dataesr/scientific-paragraphs-categorization huggingface dataset:
SVG-A cells were transfected with either a control, B-Raf-, or C-Raf-specific siRNA, then infected with JCPyV, and infectivity was then scored by indirect immunofluorescence and quantitation of nuclear VP1 expression. Cells treated with B-Raf siRNA demonstrated an ~50% reduction in infectivity in comparison to the control siRNA-treated cells, while C-Raf siRNA-treated cells demonstrated an ~80% reduction in infection (Figure  1A ). Protein knockdown by siRNA treatment was analyzed by In-cell Western (ICW) analysis, which demonstrated that B-Raf expression was reduced by ~60% while C-Raf expression was reduced by ~75%. Together, these data suggest that Raf is necessary for promoting successful viral infection, and that both B-and C-Raf play a role in JCPyV infection (Figure  1 ).
To further define the role of MEK1/2 in JCPyV infection, SVG-A cells were transfected with siRNAs specifically targeting MEK1/2 or a control. Cells were subsequently infected with JCPyV, and infectivity was scored based on the presence of nuclear VP1 expression. At 72 hours postinfection (hpi), cells treated with MEK1/2 siRNA demonstrated an ~80% decrease in infectivity in comparison to control siRNA-treated cells (Figure  2A ). MEK1/2 protein expression was reduced by an ~60% as measured by ICW analysis (Figure  2B, C ). These data suggest that MEK1 and 2 are necessary for viral infection, in correlation with previously published work  [24, 25]. Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 5 of 19 targeting MEK1/2 or a control. Cells were subsequently infected with JCPyV, and infectivity was scored based on the presence of nuclear VP1 expression.
Statistical Analyses Experiments were performed in triplicate containing a minimum of triplicate samples or a minimum of 30 samples for microscopy analysis. Data was analyzed using Matlab and Statistics Toolbox to determine the appropriate statistical test for each experiment. Samples that were normally distributed were analyzed using a two-sample, student's t test assuming either equal unequal variances (when appropriate) to compare the means for at least triplicate samples. p-values < 0. 05 were considered statistically significant. The Anderson-Darling test was used to determine if the populations were normally distributed and variation between populations was determined using the F Test for Equal Variance. The Wilcoxon rank sum test was used in populations that were not normally distributed.
Acknowledgements This work has received support from  AWE plc. , the  Engineering and Physical Sciences Research Council  (grant numbers  EP/M022331/1  and  EP/N014472/1 ) and the  Science and Technology Facilities Council of the United Kingdom. This material is partially based upon work supported by the  U. S. Department of Energy, Of-fice of Science, Office of Fusion Energy Science  under Award Number  DE-SC0019268. c  British Crown Copyright 2018/AWE.
A series of intermediate facade fire propagation tests according to the ISO 13785-1 standard,  25  with additional heat release rate (HRR), and gas analysis using FTIR was addressed in Guillaume et al. 3  The test series comprised nine different combinations of three grades of ACM and three different insulants. Based on the ISO 13785-1 facade fire tests detailed in Guillaume et al, [3] a preliminary numerical study  26  was carried out with the Computational Fluid Dynamics (CFD) code Fire Dynamics Simulator (FDS),  27  developed by the American Institute NIST and widely used in the fire safety community. Thermal characteristics of the system chemical species release. Thus, the numerical model was validated at intermediate scale. The numerical simulations and the tests both showed that the ACM cladding was the most important element driving global fire behaviour of the tested facade systems.
In particular, ACM-PE-based cladding systems, whatever the insulant used in the system, showed very marked fire propagation and the consumption of the ACM, while burning, affected the integrity of the cavity. This paper details the extension of this previously validated numerical model and input dataset from Dréan et al,  26  to evaluate the scaling influence. The previously validated model was implemented on the larger scale of the BS8414-1 test, in order to confirm the input dataset and fix several numerical parameters such mesh size or gradients for future upscaling. The fire test protocol  12  detailed in BS8414-1 is representative of an external fire source, or a fully developed (post flashover) fire in a room, venting through an opening such as a window aperture that exposes the cladding to the effects of external flames.
