dataesr/scientific-paragraphs-categorization huggingface dataset:
884631 ); the  Institute for Advanced Study ; the  Istituto Nazionale di Fisica Nucleare (INFN) sezione di Napoli , iniziative specifiche TEONGRAV; the  International Max Planck Research School for Astronomy and Astrophysics at the Universities of Bonn and Cologne ;  DFG research grant  " Jet physics on horizon scales and beyond " (Grant No. FR 4069/2-1 ); Joint Princeton/ Flatiron and Joint Columbia/Flatiron Postdoctoral Fellowships, research at the  Flatiron Institute  is supported by the  Simons Foundation ; the  Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT ; grant  JPMXP1020200109 ); the  Japanese Government (Monbukagakusho: MEXT) Scholarship; the  Japan Society for the Promotion of Science (JSPS)   Grant-in-Aid for JSPS Research Fellowship  ( JP17J08829 ); the  Joint Institute for Computational Fundamental Science, Japan ; the  Key Research Program of Frontier Sciences, Chinese Academy of Sciences  (CAS, grants  QYZDJ-SSW-SLH057 ,  QYZDJSSW-SYS008 ,  ZDBS-LY-SLH011 ); the  Leverhulme Trust Early Career Research Fellowship ; the  Max-Planck-Gesellschaft (MPG) ; the  Max Planck Partner Group of the MPG  and the CAS; the  MEXT/JSPS KAKENHI  (grants  18KK0090 ,  JP21H01137 ,  JP18H03721 ,  JP18K13594 ,  18K03709 ,  JP19K14761 ,  18H01245 , 25120007); the  Malaysian Fundamental Research Grant Scheme  ( FRGS) FRGS/1/ 2019/STG02/UM/02/6 ; the  MIT International Science and Technology Initiatives (MISTI) Funds ; the  Ministry of Science and Technology (MOST) of Taiwan  ( 103-2119-M-001-010-MY2 ,  105-2112-M-001-025-MY3 ,  105-2119-M-001-042 ,  106-2112-M-001-011 ,  106-2119-M-001-013 ,  106-2119-M-001-027 ,  106-2923-M-001-005 ,  107-2119-M-001-017 ,  107-2119-M-001-020 ,  107-2119-M-001-041 ,  107-2119-M-110-005 ,  107-2923-M-001-009 ,  108-2112-M-001-048 ,  108-2112-M-001-051 ,  108-2923-M-001-002 ,  109-2112-M-001-025 ,  109-2124-M-001-005 ,  109-2923-M-001-001 ,  110-2112-M-003-007-MY2 ,  110-2112-M-001-033 ,  110-2124-M-001-007 , and  110-2923-M-001-001 ); the  Ministry of Education (MoE) of Taiwan Yushan Young Scholar Program ; the  Physics Division, National Center for Theoretical Sciences of Taiwan ; the  National Aeronautics and Space Administration  ( NASA, Fermi Guest Investigator  grant  80NSSC20K1567 ,  NASA   Astrophysics Theory Program  grant  80NSSC20K0527 ,  NASA   NuSTAR award   80NSSC20K0645 );  NASA   Hubble Fellowship  grant  HST-HF2-51431.
001-A  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , for  NASA , under contract  NAS5-26555 ; the  National Institute of Natural Sciences (NINS) of Japan ; the  National Key Research and Development Program of China  (grant  2016YFA0400704 ,  2017YFA0402703 ,  2016YFA0400702 ); the National Science (NSERC, including a  Discovery Grant  and the  NSERC   Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program ); the  National Youth Thousand Talents Program of China ; the  National Research Foundation of Korea  (the Global PhD Fellowship Grant: grants  NRF-2015H1A2A1033752 , the  Korea Research Fellowship Program :  NRF-2015H1D3A1066561 ,  Brain Pool Program :  2019H1D3A1A01102564 ,  Basic Research Support  Grant  2019R1F1A1059721 ,  2021R1A6A3A01086420 ,  2022R1C1C1005255 );  Netherlands Research School for Astronomy (NOVA) Virtual Institute of Accretion (VIA ) postdoctoral fellowships;  Onsala Space Observatory (OSO)  national infrastructure, for the provisioning of its facilities/observational support (OSO receives funding through the  Swedish Research Council  under grant  2017-00648 ); the  Perimeter Institute for Theoretical Physics  (research at  Perimeter Institute  is supported by the  Government of Canada  through the  Department of Innovation, Science and Economic Development  and by the  Province of Ontario  through the  Ministry of Research, Innovation and Science) ; the  Spanish Ministerio de Ciencia e Innovación  (grants  PGC2018-098915-B-C21 ,  AYA2016-80889-P ,  PID2019-108995GB-C21 ,  PID2020-117404GB-C21 ); the  University of Pretoria  for financial aid in the provision of the new Cluster Server nodes and  SuperMicro (USA)  for a SEEDING GRANT approved towards these nodes in 2020; the  Shanghai Pilot Program for Basic Research, Chinese Academy of Science ,  Shanghai Branch  ( JCYJ-SHFY-2021-013 ); the  State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa " award for the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ); the  Spinoza Prize SPI   78-409 ; the South African Research Chairs Initiative, through the  South African Radio Astronomy Observatory  (SARAO, grant  ID 77948 ), which is a facility of the  National Research Foundation (NRF) , an agency of the  Department of Science and Innovation (DSI) of South Africa ; the  Toray Science Foundation; Swedish Research Council  (VR); the  US Department of Energy (USDOE)  through the  Los Alamos National Laboratory  (operated by  Triad National Security, LLC , for the  National Nuclear Security Administration of the USDOE  (Contract  89233218CNA000001 ); and the  YCAA Prize Postdoctoral Fellowship.
