dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, we discussed future improvements that can be introduced to optimize the usage of these and similar data, to improve the system sensitivity, and, overall, the chances for the detection of pulsars that likely still hide in the vicinity of the SMBH Sgr A *. The top two panels corresponds to data from phased ALMA, the middle two panels to the LMT, and the bottom two panels to IRAM 30 m Telescope data. Each pair of panels corresponding to one station show the following: (top panels) the raw total intensity data just after the conversion to PSRFITS, and (bottom panels) the same total intensity time series after the preparation and cleaning described in Section 2. 2. A description of the main features of the data from each station is presented in Section 2. 2. The rightmost panels present a histogram of the distribution of the samples in linear scale.
After the cleaning, the histograms tend to a Gaussian shape (although not perfect), indicating that the filtering and flagging schemes result in statistically better-behaved data. The top two panels corresponds to data from phased ALMA, the middle two panels to the LMT, and the bottom two panels to IRAM 30 m Telescope data. Each pair of panels corresponding to one station show the following: (top panels) the raw total intensity data just after the conversion to PSRFITS, and (bottom panels) the same total intensity time series after the preparation and cleaning described in Section 2. 2. A description of the main features of the data from each station is presented in Section 2. 2. The panels on the right show an even closer zoom in, to facilitate the identification of the main issues of each data set.
Acknowledgments We are grateful to the anonymous referee for the review and providing suggestions that improved the manuscript. We thank the staff at the participating observatories and correlator centers that made possible the EHT 2017 observations. P. T. thanks  Pablo Mellado  and  William Robertson  for their support through several stages of the data reduction in the IRAM servers. R. P. E. is funded by the  Chinese Academy of Sciences President's International Fellowship Initiative , grant No. 2021FSM0004. S. M. R. is a CIFAR Fellow and is supported by the  NSF Physics Frontiers Center  awards  1430284  and  2020265. This work was supported by the  European Research Council   Synergy Grant  " Black-HoleCam: Imaging the Event Horizon of Black Holes " (grant  610058 ). This paper makes use of the following ALMA data: ADS/JAO.
Sloan Research Fellowship ;  Allegro , the  European ALMA Regional Centre node in the Netherlands , the NL astronomy research network NOVA and the astronomy institutes of the  University of Amsterdam ,  Leiden University  and  Radboud University ; the  ALMA North America Development Fund ; the  Astrophysics and High Energy Physics program  by  MCIN  (with funding from  European Union NextGenerationEU ,  PRTR-C17I1 ); the  Black Hole Initiative , which is funded by grants from the  John Templeton Foundation  and the  Gordon and Betty Moore Foundation  (although the opinions expressed in this work are those of the author(s) and do not necessarily reflect the views of these Foundations); the  Brinson Foundation ; Chandra  DD7-18089X  and  TM6-17006X ; the  China Scholarship Council ; the  China Postdoctoral Science Foundation  fellowships ( 2020M671266 ,  2022M712084 );  Consejo Nacional de Ciencia y Tecnología (CONACYT, Mexico , projects  U0004-246083 ,  U0004-259839 ,  F0003-272050 ,  M0037-279006 ,  F0003-281692 ,  104497 ,  275201 , and  263356 ); the  Consejería de Economía, Conocimiento, Empresas y Universidad of the Junta de Andalucía  (grant  P18-FR-1769 ), the  Consejo Superior de Investigaciones Científicas  (grant  2019AEP112 ); the  Delaney Family  via the  Delaney Family John A.
Wheeler Chair  at  Perimeter Institute ;  Dirección General de Asuntos del Personal Académico-Universidad Nacional Autónoma de México (DGAPA-UNAM , projects  IN112417  and  IN112820 ); the  Dutch Organization for Scientific Research (NWO) for   VICI award  (grant  639. 043. 513 ), grant OCENW. KLEIN. 113 and the Dutch black hole Consortium (with project No. NWA 1292. 19. 202) of the research program the National Science Agenda; the Dutch National Supercomputers, Cartesius and Snellius (NWO grant 2021. 013); the EACOA Fellowship awarded by the East Asia Core Observatories Association, which consists of the Academia Sinica Institute of Astronomy and Astrophysics, the National Astronomical Observatory of Japan, Center for Astronomical Mega-Science, Chinese Academy of Sciences, and the Korea Astronomy and Space Science Institute; the European Union Horizon 2020 research and innovation program under grant agreements RadioNet (No 730562) and M2FINDERS (No 101018682); the Horizon ERC Grants 2021 program under grant agreement No.
