical research and medical practice and by  PRECISE   H2020 EU grant. This work received support from  MASTODON program  by  CNRS  (project  APLIGOOGLE ).
SBGN is compatible with multiple pathway drawing and analytical tools, http://sbgn. github. io/sbgn/ SBML Systems Biology Markup Language (SBML) is a representation format, based on XML, for communicating and storing computational models of biological processes. It is a free and open standard language with widespread software support, http://sbml. org Standard identifier (ID) Community-accepted nomenclature for scientific naming of biomolecules as genes, proteins, chemicals, drugs etc. Signalling and metabolic network map Map Diagram of detailed molecular interactions with meaningful layout reflecting a certain biological process, which is graphically represented in CellDesigner tool. Map module (in ACSN) Part of the map representing a sequence of molecular interactions responsible for execution of a particular function.
The propagation rate coefficient (k p ) for TBDMSMA free-radical polymerization was determined by pulsed-laser polymerization (PLP) in conjunction with the analysis of the molecular weight distribution (MWD) via size-exclusion chromatography (SEC). Determination of k t was carried out using kinetic method and via steady-state rate measurement. Modeling of the time evolution of molecular weight, monomer conversion, RAFT agent conversion, and dispersity at various initial RAFT agent concentrations using PREDICI ® software (Dr. M. Wulkow Computing in Technology, Rastede, Germany) was comprehensively studied and compared to experimental data. The aim is to evaluate the rate coefficients, especially those associated with the addition-fragmentation equilibrium and to give a better understanding of the mechanism and the kinetics of the RAFT polymerization of TBDMSMA.
Simulation The PREDICI ® model for the CPDB-mediated polymerization of TBDMSMA was constructed on the basis of kinetic and thermodynamic parameters from literature. The kinetic steps for RAFT polymerization were shown in Scheme 1. They were implemented into the PREDICI ® program package using the kinetic parameters, the initial values of reactant concentrations, and the individual reaction steps. The program package uses a method of approximation for countable differential equation systems and a special time discretization, which is especially efficient in the context of polymerization reactions  [14]. For the CPDB-mediated TBDMSMA polymerization, the kinetic scheme consists of the initiation, a pre-equilibrium, propagation and re-initiation, the main or core equilibrium, and termination processes.
Scheme S1 (in Supplementary Materials) describes the translation of the chemical model given in Scheme 1 into a form that can be implemented into the PREDICI ® program package. For the main equilibrium, two fictive species Q 1 and Q 2 (each acting as a chain-length memory for the macroradical RAFT species) are introduced to overcome the difficulty to assign two different chain lengths to one radical species. Further detailed discussions about the implement of the RAFT process in PREDICI software can be found in the literature  [15] [16] [17] [18]. Polymers 2018, 10, x FOR PEER REVIEW 5 of 15 Scheme 1. Detailed mechanism of the RAFT process.
8 × 10 3 and 1. 8 × 10 7 L•mol -1 •s -1 ) or remained constant but changing simultaneously the magnitude of kβ and k-β (kβ between 1. 8 × 10 3 and 1. 8 × 10 5 L•mol -1 •s -1 and k-β between 2 × 10 -3 and 2 × 10 -1 s -1 ). Unfortunately, no couple of kβ and k-β other than the values reported in Table  4  allows a better description of the experimental data. The evolution of Đ with reaction time matches well with theoretical values for the simulation, but the experimental values of Đ are below the theoretical line; i. e. , the number-average molecular weights are higher than the theoretical values. This could be due to the fact that PREDICI ® software takes into account all species, including very short (dead) oligomers, while SEC analyses do not consider all species (due to the position of the low-molecular weight integration limit and differences in refractive index), which would also lead to values of Mn above the theoretical line  [27].
8 × 10 3 and 1. 8 × 10 5 L•mol -1 •s -1 and k -β between 2 × 10 -3 and 2 × 10 -1 s -1 ). Unfortunately, no couple of k β and k -β other than the values reported in Table  4  allows a better description of the experimental data. The evolution of Đ with reaction time matches well with theoretical values for the simulation, but the experimental values of Đ are below the theoretical line; i. e. , the number-average molecular weights are higher than the theoretical values. This could be due to the fact that PREDICI ® software takes into account all species, including very short (dead) oligomers, while SEC analyses do not consider all species (due to the position of the low-molecular weight integration limit and differences in refractive index), which would also lead to values of M n above the theoretical line  [27].