The NRAO is a facility of the  NSF  operated under cooperative agreement by  AUI. This research used resources of the  Oak Ridge Leadership Computing Facility  at the  Oak Ridge National Laboratory , which is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC05-00OR22725. We also thank the  Center for Computational Astrophysics, National Astronomical Observatory of Japan. The computing cluster of Shanghai VLBI correlator supported by the  Special Fund for Astronomy  from the  Ministry of Finance in China  is acknowledged. APEX is a collaboration between the  Max-Planck-Institut für Radioastronomie (Germany) ,  ESO , and the  Onsala Space Observatory (Sweden). The SMA is a joint project between the SAO and ASIAA and is funded by the  Smithsonian Institution  and the  Academia Sinica.
The SMT is operated by the  Arizona Radio Observatory , a part of the  Steward Observatory of the University of Arizona , with financial support of operations from the  State of Arizona  and financial support for instrumentation development from the  NSF. Support for SPT participation in the EHT is provided by the  National Science Foundation  through award  OPP-1852617  to the  University of Chicago. Partial support is also provided by the  Kavli Institute of Cosmological Physics at the University of Chicago. The SPT hydrogen maser was provided on loan from the GLT, courtesy of ASIAA. This work used the  Extreme Science and Engineering Discovery Environment (XSEDE) , supported by  NSF  grant  ACI-1548562 , and CyVerse, supported by  NSF  grants  DBI-0735191 ,  DBI-1265383 , and  DBI-1743442.
Interestingly, there are additional mounting lines of evidence suggesting that ELANe are located in overdense environments. Indeed, they are (i) all associated with multiple AGN, with up to four known quasars within the same structure  (Hennawi et al. 2015) , (ii) frequently associated with exceptional overdensities of Lyα emitters on small  (Arrigoni Battaia et al. 2018a ) and on large scales  (Hennawi et al. 2015; Cai et al. 2017), and (iii)  probably in fields characterized by high number counts of submillimeter sources  (Arrigoni Battaia et al. 2018b). Despite these findings, a systematic study of the environment and nature of ELANe has not been conducted yet. For this reason, we initiated the project "a multiwavelength study of ELAN environments" (AMUSE 2 ) collecting datasets from the rest-frame ultraviolet out to the submillimeter regime with the specific aim of studying their astrophysics, while firmly locating these large-scale structures in the wide framework of galaxy formation and evolution.
Specifically, our observations target the H-band, 870 µm (single-dish), 850 µm (single-dish), 450 µm (single-dish), 850 µm (interferometer), 2 mm and the CO(5-4) rotational transition of the carbon monoxide, respectively. The paper is structured as follows. In section 2 we report on our observations and data reduction for each individual instrument/dataset. Section 3 presents the observational results, quantifying the significance of the detections. The observational results allowed us to examine several aspects of the nature and astrophysics of this ELAN. In section 4, we first estimate the star formation, dust, stellar and molecular gas masses, and infer the dark matter halo mass with two orthogonal methods. In this way, we obtain a first-order mass budget of the whole system (section 4.