Indeed, 8 to 9 m of facade are tested at this scale. The propagation of the fire on more than two floors can then be studied. The objective of the present study is to accurately numerically reproduce the thermal load to which the tested system is exposed, the thermal behaviour of the system and the fire propagation via the facade. The simulations are carried out with FDS software, based on the numerical hypothesis and input dataset previously validated. 26 erative calculations are performed to verify the model's consistency with, and relevance to, the experimental results obtained during the reference fire tests referenced  [13] [14] [15]  on various combinations of two grades of ACM and two different insulants, similar to those subsequently tested at intermediate scale. 3  the first step, the BS8414-1 test is numerically modelled, to reproduce the fire test behaviour of a facade system comprising [ACM-PE] cladding and [PIR] insulant.
| Numerical tools The numerical simulations are performed with the CFD code FDS version 6. 7. 0. FDS is a computational code in fluid dynamics that incorporates a combustion model and a large scale model (LES) for the description of turbulent flows. This tool allows 3D modelling of the computational domain. It considers heat transfer at walls, ventilation conditions for the removal of hot gases and air intake. The Navier-Stokes equations are solved in the limit of low Mach number, thermally driven flow with an emphasis on smoke and heat transport from fires. The radiative heat transfer is included in the model through the solution of the radiative transport equation for a grey gas. Detailed information is provided in previous study. 27 e default submodels of FDS were used for the gas phase radiation exchanges even if a sensibility analysis performed with 100 (default value), 500, and 800 solid angles was addressed in Appendix B (see Supporting Information).
7 ± 7. 5% of control, p > 0. 99), normally abolished in TKO cells (22. 8 ± 1. 6%, p < 0. 0001, Fig. 1c, e ). We additionally verified that dyn2-GFP expression levels did not influence A568-Tfn internalisation (Supplementary Fig. 1B ) such that we could pool the data obtained with a given construct and use as a single dataset. Then, we tested dyn2-GFP mutated in the C-terminal portion of the PRD according to our hypothesis. We had noted that this domain contains three class II SH3 binding motifs. We labelled them A, B and C such that motif B is part of the known D15 peptide while motifs A and C flank motif B as illustrated in Fig. 1d. Of note, all dynamin mutants tested thereafter were expressed in TKO cells at similar levels (Supplementary Fig. 1C ). As expected  6  , neither reexpression of dyn2-GFP lacking the whole PRD domain (dyn2-GFP-ΔPRD) nor the D15-containing C-terminal domain (dyn2-GFP-ΔCter) could rescue endocytosis in those cells (Fig.
Data were acquired in a datadependent mode. Mass spectrometry (MS) scans (m/z 300-1700) were recorded at a resolution of R = 70,000 (@ m/z 400) and an AGC target of 5 × 10 5 ions collected within 500 ms. Dynamic exclusion was et to 30 s and top 6 ions were selected from fragmentation in CID mode. MS/MS scans with a target value of 1 × 10 4 ions were collected in the ion trap with a maximum fill time of 200 ms. Additionally, only +2 and +3 charged ions were selected for fragmentation. Others settings were as follows: no sheath nor auxiliary gas flow, heated capillary temperature, 200 °C; normalized CID collision energy of 35% and an isolation width of 3 m/z. Data were searched by SEQUEST through Proteome Discoverer 1. 4 (Thermo Fisher Scientific Inc. ) against a subset of the 2018. 01 version of UniProt database restricted to Rattus norvegicus Reference Proteome Set (29,961 entries).
05. Noticeably, only non-conflicting features and unique peptides were considered for calculation at protein level. Quantitative data were considered for proteins quantified by a minimum of two peptides. SH3 domain production. SUMO-amphSH3 and GST-amphSH3 were generated by subcloning murine Ampiphysin1 SH3 domain residues 607-686) from pAmph1-mCherry (kind gift from C. Merrifield, Addgene 27692) using BamHI and XhoI restriction sites into a modified pET-SUMO bacterial expression vector (Life Technologies) incorporating a TEV cleavage site and a multiple cloning site following the SUMO tag or a modified pGEX-4T-2 vector incorporating a TEV cleavage site before the gene of interest, using the following primers: 5′-ggatccactcaggaactgcctcctggc-3′ (forward) and 5′-ctcgagttactccaggcgccgcgtgaagtt-3′ (reverse).
Following 16 washes with PT, 100 µL of horseradish-peroxidase-conjugated streptavidin (Jackson ImmunoReaserch) diluted at 1:3000 in PT) was added to each well. Samples were incubated on a rocking shaker for 30 min, and were then washed 6 times with PT and 4 times with PBS. Next, 100 µL of 1-Step Ultra (3,3′,5,5′-tetramethylbenzidine) TMB-ELISA was added and the reaction was stopped with 100 µL 1 M H 3 PO 4. Absorbance was read at 450 nm on a Multiskan GO plate reader and corrected for absorbance at 800 nm as well as for no-specific binding of the peptides by subtracting the response in absence of SH3 domain. Data from independent run were normalized to the maximum values of the D54-dyn2 peptide and fitted using the One site binding hyperbola formula from GraphPad Prism v7. 02. SPR measures and analysis.