Acknowledgments: This research is funded by  Vietnam National Foundation for Science and Technology Development (NAFOSTED)  under grant number  104. 04-2012. 61. The authors would like to thank the  Institut de Chimie Radicalaire  ( UMR7273 ), France for allowing us to use PREDICI software and  M. Buback  of the  Institut für Physikalische Chemie, Georg-August-Universität Göttingen  for his kind support regarding the determination of k p.
2 Throughout this paper we use the ΛCDM cosmology with Ω m =0. 27, Ω Λ =0. 73 and H o =71 km s -1 Mpc -1. Figure  1. SDSS color representation (100 ′′ ×100 ′′ ) of the quasar (SDSS J124355. 78+404358. 4, z q = 1. 527) -galaxy (SDSS J124357. 17+404346. 1/UGC 07904, z g =0. 017) pair. The quasar sight line passes through the galaxy at an impact parameter of 20. 3 ′′ (6. 9 kpc at the z g ) as measured from the center of the galaxy. Dashed lines mark the orientation of the slits used in the IGO observations. redshifts are from the SDSS database, and the mean redshift of the galaxy group is 0. 0174. To study this system in detail, we have performed longslit spectroscopic observations of the QGP J1243+4043 using 2-m optical telescope at IUCAA 3  Girawali Observatory (IGO) to measure the quasar redshift, line of sight reddening and the properties of the ionized gas in the galaxy.
We have used radio data from the Giant Metrewave Radio Telescope (GMRT), the Westerbork Synthesis Radio Telescope (WSRT), and the global-VLBI array consisting of the Very Long Baseline Array (VLBA) and the European VLBI Network (EVN) to map the large scale H i 21-cm emission from the QGP and the associated galaxy group, and detect 21-cm absorption towards the background quasar at arcsecond and mas scales. The layout of this paper is as follows. In Section 2, we present details of optical and radio data used for this study. Results and discussion are presented in Section 3. We also discuss the unusually high RM of quasar J1243+4043 in the context of other polarized quasars with DLAs and 21-cm absorbers. A summary of the results is presented in Section 4.
GMRT observations We observed the QGP J1243+4043 using the GMRT 1. 4 GHz receiver on 2010, July, 1. The pointing center was the position of the quasar. The baseband was centered at the redshifted 21-cm absorption frequency corresponding to the galaxy redshift, z g = 0. 0169, as measured from the SDSS spectrum. We observed the standard flux density calibrator 3C 286 for 10 to 15 min every 2-3 hr to obtain a reliable flux and bandpass calibration. The compact radio source J1227+365 was also observed for 7 min every ∼50 min for phase calibration. The total on-source time was ∼4. 6 hrs. The data were acquired using both the hardware and software backends. The H i 21-cm absorption spectrum obtained using the data from GMRT hardware correlator are presented in  Dutta et al. (2017b). Here we present the higher spectral resolution data with a ∼4 MHz bandwidth split into 512 channels, acquired through the GMRT software backend (GSB).
These data were reduced using the Automated Radio Telescope Imaging Pipeline (ARTIP) that is being developed to perform the end-to-end processing (i. e. from the ingestion of the raw visibility data to the spectral line imaging) of data from the uGMRT and MeerKAT absorption line surveys. The pipeline is written using standard python libraries and the CASA package. Details will be presented in a future paper. In short, following data ingestion the pipeline automatically identifies bad antennas, baselines, time ranges and radio frequency interference (RFI), using directional and median absolute deviation (MAD) statistics. After excluding these bad data, the complex antenna gains as a function of time and frequency are determined using the standard flux/bandpass and phase calibrators. Applying these gains, a continuum map that uses a user-defined range of frequency channels (200-450 -excluding channels with absorption, in the case of QGP J1243+4043) is made.
WSRT observations The QGP J1243+4043 was observed with the WSRT at 1. 4 GHz in Maxi-Short configuration using a baseband bandwidth of 10 MHz split into 2048 frequency channels. The telescope was pointed at the quasar coordinates. The total on-source time, split over five observing runs between 2010, October, 25 and 2011, January, 29, is ∼21 hr. The standard flux density calibrators 3C 147 and 3C 286 were observed at the beginning and the end of each observing run for flux and bandpass calibration. The editing of bad data, and the flux and bandpass calibration were done using the AIPS. After this, the uv data from different observing runs were concatenated using the AIPS task DBCON. An initial continuum image was made using line-free channels, and this was then self-calibrated. The image was made using ROBUST=0 weighting, and has a beam of 30.
