dataesr/scientific-paragraphs-categorization huggingface dataset:
Interestingly, the mAb binding epitope also partially covers the antiparallel b5, b6 strands of Spike (residues 451-456 and 491-495), a domain that should not be subjected to the development of resistance mutations owing to its essential folding function (Figure  S5B ). To understand further why P5C3 binding is not affected by mutations harbored by SARS-CoV-2 variants, we superimposed our structure with that solved of the ACE2-RBD interaction (PDB: 6M0J)  (Lan et al. , 2020)  (Figures  5A  and 5B ). ACE2 covers around 860 A ˚2 on the RBD, compared with 600 A ˚2 for P5C3 where P5C3 interacts with the RBD ridge at a 90 angle compared with a 130 angle for ACE2  (Wang et al. , 2020b) (Figures 5A and 5B). Importantly, $70% (414 A ˚2 out of 600 A ˚2) of the P5C3 buried surface area is shared with the ACE2 site on the RBD.
P5C3 and ACE2 binding overlaps with L455, F456, A475, G476, S477, E484, F486, N487, Y489 and Q493 of the RBD, which constitute a core for tight binding. Indeed, these residues form a hydrophobic patch surrounding F486 on the RBD, where F486 interacts with Q24, L79, M82, and Y83 of ACE2. Furthermore, additional critical residues necessary for RBD interaction with ACE2 are blocked by P5C3, such as F456 and Q493  (Lan et al. , 2020)  (Figure  S5D ). We went on to compare the P5C3 binding mode to that of leader ACE2 blocking mAb candidates currently in clinical trials for REGN10933, REGN10987 (PDB: 6XDG)  (Hansen et al. , 2020a) , and LY-CoV016 (PDB: 7C01)  (Shi et al. , 2020) (Figure 5C). It was recently demonstrated that the neutralizing activity of these three mAbs could be negatively affected by mutation identified in circulating variants that are not affecting ACE2 binding to the open RBD  (Starr et al.
The SARS-CoV-2 strain used in the hamster study, BetaCov/Belgium/GHB-03021/2020 (EPI ISL 109407976|2020-02-03), was recovered from a nasopharyngeal swab taken from an RT-qPCR confirmed asymptomatic patient who returned from Wuhan, China in the beginning of February 2020. A close relation with the prototypic Wuhan-Hu-1 2019-nCoV (GenBank accession 11 number MN908947. 3) strain was confirmed by phylogenetic analysis. Infectious virus was isolated by serial passaging on HuH7 and Vero E6 cells  (Boudewijns et al. , 2020) ; passage 6 virus was used for the study described here. The titer of the virus stock was determined by end-point dilution on Vero E6 cells by the Reed and Muench method. Live virus-related work was conducted in the high-containment A3 and BSL3+ facilities of the KU Leuven Rega Institute (3CAPS) under licenses AMV 30112018 SBB 219 2018 0892 and AMV 23102017 SBB 219 20170589 according to institutional guidelines.
Staining was performed outside the BSL3 laboratory with 50 ml of 0. 1% crystal violet solution for 20 minutes at RT. Wells were washed 3 times with water and plates were dried, scanned and analyzed for the density of live violet stained cells using ImageJ software (National Institutes of Health). For each 96-well plate, at least 4 wells were treated with a negative pool of sera from pre-pandemic healthy donors and 4 wells with virus only and used as negative and positive controls respectively. The percent inhibition of cytopathic effect of the virus was calculated using the formula: % Inhibition = ([cell density Test dilution -cell density with virus only] / [cell density negative control no virus infection -cell density with virus only]) 3 100. Serum dilution response inhibition curves were generated with GraphPad Prism 8.
3. 0 using NonLinear four parameter curve fitting analysis of thelog(agonist) versusresponse. Neutralization EC 50 and EC 80 values were calculated using the GraphPad prism NonLinear four parameter curve fitting analysis. Spike-pseudotyped lentivector production and neutralization assays HDM-IDTSpike-fixK plasmid (BEI catalog number NR-52514, obtained from J. D. Bloom, Fred Hutchinson Cancer Research Center) encoding the Wuhan-Hu-1 (or 2019-nCoV) SARS-Cov2 Spike (GenBank NC_045512) was modified using QuickChange mutagenesis to generate mutants with D614G alone or in combination with A222V, N439K, S477N and E484K/N501Y and K417N/E484K/N501Y as previously described  (Fenwick et al. , 2021b). Spike-pseudotyped lentivectors were generated by co-transfecting HDM-IDTSpike-fixK, pHAGE2-CMV-Luc-ZSgreen, Hgpm2, REV1b and Tat1b (a kind gift from J.