The capture of streptavidin and regeneration of the functionalised surface was achieved following the manufacturer's recommended protocols. Sensorgrams were double-referenced using Biacore evaluation software (Biacore). No points were removed prior to data fitting. Spikes still present after the doublereferencing process had no influence on the analysis. The kinetic data were analysed using a 1:1 Langmuir binding model of the BIAevaluation software with the bulk refraction index (RI) kept constant and equal to 0 and the mass transfer constant (tc) kept constant and equal to 10 8. In the case of fast association and dissociation rate constants that prevented reliable kinetics analysis, the curves were thus exploited using equilibrium analysis to obtain the dissociation equilibrium constants in GraphPad Prism (GraphPad Software v7.
02 for Windows) by fitting the data with the one-site binding hyperbola formula Y ¼ B max Ã X K D þ X ð1Þ where Y is the response (in RU), B max is the response value at maximal binding, K D is the affinity constant to be determined and X is the protein concentration. Dynamin2 mutant design. Dynamin 2 (human dynamin2bb according to the nomenclature of ref. 56  ) plasmids were based on the dyn2-mCherry construct from Taylor et al. 2011 (Addgene 27689). We first generated dyn2-GFP by sub-cloning dyn2 into pEGFP-N1 vector with BglII/EcoRI sites. To generate GFP-dyn2, we amplified its ORF by PCR with the following primers: tcgtaacaactccgcccc (forward) ggcctcgagtcagtcgagcagggatggctc (reverse) and sub-cloned it into pEGFP-C1 with BglII/XhoI sites. We then generated dyn2-GFP and dyn2-mCherry constructs in which the GFP/mCherry tag is relocated before the PRD (residue 740) with linkers composed of six residues (GTSGSS).
Samples were excited in TIRF illumination (Olympus IX2RFAEVA illuminator) with a 473 nm laser source (Cobolt). Emitted fluorescence was filtered with a 525/50 m filter (Chroma Technology). Images were acquired by an electron multiplying charge coupled device camera (EMCCD QuantEM:512SC, Roper Scientific) controlled by the software MetaVue 7. 8 (Molecular Devices). Two-colour imaging was performed with a coaligned 561 nm laser (Cobolt) with the same TIRF angle as the 473 nm laser. Emitted fluorescence was filtered with a 605/52 m filter (Chroma Technology). Red and green fluorescence images were obtained 200 ms apart using a filter wheel (Lambda 10-3, Sutter Instruments). To correct for x/y distortions between the two channels, images of fluorescently labelled beads (Tetraspeck, 0. 2 μm; Invitrogen) were taken before each experiment and used to align the two channels.
The solution contained (in mM): 130-140 KCH 3 SO 3 , 1 EGTA, 0. 1 CaCl 2 , 10-20 HEPES, 4 ATP (sodium or magnesium salt), 0. 4 Na-GTP (or GTPγS when indicated), 5 phosphocreatine and 3 sodium ascorbate. pH was adjusted to 7. 2 using KOH and osmolarity to ~300 mOsm L -1. Sodium ascorbate was omitted in earlier recordings but was added for better stability. Chloride ions either came from 2 mM KCl, 10 mM NaCl or 4 mM MgCl 2 (when Na-ATP was used) in various trials. All salts were purchased from Sigma-Aldrich. Cells were voltage-clamped at -60 mV with an EPC10 amplifier (HEKA) with series resistance and cell capacitance estimation every 4 s in the whole cell mode. The digital triggers of the amplifier were used to drive the electrovalves for pH changes. Cell viability and dialysis efficiency were made sure of by monitoring three parameters throughout the recording using the Patchmaster software (HEKA): series resistance should remain below 10 MΩ and holding current should remain above -100 pA.
