dataesr/scientific-paragraphs-categorization huggingface dataset:
e. a set of structures defining the conformational ensemble of DNA (and associated proteins), which is typically obtained through atomistic or coarse-grained molecular dynamics (MD) simulations  (11) (12) (13) (14) (15) (16) (17). Specific databases on DNA simulation data  (18)  or protein-DNA complexes  (19, 20) , and tools to perform flexibility analysis of nucleic acids like NAFlex  (21)  or 3DNA  (22)  are available. Few tools allow mapping 3D structures onto the genomic sequences  (23) (24) (25). CC-BY-ND 4. 0 International license under a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available The copyright holder for this preprint (which was not this version posted April 9, 2019. ; https://doi. org/10.
1101/602474 doi: bioRxiv preprint A third level of data is represented by nucleosome and protein positioning studies  (26) (27) (28) (29) (30). Raw data is obtained from sequencing analysis (MNAse-seq, Chip-seq) and is usually limited to the annotation of specific binding sites. Data at this level is available from public repositories like ArrayExpress  (31) , where it is presented in a 1D manner without any connection to the 3D structure and flexibility of the chromatin fiber. At the upper end of the scale the main experimental strategies include FISH  (32) , and chromosome conformation capture (3C-like techniques  (33) ), which provide chimeric sequences representing interactions within the genome, from which structural insight can be derived after a complex manipulation of the data. Data visualization at this level is complex  (34) ; several tools are already available  (35) (36) (37) (38) , but they ignore any atomistic detail, or even the general description of the nucleosome string.
In summary, 3D (structure) and 4D (dynamics) genomics cover a large variety of data types and experimental and theoretical strategies. Tools and data repositories exist; but they are not integrated. As a result, research at the different levels is performed in isolation, and researchers almost ignore alternative views coming from different levels. The Multiscale Complex Genomics project (MuG, http://www. multiscalegenomics. eu/MuG) is one of the initiatives aiming to bridge the gap among the different levels of chromatin study and provide such an integrated view. Here, we present the MuG Virtual Research Environment (MuGVRE) infrastructure, designed to provide researchers with a single access point where data and tools covering the full spectrum, from sequence and atomistic data to chromosome capture results, can be used and combined to obtain a holistic picture of chromatin.
MuGVRE is cloud based, allowing for an easy deployment and extension at the technical level. It is a base infrastructure where additional tools can be plugged-in to extend the functionality. The initial offer of tools already hosted, includes sequence, structure and Hi-C data analysis and visualizers; tools to handle nucleosome positioning, and tools for performing and analyzing simulation data from atomistic to coarse-grained levels. All the tools are accessible through an intuitive personal workbench, where most technical decisions are taken automatically by the system, allowing the user to concentrate on the scientific aspects of the analysis. We believe that MugVRE is the missing element to integrate the different views of physiological DNA.
INFRASTRUCTURE DESIGN AND COMPONENTS The MuGVRE infrastructure has been designed to fulfil the following principles: Technical: 1. Flexible structure , able to adapt to the specific needs of the analysis tools, both in terms of software requirements, and computational resources. CC-BY-ND 4. 0 International license under a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available The copyright holder for this preprint (which was not this version posted April 9, 2019. ; https://doi. org/10. 1101/602474 doi: bioRxiv preprint 2. Software scheduler(s) , able to manage analysis workflows, and computational resources in a transparent and adaptable manner. This should constitute an elastic infrastructure with automatic adaptation to user loads.
MuGVRE Main components Cloud deployments: MuGVRE infrastructure has been designed as a fully virtualized environment. At its present state, MuGVRE has been deployed in at the Starlife cloud infrastructure (https://www. bsc. es/marenostrum/star-life), at the Barcelona Supercomputing Center, using OpenNebula (https://opennebula. org/) and the KVM hypervisor (https://www. linux-kvm. org). Process management: MuGVRE uses two complementary layouts for process management: i) Protein Data Bank  (10) , Uniprot  (41) , and BiGNASim  (18)  database. User's data is stored in a standard filesystem in its original format. Authentication and authorization system: Data privacy is maintained using the authentication and authorization server Keycloak ( http://www. keycloak. org/ ) to handle all internal communications and user access.