Cryo-EM sample preparation For cryo-EM, P5C3 Fab with Spike D614G complexes (3 mL aliquot of Spike with Fab (1:4 molar ratio)) were almost immediately applied to a freshly glow-discharged Quantifoil holey carbon grids (R2. 0/2. 0, 200 mesh, copper) at 1 mg/ml and. The grids were placed in an automatic plunge freezing apparatus Vitrobot Mark IV (Thermo Fisher, Hillsboro, USA) to control humidity (100%) and temperature (22 C). The blotting time was set for 2. 0-3. 0 s using filter paper (grade 595, Ted Pella Inc. ) before vitrification in liquid ethane. Cryo-EM data collection and image processing Dose-fractionated images (i. e. , movies) were recorded with a FEI Titan Krios (Thermo Fisher), operated at 300kV, and equipped with a Gatan Quantum-LS energy filter (20 eV zero-loss energy filtration) followed by a Gatan K2 Summit direct electron detector.
, 2017). Approximately 2 million of particles were used to generate four Ab initio volumes were generated, with one class of 585 005 particles revealing an S-trimer complexed with 3 Fabs. The map of interest was processed by non-uniform refinement with C1 symmetry to generate a map with a resolution of 3. 5A ˚. Next, to improve the Fab RBD interface resolution, a local refinement was performed with mask shown in Figure  S4A. All procedures were carried out in cryoSPARC v3. 1. 0 (Punjani et al. , 2017). The reported resolutions are based on the gold-standard Fourier shell correlation curves (FSC = 0. 143) criterion  (Rosenthal and Henderson, 2003). Cryo-EM model building and analysis UCSF Chimera  (Pettersen et al. , 2004)  and Coot  (Emsley et al. , 2010)  were used to fit atomic model (PDB ID: 7K4N)  (Tortorici et al.
QUANTIFICATION AND STATISTICAL ANALYSIS All Luminex binding studies for Spike affinity and for detection of P5C3 antibody levels in plasma from hamsters and serum from hFcRn mice were performed with a 200 Bioplex instruments with data analysis and visualized using GraphPad Prism 8. 3. 0. Cytopathic effect in the live virus neutralization assays was monitored by analyzing the density of live violet stained cells at a given concertation of antibody using ImageJ software (National Institutes of Health). Data analysis and figures were visualized using Graph-Pad Prism 8. 3. 0. Luciferase signals detected for the SARS-CoV-2 Spike pseudoviral assays were measured on a Synergy H1 instrument (BioTek) using the Gen5 Software and with data analysis and visualized using GraphPad Prism 8. 3. 0. Statistical parameters including the exact value of n, the definition of center, dispersion, and precision measures (Mean or Median ± SEM) and statistical significance are reported in the Figures and Figure  Legends.
Data were judged to be statistically significant when p < 0. 05. In Figures, asterisks denote statistical significance as calculated using the two-tailed non-parametric Mann-Whitney U test for two groups' comparison. Analyses were performed in GraphPad Prism (GraphPad Software, Inc. ) and Microsoft Excel. In vivo terminal half-lives for P5C5 and P5C3 LS were calculated using a one-phase exponential decay analysis.
ACKNOWLEDGMENTS We thank the  Service of Immunology and Allergy at the Lausanne University Hospital  for contributions in analysis of subject serum samples for levels of anti-Spike protein IgG antibodies,  Michael Moulin  for establishing the  SEC-HPLC  protocol for antibody characterization,  Antonio Mancarella  for initial help with the mAb cloning,  Laurence Durrer  and  Soraya Quinche  from  PTPSP-EPFL  for mammalian cell culture, and  Michae ¨l Francois from PTPSP-EPFL  for helping in purification of the Spike trimer proteins. We would like to thank the  Trono laboratory ,  Caroline Tapparel  for help to start the SARS-CoV-2-related work,  I. Eckerle  for providing a SARS-CoV-2 isolate, and  Valeria Cagno  for providing the initial stocks of titrated SARS-CoV-2 virus;  Dr. Chami  from  C-CINA, Biozentrum, Uni Basel  for FEI Titan Krios images acquisition;  Christel Genoud  at the EPFL for initial EM studies; and  Ni Dongchun  at the EPFL for precious advice on cryo-EM.
