dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, the KEGG pathways clusters from these 214 nucleic acid binding proteins correspond to almost the same biological processes defined by the complete set of G4-interacting proteins (Supplementary Figure  S4 ). An additional analysis of nucleic acid binding proteins enriched on constrained G4 structures defines five functional groups covering (i) ATP dependent DNA/RNA helicases activities, (ii) hnRNP proteins, (iii) proteins involved in the polyadenylation process, (iv) a large group of proteins involved in splicing and (v) proteins related to small nuclear ribonucleoprotein complexes (Table  1 ). Next, using UniprotKB, gene Ontology (GO), G4IPDB  48  databases and G4 search terms in PubMed, we determined that eighteen proteins found enriched on constrained G4 structures 1a-2 have been already implicated in G4 biology (Table  2 ).
a unique and highly enriched KEGG pathway cluster (p < 0. 0005) consisting of factors involved in aminoacyl-tRNA biosynthesis  50. Further analysis based on the STRING protein-protein interactions database  51  unveiled that all of the components of the multi-tRNA synthetase complex (MSC) are enriched on constrained G4 1a (Fig. 4B-C ). Indeed, in addition to eight cytoplasmic aminoacyl tRNA synthetase enzymes (methionyl MARS, glutaminyl DARS, lysyl KARS, arginyl RARS, isoleucyl IARS, leucyl LARS, aspartyl DARS and glutamyl-, prolyl EPRS-tRNA synthetase) composing this complex, our pull down assay also isolated the three non-enzyme components (AIMP1, AIMP2 and AIMP3, also known as EEF1E, proteins) of the MSC complex (Fig. 4B-C ). Furthermore, western blotting analysis showed that AIMP1 interacts with constrained and unconstrained G4 structures in vitro (Fig.
After elution by competition with a Flag peptide (Supplementary Figure  S6 ), the NELF complex was use in pull-down experiments using     49. From this analysis we determined that 62 out of 425 proteins were reported as G4 ligands sensitisers. constrained constructions and western blotting analyses were performed to quantify the relative signal of the bound NELFE protein. As shown in the Fig. 6B , the NELFE protein is significantly enriched on constrained G4 structures relatively to the cyclopeptide (CP-T23) and duplex control 8, indicating a selective binding of the NELFE protein to G4 structures. In addition, hybridization with an anti-NELFA antibody indicates that at least two proteins from the immunoprecipitated NELF complex are found enriched on constrained G4 structures relatively to the controls (CP-T23 and system 8) (data not shown).
MS-based quantitative proteomic analysis. Eluted proteins were stacked in a single band in the top of a SDS-PAGE gel (4-12% NuPAGE, Life Technologies) and stained with Coomassie blue R-250 before in-gel digestion using modified trypsin (Promega, sequencing grade) as previously described  74. Resulting peptides were analyzed by online nanoliquid chromatography coupled to tandem MS (UltiMate 3000 and LTQ-Orbitrap Velos Pro, Thermo Scientific). Peptides were sampled on a 300 µm × 5 mm PepMap C18 precolumn and separated on a 75 µm × 250 mm C18 column (PepMap, Thermo Scientific) using a 120-min gradient. MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Peptides and proteins were identified and quantified using MaxQuant (version 1. 6. 2. 10,  75  ) using the Uniprot database (Homo sapiens reference proteome, October 22 nd 2018 version) and the frequently observed contaminant database embedded in MaxQuant.
Trypsin was chosen as the enzyme and 2 missed cleavages were allowed. Peptide modifications allowed during the search were: carbamidomethylation (C, fixed), acetyl (Protein N-ter, variable) and oxidation (M, variable). Minimum peptide length was set to 7 amino acids. Minimum number of peptides and razor + unique peptides were set to 1. Maximum false discovery rates-calculated by employing a reverse database strategy-were set to 0. 01 at peptide and protein levels. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  76  partner repository with the dataset identifier PXD 021003. Statistical analysis were performed using ProStaR  77. Proteins identified in the reverse and contaminant databases, proteins only identified by site, proteins identified with only 1 peptide and proteins exhibiting less than 3 intensity values in one condition were discarded from the list.