Personal workspace The MuGVRE personal workspace is the central environment for user activity. It is based on a filesystem-based layout (see Figure  2 ), where both uploaded data and analysis results are available. Uploaded data should be annotated to specify data types and formats. This allows the MuGVRE workspace to offer an adapted toolkit for each file, including only compatible tools and visualizers (see Suppl. Material Section 2 for a description of the procedure, and Suppl. Table  S3  for a description of accepted data types and formats). The user workspace has been laid out to provide an intuitive look-and-feel. The workspace itself is structured in projects, to keep data logically organized. Within each project the input data is distributed in folders: Uploads (uploaded data), and Repository (data obtained from public repositories).
Applications and developer access MuGVRE is designed as an infrastructure open to any application designed for the analysis of 3D/4D genomics data. Tools installed in MuGVRE should accept free, unrestricted usage. Guidelines are available for developers (see Suppl. Material, Section 2). Developers wishing to include their applications are granted access to a specific workspace to manage tool definitions and execution details, and to edit the tool's help pages, and perform execution tests. Also developers get access to usage statistics, execution logs, and job associated files and metadata. The current offer of tools is detailed in Suppl. Table  S4. Several data visualizers are available (Suppl, Table  S5 ). Figure  2  shows a representative set of the types of data than can be visualized in the infrastructure.
USAGE Users can populate the MuGVRE workspace in several ways: • Direct upload into the workspace , using the HTTPS protocol • Create files using an embedded text editor. This is intended for data or metadata of reduced size. • Upload from an External URL : MuGVRE can access external sites to download. This is the recommended procedure to obtain data from public repositories, or import bulky data, as the upload process becomes a batch job. HTTP and FTP protocols are accepted, also when user credentials are required. • From repository: Data imported from the list of public repositories made browsable at the infrastructure (currently ArrayExpress  (31)  and BigNASim  (18) ). • From sample data: selected input and output examples for the available tools are provided as help to start using the interface.
DISCUSSION 3D/4D Genomics is an emerging field originated from the unplanned aggregation of different disciplines which have developed their tools, and associated data types and formats, independently. This diversity is a major obstacle towards the generation of a complete picture of chromatin structure and dynamics. MuGVRE has been designed as an integration space. It follows the traditional concept of the personal workbench, already used in general genomic workbenches like Galaxy  (9)  or GenePattern  (44) , or spaces designed for simulation data analysis like NAFlex  (21). In this kind of environment, data and tools are available and the user has the freedom to design his/her own analysis pipelines. MuGVRE has an initial offer of tools and visualizers covering all levels of resolution, from atomistic simulation to chromatin fiber simulation, or Hi-C data analysis.
Tools and visualizers are offered in a single space where, for example, chromosome conformation capture or nucleosome positioning data can be visualized along with sequence annotations and the structures and binding modes of the transcription factors affecting the same DNA region. A strong commitment of MuGVRE design is to free the user from understanding the technical side of the infrastructure. With this aim, not only the computational layout is hidden, but also most technical decisions are taken automatically by the system. For example, we have designed a comprehensive ontology of data types and formats that are checked internally to configure the options offered to the user. The user can just select a tool, and the workspace selects those files that match its input requirements. Output from the.
org), EGI (https://www. egi. eu), or EUDAT (https://eudat. eu). The choice of a fully flexible cloud system, controlled with a multiscale software scheduler, and linked to HPC facilities, assures the usage of the optimal computational environment for each specific analysis. MuGVRE is presented as an open platform with the aim of growing in functionality, since new tools can be easily incorporated by external developers. Hosting at MuGVRE can be an option for developers alternative to build a dedicated web site to run their tools. MuGVRE was presented to the multiscale genomics community in November 2017, and has performed already over 6,500 analysis runs. MuGVRE is a unique tool that aims to help researchers in the 3D/4D genomics field to gain an integrated view of discipline, sharing data among the diverse analysis levels and providing a complete and integrated view on DNA.
review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available certified by peer The copyright holder for this preprint (which was not this version posted April 9, 2019. ; https://doi. org/10. 1101/602474 doi: bioRxiv preprint. CC-BY-ND 4. 0 International license under a COMPS Superscalar (COMPSs) programming model (and its python binding pyCOMPSs (39)), managed by the Programming Model Enactment Service (PMES) (40), which interacts with cloud infrastructures through Open Cloud Computing Interface (OCCI, http://occi-wg. org/ ) servers. PMES/pyCOMPSs are used to control complex workflows and distributed execution. Database manager: Operational data and metadata regarding installed tools, public repositories, and user's files are maintained in a MongoDB database Sun Grid Engine (SGE, https://sourceforge.