101040021; the Generalitat Valenciana (grants APOSTD/2018/177 and ASFAE/2022/018) and GenT Program (project CIDEGENT/ 2018/021); MICINN Research Project PID2019-108995GB-C22; the European Research Council for advanced grant "JETSET: Launching, propagation and emission of relativistic jets from binary mergers and across mass scales" (grant No. 884631); the Institute for Advanced Study; the Istituto Nazionale di Fisica Nucleare (INFN) sezione di Napoli, iniziative specifiche TEON-GRAV; 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 Columbia/Flatiron Postdoctoral Fellowship, 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 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  grants  HST-HF2-51431.
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. This work was partially supported by FAPESP (Fundação de Amparo á Pesquisa do Estado de São Paulo) under grant 2021/01183-8. APEX is a collaboration between the Max-Planck-Institut für Radioastronomie (Germany), ESO, and the Onsala Space Observatory (Sweden). 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.
We consider MoS 2 membranes with dimensions of 7. 5 × 7. 5 nm 2 and pores of diameter D = 1. 3 nm. Last but not least, the membrane is considered globally neutral, with atomic partial charges q i for Mo and S computed from the charge equilibration algorithm  (Rappe and Goddard, 1991; Nakano, 1997)  in vacuum using ReaxFF, available in the LAMMPS software package  (Ostadhossein et al. , 2017). Partial charges, on average, are around +0. 42 for Mo atoms and -0. 21 for S atoms, and the distribution of partial charges relative to the center of the pore is shown in Supplementary Figure  S1. As expected, partial charges are strongly influenced by the presence of the pore (vacancies) at the center of the membrane, with a decrease of partial charges for S atoms at the mouth of the pore and a decrease or increase for Mo atoms partial charges depending on their S environment (see Supplementary Figure  S1 ).
The modeling of partial charges is essential for a better description of electrostatic interactions between the peptide, the electrolyte with membrane atoms belonging to the edge of the nanopore. Biological peptides were built using the AmberTools software. From the sequence of amino acids defining the peptide, the module leap creates the all-atom structure from a database. The initial structure of the peptide created that way does not exhibit a particular 3D shape and is linear (Figure  1A ). During MD simulations, the structure of the peptide is fully relaxed and can adopt any conformation. However, during the translocation process, the peptide is elongated in the nanopore due to its small diameter. In this work, we study the translocation of 20 distinct peptide sequences made of a lysine heptapeptide plus one of the twenty natural amino acids (Figure  1B ).
Structural break detection and clustering analysis Structural break detection was performed using the Chow test, an algorithm used when a potential structural break in the time series may be recognized a priori. The principle is to evaluate the parameter stability, namely, to determine if the underlying regression model parameters have remained unchanged. In this case, peptide-induced blockade event ionic current data were split by one point in time, getting two different datasets. The null hypothesis of the Chow test asserts that true coefficients in two linear regressions on these two datasets are equal. Structural changes take place in points where the null hypothesis is rejected  (Aronov et al. , 2019; Sun and Wang, 2022). Clustering was performed using the Gaussian mixture model  (Reynolds, 2009)  (GMM) for which Gaussian free parameters (π k , μ k , and Σ k ) representing the weight, the means, and the covariances, respectively, were estimated from the expectation-maximization (EM) algorithm  (Dempster et al.
, 1977). To do so, we used scikit-learn, which is an open-source machine learning Python library. In addition, to estimate the number of sub-populations for each amino acid, we used the Bayesian Information Criterion (BIC) score to estimate the proper number of components K to GMM  (Schwarz, 1978)  (Supplementary Figure  S13 ). In addition, full and tied covariances were set as a parameter of the model for 1D and 2D clustering, respectively. Finally, the convergence threshold used was 0. 001, which means that when the lower bound average gain falls under this limit, EM iterations will end. From GMM clustering outputs, i. e. , cluster means < ΔI b > and < τ b > , we computed 2D probability densities P(< ΔI b >, < τ b >) using 20 and 30 bins, respectively. The convergence of GMM clustering techniques applied to 1D (Figure  3 ) and 2D probability densities (Figure  4 ) as a function of input data is presented in Supplementary Figure  S14.
Probability densities of blockade ionic current traces Figure  3C  shows the probability densities P(I b ) for each amino acid grouped per family. Overall, the superimposed densities do not exhibit well-separated populations between the amino acids within a family, as measured experimentally for biological nanopores  (Ouldali et al. , 2020). Nevertheless, some notable exceptions are observed and discussed in the following text. In the present work, P(I b ) densities present multiple peaks for each amino acid, i. e. , sub-populations, which means that different fingerprints of blockade current exist during translocation simulations through MoS 2 nanopores. Per amino acid, the number of sub-populations in the data was assessed by using the Gaussian mixture model (GMM) clustering technique associated with the Bayesian Information Criterion (BIC, see Materials and methods).