7 ′′ ×13. 9 ′′ with a position angle = 31 •. The quasar J1243+4043 in this map has a flux density of ∼200 mJy, consistent with the GMRT observations. The complex gains from self-calibration were applied to the line uv dataset. The radio continuum-subtracted line dataset was then shifted to the heliocentric frame using the AIPS task CVEL and also Hanning smoothed to a velocity resolution of ∼4 km s -1 (i. e. similar to the resolution of the GMRT dataset used for the analysis). We imaged this line dataset using the CASA package to obtain H i cubes and total H i maps. For the 21-cm absorption line analysis, we made an H i cube using the same weighting and beam size as the continuum image. The H i cube was CLEANed down to twice the single channel noise using image masks. The masks were iteratively enlarged after each major cycle of CLEAN to include the pixels with detectable extended emission as determined from the H i cubes spatially smoothed using a Gaussian kernel.
Global VLBI 21-cm absorption The global VLBI observations of quasar J1243+4043 were carried out on 2011, June, 8. In total 16 stations, 10 from the VLBA and 6 (i. e. Effelsberg, WSRT as a phased array, the Jodrell Bank (Lovell) telescope, Onsala-25m, Medicina and Torun) from the EVN were used. The interferometric data from the WSRT dishes were also recorded in order to measure the total continuum flux density of the quasar at arcsecond scales. The source 3C286 was observed for the flux density and bandpass calibrations. For VLBI, we observed 4C 39. 25 and 3C 345 as fringe-finders and bandpass calibrators, and J1242+3751 as the phase calibrator. The total observing time was 12 hrs. The EVN participated only for the first 10 hrs. The aggregate bit rate per telescope was 128 Mbit/s. We employed 2-bit sampling and split the data to 4×4 MHz sub-bands in each of the polarizations (R, L).
The VLBI data were correlated at EVN MkIV Data Processor at the Joint Institute for VLBI in Europe (JIVE) in Dwingeloo, the Netherlands in two separate passes with an averaging time of 2 seconds. The low spectral-resolution continuum pass had 16 spectral channels in each sub-band. In the line pass the second band which was centered at the redshifted 21-cm frequency was correlated to yield 1024 spectral channels. The shortest baseline in the data is ∼250 kλ, where λ is the observing wavelength. Conservatively, this implies that the data are only sensitive to structures < 1 ′′. We followed the standard data reduction procedures in AIPS to calibrate these data. The continuum and line images were made following the steps as outlined in the Sections 2. 2 and 2. 3. The VLBI radio continuum image made using ROBUST=0 weighting has a beam size and rms of 7.
0 mas×2. 7 mas (Position angle=-15 • ) and ∼100 µJy beam -1 respectively. For spectral line analysis, the H i data cube from the VLBI data was made using ROBUST=0 weighting and has the same beam size as mentioned above. The H i cube, at the full spectral resolution of ∼0. 9 km s -1 , has an rms of ∼0. 7 mJy beam -1. In the spectrum of the quasar, we detect 21-cm absorption at the frequency expected from the GMRT and WSRT datasets.
Redshift of the quasar and line of sight reddening The IGO spectrum extracted from slit orientation 2 at the location of the quasar (see Fig. 1 ) is shown in the left panel of Fig 2. The spectrum shows two strong emission lines which we identify as Mg ii and [C iii] emission at z em = 1. 5266 +/-0. 0032. In the figure, we also overlay the redshifted SDSS composite quasar spectrum from Vanden  Berk et al. (2001). It is interesting to note that the composite spectrum roughly reproduces the IGO spectrum of the quasar. This suggests that the line of sight reddening towards the quasar is limited. In order to see how the relative colors of the quasar compare with a typical quasar at a similar redshift, we gathered colour information of 3000 quasars in the SDSS database with 1. 525≤z em ≤1. 575. Following  Richards et al.