JCMT/SCUBA-2 The SCUBA-2 observations for this ELAN field were conducted at JCMT during flexible observing in 2018 February 12, and March 29 (program ID: M18AP054; PI: M. Fumagalli) under good weather conditions (band 1 and 2, τ 225GHz ≤ 0. 07). The SCUBA-2 instrument observes simultaneously the same field at 850 and 450 µm, with an effective beam FWHM of 14. 6 and 9. 8 , respectively  (Dempsey et al. 2013). The observations were performed with a Daisy pattern covering 13. 7 in diameter, and were centered at the location of QSO (and thus the ELAN). To facilitate the scheduling we divided the observations in 5 scans/cycles of about 30 minutes, for a total of 2. 5 hours. For the data reduction we closely followed the procedures in  Chen et al. (2013a)  and Arrigoni  Battaia et al. (2018b). In brief, we reduced the data using the Dynamic Iterative Map Maker (DIMM) included in the Sub-Millimetre User Reduction Facility (SMURF) package from the STARLINK software  (Jenness et al.
SMA We performed the SMA  (Ho et al. 2004 ) observations of this ELAN (Project code: 2017A-S015, PI: F. Arrigoni Battaia) on June 21 (UTC 3:00-8:30), June 27 (UTC 3:00-7:30), and July 10 (UTC 3:30-6:30) of 2017, in the compact array configuration. However, for the observations on June 27, we only utilized the data taken after UTC 6:30 because we noticed a large antenna pointing error before then. The atmospheric opacity at 225 GHz (τ 225 GHz ) were 0. 1-0. 15, ∼0. 1, and ∼0. 05 during these three tracks of observations. The observations were carried out in dual receiver mode by tuning the 345 GHz and 400 GHz receivers to the same observing frequencies. These two receivers took left and right circular polarization, respectively, and covered the observing frequency 329-337 GHz in the lower sideband and 345-353 GHz in the upper sideband.
Correlations were performed by the SMA Wideband Astronomical ROACH2 Machine (SWARM) which sampled individual sidebands with 16384×4 spectral channels. The integration time was 30 seconds. Prior to data calibration, we binned every 16 spectral channels to reduce filesizes. The observations on our target source cover the uv distance range of ∼8. 5-88. 5 kλ. The target sources were observed in scans of 12 minute duration, which were bracketed by scans on the gain calibration quasar source 1058+015 with 3 minutes duration. We observed Titan in the first two tracks, and observed Callisto in the last track for absolute flux calibrations. We follow the standard data calibration strategy of SMA. The application of Tsys information and the absolute flux, passband, and gain calibrations were carried out using the MIR IDL software package  (Qi 2003).
The absolute flux scalings were derived by comparing the visibility amplitudes of the gain calibrators with those of the absolute flux calibrators (i. e. , Titan and Callisto). The derived and applied fluxes of 1058+015 were 2. 5 Jy in the first two tracks, and 2. 7 Jy in the last track. We nominally quote the ∼15% typical absolute flux calibration error of SMA. After calibration, the zeroth-order fitting of continuum levels and the joint weighted imaging of all continuum data were performed using the Miriad software package  (Sault et al. 1995). We performed zeroth-order multi-frequency imaging combining the upper-and lower-sideband data, to produce a sensitive continuum image at the central observing frequency (i. e. , the local oscillator frequency). Due to the different performance of the 345 GHz and 400 GHz receivers at the same observing frequency, it would be incorrect to treat half of the difference of the parallel hand correlations (i.
We calibrated the data using the CASA software package  (McMullin et al. 2007)  version 5. 1. The derived fluxes of the gain calibrator J1025+1253 were in the range 0. 42 -0. 48 Jy. We fit the continuum baselines using the CASA task uvcontsub. We jointly imaged all continuum data using the CASA task clean, which produced the Stokes I image by averaging the parallel linear correlation data (i. e. , I = (XX + YY)/2). Our target sources are presumably weakly or not polarized. Therefore, we regarded the (XX-YY)/2 image as an approximated thermal noise map. The Briggs Robust = 2 weighted image achieved a synthesized beam of θ maj × θ min = 0. 95×0. 94 (P. A. =-5. 3 • ), and a rms noise of 4. 7 µJy beam -1. For the spectral windows including the CO(5-4) emission, we generate the continuum from the channels not affected by line emission as identified from the datacubes, and subtract it from the data.
