dataesr/scientific-paragraphs-categorization huggingface dataset:
They also performed all planned procedures of visit four (V4), collected clinical data and questionnaires at the beginning of visit five (V5) and had exclusive access to Case Report Forms or laboratory results during that phase. Participants were also instructed to exclusively contact unblinded research staff in case of any change in their health status during the study. Except the measure of weight at V4, unblinded research staffs were otherwise not involved in measuring outcomes and had no contact with the investigators. To achieve masking of the participant, the infusion bags were covered with non-translucent bags, opaque infusion lines were used and a curtain shielded the infusion arm from the participant. All study staff performing laboratory measurements were blinded to treatment allocation.
Statistics Data was analysed on an intention-to-treat approach. Continuous data derived from clamp studies at baseline (D 0 ) and at day 28 (D 28 ) were analysed as follows: first, individual differences D 28 -D 0 were computed, then compared between groups (iron and placebo) using student's t-test. Continuous data collected at D 0 , D 14 and D 28 were compared between groups using a mixed model for repeated measures. The Stata syntax for the mixed model was the following: mixed outcome i. group time i. group#c. time || ID: time, reml Where group identifies the group (iron or placebo), time identifies the assessment period, and ID corresponds to the participant's identification code. Serum ascorbate levels, serum ascrobyl/ascorbate ratio and serum antioxidant status were normalized for baseline values.
Thirdly, we accounted for circadian regulation of glucose homeostasis by starting all metabolic investigations at 6:30 AM. Fourthly, we controlled for differences in caloric intake before the metabolic investigation by providing a standardized meal, the evening before the test. Our study has also some limitations: while diabetes is believed to be a late complication of HH in humans, the restoration of iron sufficiency might not mimic the time/ dose effect of more prolonged absolute iron overload and as such, not fully recapitulate the physiopathology of the disease. Besides, the sample size was small and the study was potentially underpowered to assess differences among groups in the chosen outcome, especially at the second more variable glycaemic step of the clamp also given, that data was missing for 3 out of 16 s steps for reasons  previously reported.
Acknowledgments The authors would like to thank the participants of the study,  Christiane Pellet ,  Philippe Schneiter ,  Nathalie Stefanoni ,  Valentine Rey-Roethlisberger ,  Julia Lacheze  for technical assistance. They would also like to thank  Aline Voidey  and  Isabelle Sommer  at the pharmacy department of the  Lausanne University Hospital  for ensuring the preparation and masking of the study drug.
van der Werf. The human sample from which strain BetaCoV/France/IDF0372/2020 was isolated has been provided by X. Lescure and Y. Yazdanpanah from the Bichat Hospital, Paris, France. Moreover, the strain BetaCoV/ France/IDF0372/2020 was supplied through the European Virus Archive goes Global (Evag) platform, a project that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 653316.
After the immunostaining, cells were stained with the HCS CellMask Blue Stain (1:300; Invitrogen) in 1× PBS for 30 min and then mounted. Images were acquired on an LSM 700 confocal microscope (Zeiss) with a 40× objective. After image acquisition, the number of acceptor cells, which had received SARS-CoV-2, identified by the anti-N and/or anti-S immunostaining was quantified. Briefly, after image acquisition, the number of acceptor cells, which had received SARS-CoV-2, identified by the anti-N and/or anti-S immunostaining was semiautomatedly quantified with the open-source software Icy (http:// icy. bioimageanalysis. org/). To evaluate the possibility of SARS-CoV-2 transfer from donor to acceptor cells mediated by secretion, the supernatants from SARS-CoV-2-infected Vero E6 cells were collected, centrifuged at 1000 rpm for 10 min to remove floating cells, and added on acceptor cells: SH-SY5Y mCherry.
The cocultures were incubated at 37°C for 24 and 48 hours at 5% CO 2. Then, cocultures were fixed in 4% PFA for 30 min and immunostained for the anti-N (protocol described above) and with the HCS CellMask Blue Stain (1:300; Invitrogen) for 30 min. Images were acquired on an LSM 700 confocal microscope (Zeiss) with a 40× objective. After image acquisition, the number of acceptor cells, which had received SARS-CoV-2, identified by the anti-N immunostaining was quantified by the Icy software as before. In parallel, the supernatant of each condition was then collected to assess viral neutralization using focus-forming assay titration protocol. For the secretion test, performed in parallel with the coculture, an aliquot of the supernatant from the donor was incubated with anti-SARS-CoV-2 IgG C3 235 (10 g/ml) for 1 hour at 37°C, to neutralize the viral particles, present in the supernatant.