Aims. In order to understand the observed molecular diversity in high-mass star-forming regions, we have to determine the underlying physical and chemical structure of those regions at high angular resolution and over a range of evolutionary stages. Methods. We present a detailed observational and modeling study of the hot core VLA 3 in the high-mass star-forming region AFGL 2591, which is a target region of the NOrthern Extended Millimeter Array (NOEMA) large program CORE. Using NOEMA observations at 1. 37 mm with an angular resolution of ∼0. 42 (1 400 au at 3. 33 kpc), we derived the physical and chemical structure of the source. We modeled the observed molecular abundances with the chemical evolution code MUSCLE (MUlti Stage ChemicaL codE). Results. With the kinetic temperature tracers CH 3 CN and H 2 CO we observe a temperature distribution with a power-law index of q = 0.
The chemical composition of these objects can be modeled using a chemical kinetics approach  (Semenov et al. 2010; Cuppen et al. 2017 ) and an appropriate network of astrophysically relevant chemical reactions, for example, the KInetic Database for Astrochemistry (KIDA, Wakelam et  al. 2015)  and the UMIST Database for Astrochemistry  (UDfA, McElroy et al. 2013). The reaction data are obtained by quantum chemical calculations and ultra-high vacuum laboratory experiments (for a review, see  Cuppen et al. 2017 ). In the early phases of star formation at low temperatures of ∼10 K, atoms and small molecules (e. g. , CO) produced in the gas-phase stick to the grain surface creating icy mantles  (Tielens & Hagen 1982; Hasegawa et al. 1992). Hydrogenation of CO leads to species such as H 2 CO and CH 3 OH on the grains.
Most chemical studies at high angular resolution focus on individual cores (e. g. ,  Beuther et al. 2007c; Fuente et al. 2014; Beltrán et al. 2018) , whereas the influence of the environment on the chemical evolution may play an important role. For example,  Feng et al. (2016) ,  Allen et al. (2017) ,  and Mills et al. (2018)  observed and investigated chemical differentiation between adjacent hot cores. In the framework of a sample of HMSFRs observed with the same setup and high spatial resolution, we carried out the NOrthern Extended Millimeter Array (NOEMA) 1 large program CORE at 1. 37 mm  (Beuther et al. 2018). In total, 18 HMS-FRs were observed with an angular resolution of ∼0. 4. The broad frequency bandwidth of ∼4 GHz covers a large number of molecular lines and thus we are able to study the chemical properties of the objects in this sample.
The CORE observations are described in Sect. 2 with a summary of the data calibration and properties of the final data products. The observational results are reported in Sect. 3, where the physical and chemical structure are analyzed. The applied physical-chemical model of the source is presented in Sect. 4. The observational and chemical modeling results are discussed in Sect. 5. In addition, the results are discussed in the context of previous studies of the target region. The main conclusions and an outlook are given in Sect. 6.
Observations This study is part of the NOEMA large program CORE  (Beuther et al. 2018). The main goals of the project are to study of the fragmentation and disk formation processes, as well as the outflows and chemistry in high-mass star formation. A detailed description of the full survey, technical setup, scientific goals, and analysis of the continuum data is given in  Beuther et al. (2018). The observations of the sources in the 1 mm band were carried out with NOEMA from 2014 -2017 in the A, B, and D configurations. Using the wide-band correlator WideX, spectra from 217 GHz to 221 GHz with a spectral resolution of 1. 95 MHz (∼2. 7 km s -1 at 1 mm) were obtained in H and V linear polarizations. In order to study the kinematics, high spectral resolution units were placed around the wavelengths of key molecular transitions (see  Ahmadi et al.