, 2015; Nicolaï et al. , 2020) , is ACABCC. As a proof of concept, we tested the CGSEQ protein sequencing hypothesis by using the protein sequences available from the ASTRAL database  (Brenner et al. , 2000) , which provides representative subsets of proteins after the elimination of doublons and sequence identity larger than 95%. It corresponds to a total of 13,000 protein sequences instead of 35,000 available. For each pair of sequences of the same length, we computed the CGSEQ percentage identity as the normalized dot product between simplified sequences by assigning value 1 for the product of two identical symbols and 0 otherwise. For example, the dot product of ACAB with BCAA is (0 + 1 + 1 + 0)/4 = 0. 5. As shown in Figure  5A , the average percentage of CGSEQ identity, computed considering at least 10 protein sequences of the same length for each length available, varies from 9.
Funding The authors declare financial support was received for the research, authorship, and/or publication of this article. This work is part of the project SEPIA supported by the EIPHI Graduate School (contract ANR-17-EURE-0002), the Conseil Régional de Bourgogne-Franche-Comté, and the European Union through the PO FEDER-FSE Bourgogne 2021/2027 program.
2010 Mayer et al. , 2015;; Ferrara et al. 2013; Bonoli et al. 2014). Altogether, while existing studies show a tight coevolution of SMBHs, AGN, and their host galaxies with cosmic time, this interplay is still substantially debated and unconstrained. This is at least partially due to the difficulty of building large samples of distant AGN with well-characterized stellar and black hole properties. In order to better understand the growth of SMBHs with cosmic time and their coevolution with their host galaxies, we have built a sample of distant radio-loud AGN spanning about 9 Gyr of cosmic time, between z ∼ 0. 3-4, with available radio to ultraviolet (UV) spectrophotometric data. Based on this multiwavelength dataset, we assess the properties of the AGN sample, for example, in terms of black hole and stellar masses, jet power, and Eddington ratio.
As radio-loud AGN are associated with the most massive black holes and host galaxies (e. g. ,  Best et al. 2005; Chiaberge & Marconi 2011; Shen et al. 2011; Shaw et al. 2012) , they are excellent sources in which to investigate the galaxy, AGN, and SMBH coevolution in the high-mass regime. The paper is organized as follows. In Sect. 2 we describe our sample selection as well as its multiwavelength dataset and properties. In Sect. 3 we report estimates for the black hole, jet, accretion, and stellar properties. In Sect. 4 we describe our comparison sample. The results in terms of the scaling relations of black hole, jet, and host galaxy are reported in Sect. 5. In Sect. 6 we summarize the results and draw our conclusions. Throughout this work, we adopt a flat ΛCDM cosmology with matter density Ω m = 0.
Dark Energy Survey We started by considering the Dark Energy Survey (DES The Dark Energy Survey  Collaboration 2005 Collaboration , 2016)) , which is composed of two distinct multiband imaging surveys: a ∼5000 deg 2 wide-area grizY survey, and a deep supernova griz survey consisting of six distinct deep fields  (Hartley et al. 2021 ). The coadded source catalog and images from the processing of all six years of DES wide-area survey observations and all five years of DES supernova survey observations have recently been made public with the DES data release 2  (Abbott et al. 2021)  1. To build our sample of distant radio-loud AGN, we limited ourselves to equatorial DES supernova fields that overlapped with the VLA FIRST survey at low radio frequencies. The selection was similar to that of our previous work  (Castignani et al.
Radio-loud AGN in GAMA DR3 In addition to the radio-loud AGN selected as described in the previous subsections, we searched for distant radio sources from the third data release (DR) of the Galaxy And Mass Assembly (GAMA) spectroscopic survey. GAMA DR3  (Baldry et al. 2018)  provides spectra obtained with the AAOmega multi-object spectrograph on the Anglo-Australian Telescope (AAT) as well as a wealth of ancillary information for more than 200 000 sources. Similarly to what we did concerning the SDSS spectra, we first selected 632 sources at z > 0. 3, with available GAMA DR3 spectra, Hα or Hβ line fluxes at S /N > 3 as well as line widths FWHM > 1000 km s -1 , as inferred from single Gaussian modeling 4. We selected these sources within the equatorial area of 180 deg 2 covered by GAMA DR3 as well as by the 1.
4 GHz FIRST VLA survey. For the 632 spectroscopic sources from GAMA, we further limited ourselves to the subsample of 39 galaxies with available 1. 4 GHz fluxes from the FIRST VLA survey. Multiple spectra are often available in the GAMA DR3 database. We then inspected each of the available spectra and discarded galaxies with dubious evidence of emission lines, which means that the associated fits are dubious as well. This analysis yielded 21 GAMA DR3 radio sources at z ∼ 0. 3-0. 8. We assigned unique WISE counterparts to them using a 6. 5 arcsec search radius, as in Sect. 2. 3. By combining these galaxies, denoted hereafter with the prefix G, with the 21 radio sources with SDSS spectra, denoted with the prefix RS, our final sample comprises 42 sources that we consider hereafter for this study.