science. org at Institut Pasteur on December 29, 2022 14 of 17 E6 donor cells were placed in coculture with SH-SY5Y mCherry acceptors pretreated at 37°C for 1 hour at 5% CO 2. The cocultures were maintained in the presence of the inhibitor. After 48 hours, cocultures were fixed and immunostained with anti-S and anti-dsRNA J2 antibodies to detect SARS-CoV-2 particles. For the secretion test, SH-SY5mCherry and Vero E6 mCherry cells were preincubated or not with the remdesivir for 1 hour and then were maintained in the presence of the inhibitor and challenged with the supernatant of infected cells for 48 hours. Images were acquired on an LSM 700 confocal microscope (Zeiss) with a 40× objective. After image acquisition, the number of acceptor cells, which had received SARS-CoV-2, identified by anti-S and J2 immunostaining, was quantified using Icy software as before.
The whole cellular volume was imaged by acquiring 0. 45-m z stacks with an inverted confocal microscope (Zeiss LSM 700) using ZEN software. TNT-connected cells, connected by straight WGA-labeled structures that do not touch the substrate, were manually counted by Icy software using the semiautomatized TNT counting tool as previously described  (53, 78). The 3D renderings of TNTs were performed using Imaris software.
Cryo-ET data acquisition and tomogram reconstruction The cryo-EM data were collected from different grids at the Nanoimaging Core Facility of the Institut Pasteur using a Thermo Fisher Scientific 300-kV Titan Krios G3 cryo-TEMs equipped with a Gatan energy filter bioquantum/K3. Cryo-ET software from Thermo Fisher Scientific was used to acquire the data. Tomograms were acquired using a dose-symmetric tilt scheme (79); a ±60° tilt range with a tilt step 2 was used to acquire the tilt series. Tilt images were acquired in counting mode with a calibrated physical pixel size of 3. 2 Å and total dose over the full tilt series of 3. 295 e -/Å 2 and dose rate of 39,739 e -per pixel per second with an exposure time of 1 s. The defocus applied was in a range of -3-to -6-m defocus. The tomogram showed in Fig.
8D  was performed on Thermo Fisher Scientific Glacios 200-kV cryo-TEM equipped with Falcon 3 direct electron detectors. Tilt series were recorded using Cryo-ET software (Thermo Fisher Scientific) in counting mode and an angular range of -60° to +60°, with a calibrated physical pixel size of 3. 2 Å and a total dose over the full tilt series of 3. 49 e -/Å 2 and dose rate of 42. 16 e -per pixel per second and 3. 49 e -/Å 2 with 1-s exposure time and 70-m objective apertures. The defocus applied was in a range of -3-m defocus. The tomograms were reconstructed using IMOD (eTomo). Final alignments were done using 10-nm fiducial gold particles coated with BSA (BSA Gold Tracer, Electron Microscopy Sciences). Gold beads were manually selected and automatically tracked. The fiducial model was corrected in all cases where the automatic tracking failed.
Statistical analysis All column graphs and statistical analysis were performed using the GraphPad Prism version 7 software. Unpaired t test was applied for comparisons of two conditions presented inFigs. 1 and 5. For more than two groups, statistical significance was assessed by a one-way analysis of variance (ANOVA) with Tukey correction in Fig. 4C. Quantifications were done blind. Quantitative data were depicted as (±SEM) mean SD. The graph in Fig. 1H  shows the percentage of N transfer in coculture at 24 and 48 hours. The mean percentage of N transfer in coculture at 24 and 48 hours is 36. 47% ± 3. 96 and 62. 56% ± 8. 28, respectively (*P = 0. 0468 for coculture at 48 hours versus coculture at 24 hours; n = 3). The graph in Fig. 1L  shows the percentage of S transfer in coculture at 24 and 48 hours.
The TNTs positive for anti-nsp3 puncta have been counted manually using Fiji software. The graph in fig. S5C  shows the percentage of S transfer in 48-hour coculture (control and treated with the inhibitor). The mean percentage of S transfer in coculture control is 46. 2% ± 1. 7, and that of S transfer in coculture plus inhibitor is 15. 3% ± 2. 1 (**P = 0. 0075 for coculture plus inhibitor versus coculture control). The mean percentage of J2 transfer in coculture control is 43. 2 ± 2. 6, and that of J2 transfer in coculture plus inhibitor is 17. 2% ± 0. 2 (*P = 0. 0101 for coculture plus inhibitor versus coculture control). The graph in fig. S7C  shows the percentage of TNT-connected cells between noninfected Vero E6 and SH-SY5Y cells and TNT-connected cells between noninfected Vero E6 and SH-SY5Y cells.