3) for bandpass, and the star MWC349 for absolute flux calibration. The data were calibrated with the CLIC package in gildas 3. The 1. 37 mm continuum was extracted from line-free channels in the spectral line data and then subtracted from the spectral line data. The NOEMA data (line and continuum) were deconvolved with the Clark CLEAN algorithm  (Clark 1980)  as the extended emission is filtered out. The NOEMA data merged with the IRAM 30 m observations (hereafter merged data) were deconvolved with the SDI algorithm  (Steer et al. 1984) , but see  Mottram et al. , subm. , for a comparison of these two deconvolution methods. The NOEMA and merged data were smoothed to a common spectral resolution of 3 km s -1. The properties of the data products are shown in Table  1.
Line identification An average NOEMA spectrum (spatially averaged over 1. 6×1. 6, indicated by the white rectangle in Fig. 2 ) around the continuum peak is used to identify emission lines detected at S /N > 5 (σ = 0. 34 mJy beam -1 in the average spectrum). The molecular transitions are taken from the Cologne Database for Molecular Spectroscopy 4  (CDMS,  Müller et al. 2005)  and Jet Propulsion Laboratory database 5  (JPL,  Pickett et al. 1998 ). The spectrum with labeled molecular lines is shown in Fig. 3. The properties of a total of 67 detected lines are summarized in Table A. 1 in the Appendix. We detect many transitions from simple molecules (e. g. , SO, HNCO, OCS), but also a forest of COM lines, among which CH 3 OH and CH 3 CN have the strongest emission lines. For some molecules (e.
Temperature structure The molecules H 2 CO and CH 3 CN can probe the kinetic temperature when multiple transitions at different energy levels are observed (e. g. ,  Zhang et al. 1998; Rodón et al. 2012). The CH 3 CN emission is strong around hot cores and can trace dense hightemperature gas  (Fuente et al. 2014) , whereas H 2 CO is used to trace the temperature of the colder extended envelope  (Mangum & Wootten 1993). The upper state energies E u /k B of the observed transitions of H 2 CO and CH 3 CN are listed in Table  A. 1. To derive the physical parameters of the molecular gas such as column density N and rotation temperature T rot , we fit the spectral lines with XCLASS 6  (eXtended Casa Line Analysis Software Suite,  Möller et al. 2017). The XCLASS software models molecular lines by solving the 1D radiative transfer equation assuming local thermal equilibrium (LTE) conditions and an isothermal source.
In dense sources, such as hot cores, the LTE assumption is a reasonable approximation for rotational lines; in Sect. 3. 3. 3 we estimate an average H 2 density of ∼10 7 cm -3 in the central part within a radius of ∼2 500 au. The critical density is < 10 7 cm -3 for most of the observed molecules (CH 3 OH being the only exception; see Table  A. 1). Therefore, the population of the molecules can be described by a Boltzmann distribution with a single characteristic temperature T ex = T rot = T kin. In XCLASS, the line profiles are assumed to be Gaussian, but optical depth effects are included. Each molecule can be described by multiple emission and absorption components. The transition properties (e. g. , Einstein coefficients, state degeneracies, partition functions) are taken from an embedded SQlite3 database containing entries from CDMS and JPL using the Virtual Atomic and Molecular Data Centre (VAMDC,  Endres et al.
Chemical model The observational analysis of the AFGL 2591 CORE data reveal a large degree of chemical complexity both in abundances and spatial distribution. In this section, we use the physical-chemical modeling code MUSCLE (MUlti Stage ChemicaL codE, a detailed description of the model is given in  Gerner et al. 2014)  to study the formation and destruction of the observed molecular abundances. MUSCLE computes a static radial physical structure coupled with the time-dependent chemical kinetics code ALCHEMIC  (Semenov et al. 2010; Semenov 2017 ) including both gas-phase and grain-surface chemistry with reaction rates obtained from KIDA  (Wakelam et al. 2012 (Wakelam et al. , 2015)). The computed abundance profiles are beam-convolved and thus converted into model column densities as a function of time.