Objects that could be tracked for more than three frames (6 s) were retained for further analysis with scripts written in Matlab 2017. The fluorescence value at each time point represents the average intensity in a 2 pixel (300 nm) radius circle around the centre of mass of the object to which the local background intensity is subtracted. This local background is estimated in an annulus (5 pixels outer radius, 2 pixels inner radius) centred on the region to be quantified as the average intensity of pixel values between the 20th and 80th percentiles (to exclude neighbouring fluorescent objects). The frame of maximum intensity is used as the reference (time 0) for aligning and averaging all traces. Before (resp. after) tracking of an object the fluorescence is measured at the location of the first (resp.
last) frame with the object tracked. Two colour alignment was performed using an image of beads (TetraSpeck microspheres, ThermoFisher T7280) taken before the experiment. The detection, tracking, and quantification of endocytic events was performed using scripts developed previously and updated in Matlab 2018a (Mathworks)  4. Images were sorted according to the pH (7. 4 or 5. 5) into two movies. These two movies were segmented using MIA with fixed optimized parameters. Objects appearing in pH 5. 5 images were then considered candidate CCVs if they were visible for more than three frames (8-12 s), if their signal to noise ratio was high enough, and if a cluster pre-existed where they appeared for at least five frames (20-24 s) in pH 7. 4 images (Supplementary Fig. 2B ). Importantly, this automated method of detection had been characterised as missing ∼30% of all events but with a low percentage of false positives (∼20%)  4, 28  , making it ideal for comparative studies over large datasets.
In rescue experiments in dynamin TKO cells all events were reviewed by eye. For the experiments of peptide dialysis, to handle the large dataset without reviewing all the events by eye, we developed a Support Vector Machine (SVM) type neural network by supervised learning. We used a dataset of 26 recordings obtained in 6 experimental sessions with 9631 candidate events reviewed by two different operators (MR and DP), leading to 7763 bona fide endocytic events. This gives 19. 1 ± 0. 1% of false positives, a percentage virtually identical to previous estimates  4, 28. The inputs of the SVM were sets of 15 × 15 pixel images centred on the candidate event, 5 frames before and 6 frames after the candidate event is detected, i. e. , 15 × 15 × 11 = 2475 input values (pixel values) per candidate event.
We used the maximal projection of the z-stack for quantification. For each cell, we traced around the dyn-GFP image (when applicable) a mask of the cell, as well as a background region with no cell. In non-transfected cells the mask was traced around the Tfn-A568 image. We detected Tfn-A568 clusters by wavelet segmentation using custom written Matlab programs. For each cell we computed the average fluorescence, the number of clusters and the cell surface. These numbers were normalized to 100 for the control condition (no OHT induction) for each of four independent internalization assays. The distribution of dyn-GFP was scored for each cell as entirely punctuate (1), punctuate on a homogenous background (2), mostly homogenous with some puncta (3), or entirely homogenous (4) (see Fig. 1f  for illustrations).
All data processing and scoring was performed blind. β adrenergic receptor internalization assay. Two days after transfection, cells were pre-treated (20 min, 37 °C) with either DMSO, Dyngo4a (30 μM, Abcam), myrD15 (10 μM, Tocris) or myrCtrl (10 μM, Tocris), incubated live with FLAG M1 antibody (Sigma F3040, dilution 1/200) (10 min, 37 °C), stimulated with Isoproterenol (10 μM, 10 min, 37 °C), rinsed 3 times with PBS + Ca 2+ and fixed with paraformaldehyde (4% in PBS, 15 min, 37 °C). Fixed cells were incubated in blocking buffer (2% BSA in PBS + Ca 2+ ) for 1 h at RT, permeabilized (0. 02% Triton-X100 in blocking buffer, 15 min, RT) and incubated with AlexaFluor555 anti-mouse secondary antibody (Thermo Fisher A31570, 1/1000) in the dark for 1 h at RT. Image acquisition and analysis was conducted following the same procedure as for Transferrin uptake assay and quantification described above.
Acknowledgements We thank  Christelle Breillat ,  Célia Michel  and  Natacha Retailleau  ( IINS ) for help with plasmid constructs,  Christopher Borcuk  for help with Matlab programming,  Pascal Desbarats  ( LaBRI, Talence ) for counselling on the SVM for sorting scission events,  Sébastien Marais  ( Bordeaux Imaging Center , part of the  France BioImaging national infrastructure   ANR-10-INBS-04 ) for help with the spinning disk confocal microscope. We also thank the  Biochemistry and Biophysics Core Facility of the Bordeaux Neurocampus  funded by the  Labex BRAIN  ( ANR-10-LABX-43 ) and  J. M. Blanc  and  Y. Ruffin  for technical assistance as well as the  Structural Biophysico-Chemistry platform  ( UMS3033/US001 ) of the  Institut Européen de Chimie et Biologie (Pessac, France ) for access to the  T200 Biacore instrument  and to  Laetitia Minder  for technical assistance.