Acknowledgments: We thank all the laboratory members for useful discussion, particularly  M. Samarani ,  D. Cordero Cervantes ,  C. Brou ,  M. Henderson ,  H. Khare , and  L. Sudarsono. We are grateful to  R. Bouyssie , a member of the administrative staff of the  Membrane Traffic and Pathogenesis department at Institut Pasteur. The NanoImaging Core at Institut Pasteur  is acknowledged for support with image acquisition and analysis, particularly  J. -M. Winter  ( NanoImaging Core at Institut Pasteur ). We also acknowledge  A. Sartori -Ruopp (Ultrapole, Institut Pasteur), G. Péhau-Arnaudet (Ultrapole, Institut Pasteur), and S. Tachon (NanoImaging Core at Institut Pasteur). We also thank  N. Escriou  ( Institut Pasteur ) for the primary antibody SARS-CoV anti- N IgG  and  C. Planchais  from the group of  Hugo Mouquet (Institut Pasteur)  for the human SARS-CoV-2 anti- S IgG  and neutralizing antibody ( C3-alpha 235 ).
Funding: This work was supported by the " URGENCE COVID-19 " fundraising campaign of  Institut Pasteur (Paris)  to C. Z. , G. B. -S. and M. V. , and from  Equipe Fondation Recherche Médicale  ( FRM-EQU202103012692 ), and  Agence Nationale de la Recherche  ( ANR-20-CE13-0032 ) to C. Z. The NanoImaging Core of  Institut Pasteur  was created with the help of a grant from the  French government 's  Investissements d'Avenir program  ( EQUIPEX CACSICE-Centre d'analyse de systèmes complexes dans les environnements complexes ,  ANR-11-EQPX-0008 ). We are grateful to late  M. Michel , whose bequest to  Institut Pasteur  has made this project possible.
Study Population and Data Collection The study was conducted in unselected adult patients with NSCLC and EGFRactivating mutations treated with osimertinib. Patients were consecutively included between October 2017 and April 2022. Patients were followed in the Cochin University Hospital (Paris, France), Georges Pompidou European Hospital (Paris, France) or Tenon Hospital (Paris, France). The recommended starting dose was 80 mg once daily. The starting dose was reduced in patients for whom a high risk of toxicity was identified at initial clinical assessment. Subsequently, doses could be adjusted based on efficacy and safety. Demographic and biological data, co-medications and clinical events (toxicity, disease progression or death) were retrospectively collected from medical records.
Parameter Estimation and Model Selection The log-likelihood ratio test, based on the reduction of the objective function value (∆OFV), was used to discriminate between hierarchical models. A decrease in OFV of at least 3. 84 (p < 0. 05) and an increase of at least 6. 63 (p < 0. 01) points were considered statistically significant for one additional parameter in the model-building process or forward insertion and backward deletion covariate steps, respectively. The validation of the final PK model was performed using a visual predictive check (VPC) based on 500 simulations of the original dataset.
Pharmacokinetic Analysis for Erlotinib The results of the exposure-response relationship for osimertinib led us to explore whether this result was specific to the drug or to the EGFR-TKI class. For this purpose, we investigated the exposure-survival relationship in a cohort of NSCLC patients treated with erlotinib. Patients were consecutively included between August 2010 and December 2017. The erlotinib cohort was previously described  [23]. A subgroup of these patients for whom efficacy data were available was included in the current study to evaluate the exposure-response relationship for survival. Erlotinib individual PK parameters (EBEs) were obtained using population PK model developed in a cohort investigated by Cardoso et al. [23]. C min,ss were estimated for each patient at each sampling occasion based on EBEs.
Osimertinib Population PK Analysis The population PK analysis included a total of 420 plasma osimertinib concentrations. A median of three samples per patient (range 1-28) was collected between 0. 33 and 27 h (median = 18 h) after the last dose intake. The median time from treatment start to blood collection was 115 days (IQR: 40-281). Steady-state osimertinib concentration-time data were described by a one-compartment model with first-order absorption and elimination. Since few PK data were available in the absorption phase, first-order absorption rate constant (k a ) was fixed to 0. 24 h -1 according to a previously published model  [8]  to allow an adequate estimation of all PK parameters. A two-compartment model was not associated with a significant improvement (∆OFV = -1. 58, p = 0. 21).