We would also like to thank  David Wyatt  and members of the  CARE-IMI  work package 4 team for helpful discussions. G. P. received a grant from the  Corona Accelerated R&D in Europe (CARE)  project funded by the  Innovative Medicines Initiative 2 Joint Undertaking (JU)  under grant agreement no. 101005077. The JU receives support from the  European Union 's  Horizon 2020 research and innovation program , the  European Federation of Pharmaceutical Industries Associations (EFPIA) , the  Bill & Melinda Gates Foundation , the  Global Health Drug Discovery Institute , and the  University of Dundee. The content of this publication reflects only the authors' views, and the JU is not responsible for any use that may be made of the information it contains. Furthermore, funding was also provided through the  Lausanne University Hospital  (to G.
Data and code availability d Data describe are available upon request and have been deposited at https://github. com/Laurent-9475/Cell-report and are publicly available as of the date of publication. The cryo-electron maps reconstruction of the Spike complex by Electron microscopy was deposited to the EMDataBank with the identifier EMD-13190 and EMD-13415. d This paper does not report original code. d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
Some studies find the brightest radio AGN, thus FRIIs, to reside in massive hosts within clusters, while others find that FRIs should reside in denser environments. The picture of the relation of the radio structure, physical properties, and environment is therefore clearly still under debate. To better understand what affects the structure of radio sources, we need to carry out a systematic study of the radio structure and host/BH properties, and the large-scale environment of radio galaxies at both high resolution and sensitivity levels. For this purpose, we used the 3 GHz VLA-COSMOS Large Project  (Smolčić et al. , 2017a)  and extensive auxiliary data for the COSMOS 1  field. These data cover a wide range of multi-wavelength properties and environmental probes that are essential to performing such a study.
This envelope was chosen in order to include the most extended and brightest sources identified by blobcat. This selection yielded 351 blobs, which were visually inspected and matched to 350 sources. The matching procedure is described in detail in  Vardoulaki et al. (2019) , which present the multi-components sources (composed of more than one radio blob) also studied in this paper. The matching was made with the aid of COSMOS auxiliary data in order to properly join the radio blobs to their parent source, when needed, and to the host galaxy. The host identification was made by visually crossmatching the radio core/body of galaxies with the optical/infrared stacked Y JHK S image from the Ultra Deep Survey with the VISTA telescope (Ultra-VISTA; see  Laigle et al. , 2016; Smolčić et al. , 2017b , and references therein), including regions observed at z ++ with an upgrade of the Subaru Suprime-Cam (see  Taniguchi et al.
, 2007; Smolčić et al. , 2017b; Taniguchi et al. , 2015). We also made use of the 1. 4 GHz data  (Schinnerer et al. , 2010)  which, when available, served as confirmation of the matching procedure. Most of the multicomponent sources (IDs from 10900-10966 in Table  C. 1) have a radio core, with the exception of sources 10915, 10926, and 10937 which show no radio core. Sources without an infrared host were matched on the basis that their radio emission resembled a radio  AGN (sources 10908, 10922, 10924, 10932, and 10938). The single-component sources in our sample, all have associated infrared hosts, with the exception of sources 115 and 248. Hales et al. , 2012)  versus signal-to-noise ratio for the 10899 entries in the blobcat catalogue identified from the 3 GHz mosaic of the COSMOS field.
FR classification We provide an FR-type classification based on the radio structure at 3 GHz, taking advantage of the COSMOS auxiliary data to identify the host galaxy, as described above. The 350 radio sources above the envelope in Fig. 1  were classified by visual inspection in three stages. At stage one they were given to a team of non-experts on radio AGN with a set of guidelines. These guidelines (see Sec. A. 1) described how to visually separate the FRI, FRII, hybrid FRI/FRII, and non-FR-type radio sources based on the FR classification scheme  (Fanaroff & Riley, 1974). The second stage of FR classification was a revision of the results from the first stage, and a selection by two experts on FR-type radio sources of a sub-sample of 130 objects which exhibit jets and lobes 2. These were taken to stage three, where we manually measured the distribution of flux-density along their structure based on the following criteria: -FRIIs or edge-brightened: the distance from the core to the brightest point along their structure is more than half of the total size of the source; these objects exhibit lobes (e.