Specifically, in this sample only the 21-cm detections are associated with sight lines through optical/H i disks of galaxies and can be classified as a DLA (for T s =100 K). The location of J1243+4043 relative to the distribution of RMs and polarized fractions in this new catalogue where control refers to the sight lines with 21-cm non-detections is shown in the bottom panels of Fig. 8. Critically, there is no detectable difference between the control versus DLA or 21-cm absorber samples in either catalogues. The only difference between the datasets are the distributions of the high-versus low-redshift RMs. We calculate a Bayesian probability of 71. 0 +6. 2 -7. 7 % that the RM of J1243+4043 is greater than the RM of the low-z DLA sample. Such a probability indicates no significant effect. Any additional RM contribution from the plasma of the intragroup medium (IGrM; see Section 3.
To constrain the latter scenario of a varying Galactic RM foreground between samples, we use the best available model from  Oppermann et al. (2015). A plot of the distribution of Galactic RMs (GRMs) towards each source is shown in Fig. 9. There is again no difference between specific samples. We calculate a Bayesian probability of 61. 5 +18. 5  -17. 1 % that the GRM towards J1243+4043 is greater than the GRM towards the low-z DLA sample based on 21-cm absorption measurements. This is again consistent with no effect. The combination of no difference in RM, together with no difference in GRM, means that we are consequently unable to draw conclusions about the coherent magnetic fields in the DLA in J1243+4043 itself. The only difference between the datasets are the distributions of the high-versus low-redshift RMs, which display no subsequent difference in the GRMs.
ACKNOWLEDGMENTS We acknowledge useful discussions with  Gyula Jozsa  and  Paolo Serra. NG  acknowledges support from  DST   Startup Research Grant :  YSS/2014/000338. NG, PN, PP and RS acknowledge support from the  Indo-French Centre for the Promotion of Advanced Research (Centre Franco-Indien pour la promotion de la recherche avancée)  under Project  5504-B. We acknowledge the use of ARTIP (https://github. com/RTIP/artip). ARTIP is developed by software engineers, in particular,  Dolly Gyanchandani ,  Sarang Kulkarni  and  Ravi Sharma , of  ThoughtWorks India Pvt. Limited and researchers of IUCAA. We thank  GMRT, IGO, VLBI (EVN + VLBA)  and  WSRT  staff for their support during the observations. GMRT is run by the  National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research.
EVN is a joint facility of European, Chinese, South African and other radio astronomy institutes funded by their national research councils. VLBA is run by  National Radio Astronomy Observatory. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. The research leading to these results has received funding from the  European Commission 7th Framework Programme  (FP/2007-2013) under grant agreement No. 283393  ( RadioNet3 ). Scientific results from the VLBI data presented in this publication are derived from the following EVN project code:  GG074. WSRT is operated by the  ASTRON (Netherlands Institute for Radio Astronomy ) with support from the  Netherlands Foundation for Scientific Research (NWO).
Thermodynamic model As mentioned, the model assumes thermodynamic equilibrium at the interface. The IPA water CO 2 ternary phase equilibrium was com puted using the Simulis ® Thermodynamics software (ProSim SA, France). A combined equation of state/activity coefficient model (EoS/ G E ) approach with suitable complex mixing rules was selected. The equation of state is the Soave Redlich Kwong equation  [26]  with the modification proposed by Boston Mathias  [27]. The co volume b of the SRK equation is obtained from a standard mixing rule (arithmetic mean of the corresponding parameters of the pure compounds). The term a of the SRK equation is computed using the PSRK mixing rule  [28]  and the UNIQUAC model  [29, 30]  is used to calculate the excess energy at zero pressure, G E 0 , involved in the PSRK mixing rule.
Regarding the fluid phase, to the best of our knowledge there are no density data for the systems CO 2 + water or CO 2 + isopropanol at su percritical conditions in the open literature. However, as a reference, data of saturated density of the system CO 2 + ethanol at near critical and supercritical conditions (40 and 55 °C, 7 9 MPa) indicate that the difference with pure CO 2 density is below 12%  [32]. Calculations performed with the Simulis © software using our thermodynamic model at the inlet: flow rate and composition are constant at the outlet: zero gradient for all quantities. A second order upwind scheme is used for internal nodes (k = 3, …,N -1) as well as for the definition of the Neumann boundary Note that the Reynolds number for the liquid phase has to be cal culated in terms of effective interfacial area (a eff ), as in Eq.