In addition, sarcopenia could explain, in part, the association between increased plasma drug exposure and worse survival. Indeed, sarcopenia can enhance the plasma drug exposure in cancer patients treated with TKIs  [44, 45]. In addition, although the data are contradictory, sarcopenia in NSCLC patients treated with EGFR-TKIs has been identified as a risk factor of poor prognosis and shorter survival  [46] [47] [48]. In the present study, sarcopenia status was not available. In this context, it deserves to be investigated in the future PK/PD studies for EGFR-TKIs. This study has several limitations. First, it included a low number of patients in both cohorts and the clinical data were retrospectively collected from medical records. Nevertheless, the median PFS and OS were coherent with those previously reported in clinical trials  [4].
Biosensors were regenerated using three cycles of alternating wash for 10 s in Glycine 10 mM pH 2. 5 and in TBS. All the steps were run at 30 • C with a continuous shake speed of 1000 RPM. For each set of measurement, one biosensor exposed to the 0 nM concentration was used as a background reference. Sensorgrams were processed using a single reference subtraction and analyzed using the Octet Data Analysis software HT 11. 1 (FortéBio). Fitting was performed with a 1:1 binding fit model.
Binding kinetic of the Exenatide-ABNF fusion on HSA and MSA at pH 7. 4 and 6 Experiments were performed by biolayer interferometry (Octet Qke, Fortébio). 100 nM of biotinylated albumin were loaded to streptavidin biosensors. Binding curves were generated by the association (300 s)/ dissociation (1800 s) of fusion proteins diluted over a range of concentrations from 1. 5 to 100 nM in assay diluent. For experiments at pH 7. 4 and pH 6, assay diluent was respectively HBS-EP pH 7. 4 supplemented with 0. 05% Tween20 and PBS 1x pH 6. 0. Before each measurement, a baseline was measured, which was subtracted from the binding curve. Affinity (KD) was calculated by fitting on-rate and off-rate curves using the Octet software.
In vitro functional GLP1R cell based assay Fusion proteins were tested in agonist mode in the classical in vitro GLP1R cell based assay based on cAMP measurements (DiscoverX). Samples were assayed in duplicate over a ten concentration points using three fold serial dilution of a solution at 50 nM. Each sample was tested in presence and absence of six molar equivalent of MSA, to investigate its influence on the dose response. Data was normalized to the maximal and minimal response observed in the presence of control ligand (Exendin-4) and vehicle. EC50 was determined by interpolation of the fitted data using GraphPad Prism V6. 01.
Plasma concentration of fusion proteins were determined by ELISA. The assay was based on the capture of Exenatide or Exenatide-ABNF by a polyclonal rabbit anti-Exenatide antibody and its recognition by a monoclonal mouse anti-Exenatide antibody (kit ABBiolabs, ref-SEK 0130-01). Measurements were made by the reading of TMB oxidized product (visible detection at 450 nm / correction at 620 nm) within 30 min. Raw data (OD) measured by the plate reader were plotted against nominal standard concentrations deduced from the respective standard calibration curves established for each of the different Exenatide-ABNF contructs. Half-life (t1/2 z), AUC and CL were determined. For the oral glucose tolerance test (OGTT), two of the Exenatide-ABNF fusions were investigated (Exenatide-12aa-ABNF and Exenatide-Exenatide-4aa-ABNF) in a side by side comparison with Exenatide and vehicule only (PBS).
At day 1, the test items (200 nmol/kg) were administered to the mice by i. v. infusion right after a measurement of their blood glucose level. Fifteen minutes after the infusion, an oral glucose load of 2 g/kg (40 mL/kg) was given and the blood glucose levels were measured at 0. 25, 0. 5, 1, 2, and 4 h after the glucose administration. At days 2 and 3, the baseline glucose level was first measured, corresponding at 24 h and 48 h after the infusion of the test items, and 15 min after mice were challenged with an oral glucose load again. Blood glucose levels were measured at 1, 2, and 4 h after glucose challenge. Blood glucose levels (mg/dl) were determined using a glucometer (Accu-Chek Performa). One whole blood drop taken in the saphenous vein of mice was absorbed by a test trip. Data was entered in Provantis software after conversion in mmol/l.