SMA continuum at 850 µm We extracted continuum sources from the SMA continuum map (top right panel in Figure  1 ) using the same algorithm described in e. g. , Arrigoni  Battaia et al. (2018b) , but working with the SMA beam of the current dataset. Briefly, the algorithm iteratively searches for maxima in the S/N map (Figure  2 ) while subtracting (at their locations) mock sources normalized to those peaks. The iterations are stopped once S/N= 2 is reached. The S/N peaks found by the algorithm are included in a source candidate catalog. In the current case, the algorithm found 7 sources. Subsequently, the same algorithm is applied to the negative dataset down to the same S/N threshold to estimate the number of spurious sources and clean the aforementioned catalog. We find no spurious sources within a radius of R = 7 from the center of the map, one such source in the annuli within 7 < R < 10.
ALMA CO(5-4) line detections We rely on the publicly available code LineSeeker  (González-López et al. 2017)  to robustly identify sources with CO(5-4) line emission in the ALMA observations. In- deed LineSeeker has been developed to systematically search for line emissions in ALMA data and quantify their significance (e. g. ,  González-López et al. 2019). The code looks for signal in the ALMA datacubes on a channel by channel basis after convolving the data along the spectral axis with Gaussian kernels of different spectral widths. A list of line candidates is obtained by joining detected signal on different channels using the DBSCAN algorithm  (Ester et al. 1996). LineSeeker finally provides a S/N for each emission line candidate. This S/N is defined as the maximum value obtained from the different convolutions.
(2018). The inferred DM halo mass is found in agreement with the estimate from an orthogonal method using the projected distances and redshift differences of the sources  (Tempel et al. 2014 ). We then discuss in section 4. 4. 2 the mass budget in the baryonic components, under several assumptions and also taking into account different baryon fractions. We conclude the section by forecasting the system evolution (section 4. 5). In each section we discuss the limitations and assumptions of each method, and also indicate some of the needed datasets to refine our estimates.
2019 ; see section 4. 1 for details). We show the best-fit model (black dashed line), and its individual components: stellar emission (blue), dust emission (brown), AGN emission (yellow), synchrotron radiation (green). We report the χ 2 of the best-fit model in the bottom right corner. Table  3  lists some of the properties obtained from these fits. For each source we also indicate a modified black-body with T=40 K for the FIR emission (dot-dashed gray line; see section 4. 1). We rely on the SED fitting code CIGALE (v2018. 0, Boquien et al. 2019), which covers the full range of the current datasets, from rest-frame ultraviolet (UV) to radio emission. CIGALE fits simultaneously all this wavelength range imposing energy balance between the UV and the infrared (IR) emission (reprocessed dust emission), while decomposing the SED into different physically motivated components.
2010). With these estimates, the dust-to-stellar mass ratios are M dust /M star = 0. 006 ± 0. 004, 0. 010 ± 0. 004, 0. 05 ± 0. 02, 0. 4 ± 0. 1, for QSO, QSO2, AGN1, and LAE1, respectively. These values are in agreement within uncertainties with observations of main-sequence and starburst galaxies at these redshifts (in the range 0. 001 -0. 1, e. g. , da  Cunha et al. 2015; Donevski et al. 2020) , except LAE1 which has a larger value. Given the similarity with the SED of AGN1, this tension could be solved by including an AGN component in the SED fit for LAE1. The current dataset does not show a clear evidence for AGN activity in LAE1, but it could be obscured. X-rays observations are therefore needed to verify the nature of this source. Further, CIGALE determined the instantaneous SFR for each source, indicating SFR = 521 ± 74, 183 ± 49, 104 ± 21, 50 ± 11 M yr -1 for QSO, QSO2, AGN1, LAE1, respectively.
Our multiwavelength observations added several continuum data-points to the four brightest sources SED (QSO, QSO2, AGN1, LAE1), and unveiled their relatively boxy CO(5-4) emission with integrated flux in the range 0. 22 I CO(5-4) 0. 43 Jy km s -1. This emission is spatially resolved and shows evidence of kinematics reminiscent of rotation-like patterns in three sources, QSO2, AGN1, and LAE1. Further, the Lyα emission in the vicinity of AGN1 and LAE1 is found to peak at a displaced position with respect to the continuua and the CO(5-4) emission, by ∼ 11. 4 kpc and ∼ 6. 8 kpc, respectively. A comparison of the Lyα emission extracted from the same aperture of the CO(5-4) emission revealed blue-skewed Lyα spectra for all four sources, and comparable line widths. We use this dataset to attempt a first calculation of the total mass of the system and forecast its evolution.