We thank  Pietro De Camilli  ( Yale University ) and  David Drubin  ( UC  for the gift of cell lines, and  Aylin Hanyaloglu  ( Imperial College London ) for plasmids. We thank  Volker Haucke  ( FMP, Berlin ) for critically reading the manuscript. This work was supported by the  Centre National de la Recherche Scientifique  ( Interface program ), the  Fondation Recherche Médicale  ( FRM ING20101221208 ), the  Agence Nationale pour la Recherche (CaPeBlE   ANR-12-BSV5-005  and LocalEndoProbes  ANR-17-CE16-0012 ) to D. P. , the  FRM , a  pre-doctoral fellowship  from the  University of Bordeaux  and a  Labex BRAIN  fellowship to M. R. and the  European Research Council  (advanced grants ADOS and  Dyn-Syn-Mem ) to D. C.
Data availability Data supporting the findings of this manuscript are available from the corresponding authors upon reasonable request. A reporting summary for this Article is available as a Supplementary Information file. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  58  partner repository with the dataset identifier PXD015292. The source data underlying Figs. 1e, f, 2b-d, g, 4b, e, 5d and Supplementary Figs. 1, 2 and 6 are provided as a Source Data file.
Patients, demography Isolates from a total of 630 patients living in Kazakhstan (435 men, 195 female) were included in the study. Patient data were only available for patients recruited in the Kazakh National Tuberculosis Reference Laboratory. New cases of tuberculosis accounted for 35. 6% of the cases (224 of 630) and 64. 4% (n = 406) were previously treated patients. Among 630 patients with pulmonary tuberculosis 69% (n = 435) were men and 23% (n = 145) were women, 50 (8%) were unknown. The age of TB patients varied from 16 to 80 years, with an average age of 37 years.
Tb-SNPID TB-SNPID was done as previously described  [6]  using semi-automated data analysis based on a threshold value as published in  [8]. Readings were performed on a Mag-Pix® using the MagPlex xTAG® microspheres and reagents provided by MRC Holland (Amsterdam, The Netherlands). DNA samples were first diluted 1:3 in TE containing RNase A (Roche Biochemicals, Pensberg, Germany). Each sample was run in a single well; no samples were repeated. A total of 543 samples were run, however 73 did not pass through the quality check. Hence the final results analysis was performed on 470 samples. Full results are available in (Additional file 1: Table  S2 ).
The total number of isolates identified as non-L2 by the two methods was around 22% (96/ 470 = 20. 4% by TB-SNPID and 24. 2% =164/677 by TB-SPRINT). The most prevalent L4 sublineage was L4. 3/LAM (n = 51), that includes the so-called LAM-RUS sublineage (previously T1 or T5-RUS2 in spoligotyping-classification)  [31, 32, 35] ; the second non-L2 most prevalent lineage was the L4. 2. 1/URAL genotype  [36]  (n = 39, labeled as "Euro-American Other" by TB-SNPID). Five isolates were labeled as "Haarlem" (L4. 1. 2) by TB-SNPID. By spoligotyping, twelve spoligotypes not reported in the SITVIT database were found, among which 4 were also identified as bona fide L4 isolates by TB-SNPID (others: ND). At least three of these non reported spoligotyping patterns were likely linked to represent mixed infection.
Acknowledgements We are grateful to the  MoH of Kazakhstan , to  Dr. Y. M. Ramanculov ,  Kazakhstan NCB  head, for allowing this project to be run, and to Beamedex® ( P. Cutolo ,  M. K. Gomgnimbou ,  C. Ripoll ) and  Luminex BV  staff in the  Netherlands  ( J. van Gils ,  E. Benning ) for technical support. Hamilton Corporation (Hamilton France ,  Les Ulis ,  L. Loridon  and  J. Vicenzi ) is also acknowledged for technical support. Three reviewers helped to improve the paper and are warmly acknowledged.
Funding This work was supported by the  Ministry of Research in Kazakhstan  through the project: "High-throughput and cost-effective techniques for control and surveillance of infectious diseases in Kazakhstan: the multi-drug and extremely -drug resistance tuberculosis problem" (Priority area: «Life Science» «Development of biomedicine  and genetic engineering ») Time  frame of the project: January 2015 -December 2017, three years and Grant n°A P05130238. BK was a PhD student from Beamedex® supported by the  French Agence Nationale de la Recherche et Technologie-Contrat Individualisé de Formation par la Recherche en Entreprise  ( ANRT-CIFRE n°2015-1177  and  CNRS-Beamedex  contract  n°144729 ). CS is grateful to the  CNRS and Institute for Integrative Cell Biology ,  UMR9198 , for financial support including the purchase and maintenance of the Hamilton StarLet automatized liquid handling system.