For the higher redshift galaxies, they used a large dataset of sub-millimetre (submm) photometry from JCMT/SCUBA-2, LABOCA, Bolocam, JCMT/AzTEC, MAMBO, ALMA, and PdBI (see Sect. 2. 2 in  Delvecchio et al. , 2017, for references and discussion). For classification purposes, they also used the Chandra-COSMOS  (Elvis et al. , 2009; Civano et al. , 2012)  and COSMOS-Legacy  (Civano et al. , 2016)  X-ray catalogues. We further cross-correlated our FR and COM AGN samples with the most recent X-ray group catalogue for the COSMOS field, from  Gozaliasl et al. (2019) , which is an updated version of the George et al. (  2011 ) X-ray group catalogue. By groups we refer to a set of galaxies with a common dark matter halo  (George et al. , 2011). The  Gozaliasl et al. (2019)  catalogue includes 247 groups at 0.
We identify all these sources thin the original cutout of 160⇥160 arcsec and discard all the els in their skeleton -within a given aperture -for further analyses (e. g. clustering). We leave the radius for this aperture (r_resolved) as an input parameter. In this case, to assure that all the pixels from the skeleton of resolved point-like sources are removed from the field, we chose a radius of 10 pixels, or ⇠2. 5 arcsec (⇠3 times the synthesized beam). V) Clustering, mean-shift algorithm The next question is which islands within the cutout belong to the same radio source. This is particularly important when dealing with crowded fields and multiple sources, which can also be extended. Nowadays there are di↵erent clustering algorithms on 2D datasets, however, most of them rely on the number of clusters to find as an input.
We identify all these sources within the original cutout of 160⇥160 arcsec and discard all the pixels in their skeleton -within a given aperture -for further removed from the field, we chose a radius of 10 pixels, or ⇠2. 5 arcsec (⇠3 times the synthesized beam). V) Clustering, mean-shift algorithm The next question is which islands within the cutout belong to the same radio source. This is particularly important when dealing with crowded fields and multiple sources, which can also be extended. Nowadays there are di↵erent clustering algorithms on 2D datasets, however, most of them rely on the number of clusters to find as an input. On the other hand, the so-called mean shift algorithm  (Fukunaga & Hostetler 1975; Cheng 1995 ) is a nonparametric clustering technique which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters, which is very convenient for our goal.
V) Clustering, mean-shift algorithm The next question is which islands within the cut-out belong to the same radio source. This is particularly important when dealing with crowded fields and multiple sources, which can also be extended. Nowadays there are different clustering algorithms on 2D datasets, however, most of them rely on the number of clusters to find as an input. On the other hand, the so-called mean shift algorithm  (Fukunaga & Hostetler, 1975; Cheng, 1995)  is a nonparametric clustering technique which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters, which is very convenient for our goal. Mean shift clustering is a centroid based algorithm, which works by updating candidates for centroids to be the mean of the points within a given region.
Acknowledgements. We would like to thank the anonymous referee for useful comments which significantly improved our manuscript. EV acknowledges funding from the  DFG  grant  BE 1837/13-1  and from the  VLBI group in MPIfR. ID is supported by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  788679. Support for BM was provided by the  DFG priority program 1573  "The physics of the interstellar medium". EV, EFJA, AK, BM and FB acknowledge support of the Collaborative Research Center 956, subproject  A1  and  C4 , funded by the  Deutsche Forschungsgemeinschaft (DFG). EV and EFJA would like to thank  Nils Linz ,  Vishnu Balakrishnan  for their assistance during the first phase of radio classification. This manuscript was submitted and reviewed during the covid-19 pandemic.
Background Budigalimab is a humanized, recombinant immunoglobulin G1 monoclonal antibody targeting programmed cell death protein 1 (PD-1). We present the safety, efficacy, pharmacokinetic (PK), and pharmacodynamic data from patients enrolled in the head and neck squamous cell carcinoma (HNSCC) and non-small cell lung cancer (NSCLC) expansion cohorts of the phase 1 first-in-human study of budigalimab monotherapy (NCT03000257; registered 15 December 2016). Patients and methods Patients with recurrent/metastatic HNSCC or locally advanced/metastatic NSCLC naive to PD-1/ PD-1-ligand inhibitors were enrolled; patients were not selected on the basis of oncogene driver mutations or PD-L1 status. Budigalimab was administered at 250 mg intravenously Q2W or 500 mg intravenously Q4W until disease progression/unacceptable toxicity.