(2015). For 19 well-studied IRDCs, column densities of 18 molecules including deuterated species were obtained using IRAM 30 m observations at 1 and 3 mm. The column densities of the molecules were averaged and modeled to create abundances of a template IRDC. These authors derive an IRDC chemical age of τ IRDC = 16 500 years using MUSCLE (Table  A. 4 and A. 8 in Gerner et al. 2015). Finally, the physical structure of the AFGL 2591 hot core has to be defined. Our NOEMA data are missing the short-spacings in the continuum yielding a missing flux of 84 %, so we can only determine a lower limit for the outer radius of 7 000 au (Fig. 2 ). From the observed radial temperature profile shown in Fig. 4 (b), we can adopt an upper limit for the inner radius of 700 au which is half a beam size. The density index p is varied from 1.
Conclusions In this study, we analyzed continuum and spectral line data of AFGL 2591 VLA 3 at 1. 37 mm obtained within the NOEMA large program CORE. We derived the radial temperature and density profiles, studied the outflow and determined the molecular abundances towards the position of the 1. 37 mm continuum peak. We modeled the chemical abundances using the physicalchemical code MUSCLE and found a rough agreement with previous studies for the chemical age of the hot core. Our main conclusions of this study are the following: -The 1. 37 mm spectra reveal a large chemical complexity in O-, N-, and S-bearing species. In contrast to previous line surveys of the hot core at lower angular resolution, the spectra of the CORE data show that AFGL 2591 VLA 3 indeed has a typical hot core spectrum with many molecular emission lines.
Assuming spherical symmetry and optically thin emission, we obtain a value of p = 1. 7 ± 0. 1. We derived the H 2 column density from the 1. 37 mm continuum emission and estimated the gas mass reservoir within the inner 10 000 au around the continuum peak to 6. 9 ± 0. 5 M. This is a lower limit, as the NOEMA data miss a large amount of flux due to spatial filtering. -The emission of CO, SiO, and SO using single-dish and interferometric observations were used to study the outflow structure. Two potential blueshifted outflow directions can be identified, one towards the west and one towards the northeast. This suggests that there could be several fragmented cores on spatial scales < 1 400 au or that we might be observing a disk wind. -Using XCLASS the column density N and rotation temperature T rot of all detected molecules were determined.
The purpose of this work was to use the hot core AFGL 2591 VLA 3 as a case study in order to investigate the chemical abundance within the CORE spectral setup at 1. 37 mm. The high spatial resolution and sensitivity obtained by NOEMA revealed a rich chemical complexity both in abundance and spatial distribution. The physical-chemical code MUSCLE is sufficient to model a large fraction of the observed molecular abundances obtained by interferometric observations at high angular resolution assuming spherical symmetry.
Acknowledgements. The authors would like to thank the anonymous referee whose comments helped improve the clarity of this paper. This work is based on observations carried out under project number  L14AB  with the  IRAM NOEMA Interferometer  and the IRAM 30 m telescope. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany) , and  IGN (Spain). C. G. , H. B. , A. A. , and J. C. M. acknowledge support from the  European Research Council  under the  Horizon 2020 Framework Programme  via the  ERC Consolidator Grant   CSF-648505. D. S. acknowledges support from the  Heidelberg Institute of Theoretical Studies  for the project " Chemical kinetics models and visualization tools: Bridging biology and astronomy. "R. K. acknowledges financial support via the  Emmy Noether Research Group on Accretion Flows and Feedback in Realistic Models of Massive Star Formation  funded by the  German Research Foundations (DFG)  under grant no.
KU 2849/3-1  and  KU 2849/3-2. A. P. acknowledges financial support from  UNAM-PAPIIT IN113119  grant,  México. A. S. M. acknowledges funding by the  Deutsche Forschungsgemeinschaft (DFG)  via the  Sonderforschungsbereich SFB 956 Conditions and Impact of Star Formation  (subproject  A6 ). I. J. -S. acknowledges partial support by the  MINECO  and  FEDER  funding under grants  ESP2015-65597-C4-1  and  ESP2017-86582-C4-1-R. This research made use of Astropy 10 , a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013, 2018).