The majority (22 out of 42, i. e. , 52%) of our sources are in fact classified as quasars in the NASA/IPAC extragalactic database (NED), for instance, with counterparts in the 2dF-SDSS LRG And QSO (2SLAQ;  Croom et al. 2009 ) catalog, or with X-ray conterparts (XMM;  Rosen et al. 2016) , as outlined in Table  A. 1.
g. ,  van den Bosch et al. 2012; Emsellem 2013; Scharwächter et al. 2016). Motivated by these studies, we searched in the literature for clusters around the radio sources in our sample, searching by coordinates in the NASA/IPAC Extragalactic Database (NED). NED includes several catalogs of clusters that were identified in wide-field surveys  (Goto et al. 2002; Koester et al. 2007; McConnachie et al. 2009; Hao et al. 2010; Durret et al. 2011 Durret et al. , 2015;; Wen et al. 2012; Radovich et al. 2017; Rykoff et al. 2012; Oguri et al. 2018 ). Our search yielded three matches. Radio sources RS 49, G 372455, and G 748815 are in the cores (at cluster-centric distances 0. 5 Mpc) of the clusters [LIK2015] J034. 16359-04. 73395 (z = 0. 89;  Lee et al. 2015) , WHL J090325. 6+011215 (z = 0. 31;  Wen et al.
Acknowledgements. We thank the anonymous referee for helpful comments which contributed to improve the paper. GC acknowledges the support from the grants  ASI n. 2018-23-HH. 0and PRIN-MIUR 2017 WSCC32. We thank  Christophe Benoist  for helpful discussion about the exploitation of DES data. This publication makes use of data products from the  Wide-field Infrared Survey Explorer , which is a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory/California Institute of Technology , and  NEO-WISE , which is a project of the  Jet Propulsion Laboratory/California Institute of Technology. WISE and NEOWISE are funded by the  National Aeronautics and Space Administration. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration.
20, 21 By incorporating factors contributing to a drug's interindividual variability, PBPK modeling can be applied to support model-informed precision dosing. 22 Moreover, PBPK modeling is widely acknowledged for the use in model-informed drug discovery and development (MID3), demonstrated by a growing number of PBPK applications submitted to regulatory agencies to address specific research questions, mainly related to DDIs. 23 bjectives of the presented study were therefore (a) the development of a whole-body PBPK model of tacrolimus covering different CYP3A5 activities and clinically relevant formulations as well as the prediction of (b) the influence of food intake on tacrolimus PK and (c) DD(G)Is involving different CYP3A perpetrators, especially azole antifungal agents. The final model will be made publicly accessible (https://github.
Software PK-Sim® and MoBi® Version 10 (Open Systems Pharmacology Suite, www. open-syste ms-pharm acolo gy. org, 2021) were used for the development of the tacrolimus PBPK model, parameter identification (Levenberg-Marquardt algorithm), and model sensitivity analyses. Published clinical study data were digitized with the help of Engauge Digitizer Version 12. 1 (Mitchell et al. 24  ) according to best practices. 25 The compilation of plots as well as calculations of PK parameters and quantitative model performance measures were performed using the R programming language Version 4. 2. 1 (R Foundation for Statistical Computing).
Clinical study data Whole blood concentration-time profiles of tacrolimus in healthy volunteers were gathered from the literature, covering a broad dosing range of tacrolimus administered intravenously and orally in different formulations. Digitized profiles were divided into a training dataset for model development and a test dataset for model evaluation (ratio 1:3). Here, profile assignment to the test and training datasets was accomplished in a nonrandomized fashion to maximize the set of profiles for model evaluation (test dataset) while preferring information-dense (frequent measurements within a long sampling period) and heterogenous profile data to capture the range of different routes of administration, formulations, and dosage regimens for the training dataset.
PBPK model building Model development was initiated with an extensive literature search for physicochemical parameters as well as information on the absorption, distribution, metabolism, and excretion of tacrolimus. For each included study, a representative virtual individual was established based on the mean and mode for age, sex, weight, height, body mass index, and ethnicity of the respective study publication. If demographic information was incomplete, default settings were adopted from the population database accessible in PK-Sim®. Relative expression of relevant transporters and enzymes in different organs was implemented according to the PK-Sim® expression database as described in Table  S1. A virtual population of 1000 individuals was created for each study population using the respective reported demographic information to visually assess variability based on these characteristics and metabolizing enzymes.