ACKNOWLEDGMENTS We thank  Ian Smail  and  Francesco Valentino  for providing comments on an early version of this work. CCC acknowledges support from the  Ministry of Science and Technology of Taiwan  ( MoST 109-2112-M-001-016-MY3 ). H. B. L. is supported by the  Ministry of Science and Technology (MoST) of Taiwan  (Grant Nos. 108-2112-M-001-002-MY3 ). Y. Y. was supported by  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science, ICT & Future Planning  ( NRF-2019R1A2C4069803 ). A. M. is supported by a  Dunlap Fellowship  at the  Dunlap Institute for Astronomy & Astrophysics , funded through an endowment established by the  David Dunlap family  and the  University of Toronto. The University of Toronto operates on the traditional land of the  Huron-Wendat , the  Seneca , and most recently, the Mississaugas of the Credit River.
A. M. is grateful to have the opportunity to work on this land. AO is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -443044596. This project has received funding from the  European Research Council (ERC)  under the  European Union   Horizon 2020 research and innovation programme (MagneticYSOs project , grant agreement No  679937 ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  757535 ). This work has been supported by  Fondazione Cariplo , grant No  2018-2329. Based on observations collected at the  European Organisation for Astronomical Research in the Southern Hemisphere  under  ESO  programmes  094. A-0585 (A),  096. A-0937 (A),  098.
Atomistic resolution information on macromolecular assemblies, on the other hand, is gathered by X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy (cryo-EM) techniques (as deposited in the Protein Data Bank (PDB))  [13]. Recent advances in cryo-EM techniques in particular have put the structural determination of large molecular machines within our reach  [14, 15]. The interested reader will find a detailed listing of the various biophysical methods used for the detection of PPIs, listing their advantages and disadvantages, in a recent review by Zhou et al. [16].
Analysis of the Interface Dynamics Various tools are now available that have been tuned to process all-atom MD trajectories. These tools are developed to gauge changes in covalent (intra) and non-covalent (inter) interactions, as well as changes in the intrinsic dynamics of a biomolecular complex. These analysis tools usually come together with the simulation package used to create the relevant MD trajectory. In the case of GROMACS, for example, a vast collection of gmx scripts allow the user to calculate changes in interfacial backbone-related terms (such as dihedral angles), hydrogen bonds, salt bridges, specific interfacial distances, buried surface area, interfacial water molecules, and RMSDs/RMSFs  [94]. The corresponding analysis package for NAMD is VMD (Visual Molecular Dynamics)  [95] , whereas for AMBER, it is CPPTRAJ/PYTRAJ (Amber Tools)  [96].
Next to their own internal formats, all these tools can also work with any ensemble file, if recorded in the PDB format. Among these tools, gmx and CPPTRAJ are command-line-based, VMD is GUI-based, and PYTRAJ provides something in between, i. e. , it runs on a Jupyter notebook with the NGL viewer molecule visualization option. If the user requires an analysis that is beyond what is provided by these widely used simulation engines, then they can refer to cross-platform analysis tools, such as MDTraj and MDAnalysis  [97]. Both tools are composed of various Python libraries to perform MD trajectory analysis. Besides their common analysis functions, they have tool-specific options, such as the calculation of fraction of native contacts (MDAnalysis) or an extended hydrogen bond analysis, where different h-bond definitions can be invoked (MDTraj).
There are also approaches that utilize the libraries of MDAnalysis and MDTraj. One such tool, ProLIF, is specifically finetuned for calculating non-covalent interfacial contact types (hydrogen bond donor/acceptor, pi stacking, anionic, cationic, etc. ) and their propensities throughout the whole trajectory  [98]. A similar tool to ProLIF is Interfacea, another Python library, which provides interfacial non-bonded contacts, as well as OpenMM-based interaction energetics  [99]. gRINN can also calculate residue-based interface energetics using a network-based approach  [100]. The gRINN tool uses energy values calculated either by GROMACS or VMD to present residue-based energy changes and correlations through a GUI. ProDy and MD-TASK, two other network-based tools, allow us to calculate the essential dynamics of the whole complex, from which the interface region could be specifically investigated  [101, 102].
enable tracing of the trajectory details over a web browser by keeping the predefined structural annotations. Each visualization session has a unique link, which can be shared and reworked. This is achieved using NGLview and MDSrv tools that are built upon the MDAnalysis package  [105, 106]. The use of this tool holds great potential to provide a general understanding of the protein-protein interaction dynamics explored by MD trajectories. Table  1. List of the described MD analysis tools that can be used to dissect interface dynamics.