Budigalimab, formerly called ABBV-181, is a PD-1 inhibitor currently under development. Unlike nivolumab and pembrolizumab, which are both of the immunoglobulin (Ig)G4 subclass, budigalimab is a humanized, recombinant IgG1 anti-PD-1 monoclonal antibody. It has been modified by point mutations (L234A, L235A) to reduce Fc receptor interactions and limit effector function. Preclinical experiments have demonstrated that budigalimab exhibits potent PD-1-blocking activity with high specificity  [10]  and has an affinity similar to that of nivolumab  [11]  and pembrolizumab  [12]. Dose-finding and preliminary safety data from this first-in-human phase 1 study of budigalimab in patients with solid tumors (NCT03000257) have been previously presented  [13]. The recommended phase 2 dose was determined to be 250 mg every 2 weeks (Q2W), 375 mg Q3W, or 500 mg Q4W, on the basis of pharmacokinetic (PK) modeling and simulations and PK/pharmacodynamic (PD) assessments that indicated these dosing regimens would lead to comparable exposure ranges and produce similar PD activity and a consistent toxicity profile  [14, 15].
Assessments Safety evaluations were performed throughout the study and included assessment of treatment-emergent adverse events (TEAEs) and monitoring of additional clinical data (including vital signs, physical examination, electrocardiograms, echocardiograms, and laboratory test assessments). AEs were graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events v4. 03. The criteria for permanent discontinuation of budigalimab following a TEAE are described in supplementary Table  1. Immune-related AEs were managed per published guidelines  [18] [19] [20]. Intensive serial blood samples for measurement of budigalimab concentrations (PK) in serum were collected in cycles 1 and 3, and additional samples were collected during cycle 2 and cycles ≥ 4. PK parameters were estimated using noncompartmental analysis in Phoenix® WinNonlin® (Certara, Princeton, NJ) and included maximum observed concentration (C max ), time to C max , area under the concentration-time curve, and half-life.
Patient demographics and baseline characteristics Between November 2017 and January 2019, 81 patients were enrolled in the HNSCC (N = 41) and NSCLC (N = 40) expansion cohorts (data cutoff: October 31, 2019). For the HNSCC cohort, the first patient was screened on 4 January 2018, and the last patient on 22 January 2019; for the NSCLC cohort, the first patient was screened on 8 November 2017, and the last patient on 20 December 2018. Baseline demographics and clinical characteristics of both cohorts are summarized in Table  1. Sufficient tumor samples for IHC analysis were obtained from 38 patients with HNSCC and 33 patients with NSCLC; 19 patients in the HNSCC cohort and 16 in the NSCLC cohort were PD-L1 +. There was insufficient tumor tissue for analysis from three patients considered responders per RECIST v1.
Acknowledgements We gratefully acknowledge the developers of iSALE-2D, including  Gareth Collins ,  Kai Wünnemann ,  Dirk Elbeshausen ,  Tom Davison ,  Boris Ivanov  and  Jay Melosh  as well as the pySALEPlot visualization package ( Tom Davison ). K. M. acknowledges funding support from the  Australian Research Council, Curtin Research Fellowship , the  TIGeR institute at Curtin. Early works for this project were conducted at  MIT. P. B. acknowledges funding support from the  Australian Research Council.
1 m (for 125 MHz antenna) and ∆x = 0. 2 m (for 70 MHz antenna). To improve the signal/noise ratio, 8-times stacking was applied during data acquisition. All radargrams presented in the paper were subjected to standard signal processing using ReflexW software  [19] ; a detailed description of radargram processing may be found in literature  [19] [20] [21]. Along profile GPR-2, additional GPR reflection surveys with different antennae orientations (and consequently with different polarizations of electromagnetic wave) were performed; the profiles for such surveys were named: GPR-POL-1. GPR-POL-5 (Figure  2 ). For radargrams recorded along these profiles, more advanced processing was applied-the following procedures were used: signal declipping, phase correlation, 1D median filter, dewowing, DC shift, analysis of polarity, Butterworth time-depended filtration, energy decay analysis, background removal, spectral whitening, 2D average filtration, envelopes (counted from Hilbert transform), morphology filtration.
ERT Surveys An ERT survey was carried out along the profile lines 1 and 2 (Figure  2 ). The total length of the profile lines were 128 m and 110 m, respectively. The ERT surveys were made with the basic electrode spacing ∆x = 2 m, where lengths of current and potential dipoles were a = 1, 2, 5, 9, 14 ∆x and a separation factor n, which is the ratio of the distance between the current and potential dipole, equals n = 1, 2, 3, 4, 5. These parameters resulted in a proper depth and resolution for the necessary investigations  [27]. The dipole-dipole array was used to obtain the best horizontal resolution  [28] [29] [30] [31] [32]. The ERT survey was carried out using Ares II resistivity meter  [33]. The obtained apparent resistivity datasets were inverted using the Res2dinv of Geotomo Software  [34].