In vitro binding and biological activity of Exenatide-ABNF fusions Functionality conservation of the ABNF when fused to Exenatide was investigated by biolayer interferometry. We evaluated the binding kinetic of the two constructions involving the shortest and longest linker length, respectively Exenatide-4aa-ABNF and Exenatide-Exenatide-4aa-ABNF. The measurements were performed both at pH 7. 4 and pH 6 and compared to the data obtained for the ABNF alone in the same experimental conditions. Overall, the two chimeric constructions displayed similar affinities than the isolated ABNF on both serum albumins (Fig. 4 ), suggesting that the fusion do not impair the functionality of the Nanofitin. Small deviations are observed on the association rate, which appears to be slowed down when the ABNF is fused to Exenatide.
Acknowledgments We thank  Justine Picot  and  Anaëlle Perrocheau  for their contribution to the generation of the ABNF. We thank  Harmony Gorré  for her contribution to the characterization of the ABNF. We thank  Dr. Simon Huet  and  Caroline Roze  for their contribution to the definition of the HSA domain targeted by the ABNF. We thank  Isabelle Coquery  and  Francis Duffieux  for their contribution to the expression of the ABNF and  Coralie Werbrouk  for her contribution to the purification. We thank  Christine Colonna  and  Anne Severac  for their contribution to the ABNF quality control. We thank  Thomas Bertrand  and  Alexey Rak  for their contribution to the thermal denaturation. We thank  Jean-Dominique Guitton  who sponsor the project. We thank  Mélanie Roy  who performed the biological assays of the dosed animal samples.
In this paper, we have chosen to continue the characterization of poto-poto already started in the previous paper by also addressing its acoustic characteristics. Indeed, the effects of climate change are manifested by abnormal variations in temperature against seasons. Hence the urgency to master the physical properties in order to make the most of it both in terms of acoustic implementation and/or thermal insulation as regards its use as a green and modern construction material. In this work, we have, on the basis of the Biot model, carried out a 2D numerical modelling of a poto-poto wall by coupling finite elements method (FEM) and boundary elements method (BEM). The MATLAB software was used to obtain the sound absorption curves of our model. We then made several specimens of our eco-material (3 cm, 4 cm and 5 cm) dosed with 2%, 4% and 6% of bamboo fibers, on which Kundt tube measurements were performed to determine the experimental sound absorption curves.
Bioinformatics The amino acid sequences of the SARS-CoV-2 variants were taken from the NCBI nonredundant database (accessed on December 2021): accession BCN86353. All SARS-CoV-2 variants were modeled with YASARA  [43] , using the atomic coordinates of SARS-CoV-2 β in the closed state (PDB code 6VXX)  [44]  as template. The surface electrostatic potential of the RBDs of the S protein from the Wuhan strain and other variants were calculated with Chimera-X  [45] , using the Amber20 forcefield and the classical parameters for the dielectric constant (4), and -10, -3 and 5 APBS color values for electropositively charged (blue), neutral (white) and electronegatively charged (red) regions, respectively  [46]. Molecular structures and models, and the distribution of Coulombic electrostatic potentials on their molecular surfaces were drawn with Chimera-X.
Surface electrostatic potentials were also calculated with the ElectroStatic Potential by Paticle Mesh Ewald (ESPPME) method  [47]  with YASARA, using the Amber96 forcefield and a grid resolution of 5. 0 Å. Electropositively and electronegatively charged areas distributed on the molecular surface of the SARS-CoV-2 variants are colored blue and red, respectively. Neutral surfaces are white. High-mannose and complex N-glycans identified on the SARS-CoV-2 S protein  [48, 49]  were modeled using the GlyProt server (http://www. glycosciences. de/modeling/glyprot/ php/main. php, accessed on 10 January 2022)  [50] , and represented by blue spheres on the molecular surface of the spikes of the different SARS-CoV-2 variants. The atomic coordinates of the S protein of homotrimeric spike from SARS-CoV-2 Wuhan-Hu-1 strain (GenBank ID: MN908947.
3) (PDB code 7DF4)  [3] , SARS-CoV-2 variant B. 1. 1. 17 (α), in complex with ACE2 (PDB code 7FEM)  [4] , SARS-CoV-2 variant B. 1. 617. 2 (δ) in complex with ACE2 (PDB code 7WBQ)  [51] , and SARS-CoV-2 variant B. 1. 1. 529 (o) in complex with ACE2 (PDB code 7 WBP)  [51]  were taken from the PDB.