Before blocking (0. 1% Tween-20, non-fat dry milk 5% and PBS), UV exposition of membrane was used to confirm homogeneous loading and to quantify transfer signal. The membrane was successively probed with primary antibodies and appropriate goat secondary antibodies coupled to horseradish peroxidase (described in table below). Chemidoc imager (Biorad) was used to perform UV and Clarity ECL (Biorad) detection. Digital data were processed and quantified using ImageLab (Biorad) or ImageJ softwares. Quantifications of antibody signal are relative to transfer signal. Immunofluorescence. HeLa cells were seeded in 24-wells plate at 100. 000 cells/well on glass coverslips (VWR, #631-0150). Twenty-four hours later, HeLa cells were treated with 20 μM pyridostatin (Sigma-Aldrich; CAS number 1085412-37-8) for 4 h, and then washed with PBS and fixed with 2% paraformaldehyde in PBS at room temperature for 10 min, washed with PBS and permeabilized for 15 min at room temperature with 10 mM Tris-HCl pH 7.
5, 120 mM KCl, 20 mM NaCl, 0. 1% Triton-X 100. Then, cells were washed with PBS and incubated for about 1 h at 37 °C in blocking buffer (20 mM Tris-HCl pH 7. 5, 150 mM NaCl, 2% BSA, 0. 2% fish gelatin, 0. 1% Triton-X 100) prior to incubation overnight at 4 °C with γH2AX (Phospho S139) antibody (Abcam, #81299) diluted at 0. 7 µg/mL in blocking buffer. Cells were then washed with 0. 1% Tween20-PBS and incubated with secondary goat anti-rabbit antibody coupled to AlexaFluor 488 (Thermo Fisher Scientific) diluted at 2 µg/ mL in blocking buffer for 1 h at room temperature. At last, cells were washed with 0. 1% Tween20-PBS and stained with 0. 1 μg/mL DAPI for 20 min at room temperature, and coverslips were mounted with Vectashield mounting medium (Vector Laboratories). Nuclear γH2AX foci staining overlapping with DAPI staining were quantified with ImageJ software.
Statistical analysis. All results provide from at least three independent experiments. Statistical analyses were performed with GraphPad Prism Software (version 8). For γH2AX quantifications analyses, multiple unpaired t-tests (without corrections for multiple comparisons) were performed between pairs of conditions. On all figures, significant differences between specified pairs of conditions are shown by asterisks (*: p-value < 0. 05; **: p-value < 0. 01; ***: p-value < 0. 0005; ****: p-value < 0. 0001). ns means non-significant difference.
Acknowledgements This work was funded by grants from  ANR  ( ANR-16-CE11-0006-01 ), and  La Ligue Nationale Contre le Cancer  (Equipe Labellisée 2018). Patrick Calsou is a researcher from INSERM. Proteomic experiments were partly supported by the  ProFi grant  ( ANR-10-INBS-08-01 ). The Nanobio-ICMG platform ( UAR 2607 ) is acknowledged for providing facilities for the synthesis and purification of oligonucleotides ( R. Lartia ) as well as for mass spectrometry analyses ( A. Durand ,  L. Fort  and  R. Guéret ).
HeLa cells were seeded at 250. 000 cells per well in a 6-wells plate. siRNA(Table)were transfected twice at 50 nM final concentration per well with Lipofectamine RNAiMax Reagent (Invitrogen) according to manufacturer's instructions. Cells were treated and proteic extracts are realized 72 h after the first transfection. Target Dilution Species Class Reference Manufacturer KU70 0. 2 µg/mL Mouse Monoclonal MA5-13110 Invitrogen eIF4G 1/1000 Rabbit Polyclonal 2498 Cell Signaling Technology WRN 1/2000 Mouse Monoclonal W0393 Sigma-Aldrich Mre11 1/1000 Mouse Monoclonal 611366 BD bioscience hnRNP A1 1/1000 Rabbit Polyclonal GTX106208 Gene Tex DHX36 1/1000 Rabbit Polyclonal HPA035399 Sigma-Aldrich CNBP 8/1000 Kindly provided by N. Calcaterra's group NCL23 1/1000 Rabbit Polyclonal ab50279 Abcam NELFE 1 μg/mL Rabbit Monoclonal A301-913A Bethyl laboratories NELFB (COBRA) 1 μg/mL Rabbit Monoclonal A301-911A Bethyl laboratories Anti-rabbit 1:10,000 Goat Polyclonal 111-035-003 Jackson Immunoresearch Anti-mouse 1:10,000 Goat Polyclonal 115-035-003 Jackson Immunoresearch RNA interferences.
FRET data analysis The FRET efficiency was calculated using I A /(I D + I A ), where I D and I A represent the donor and acceptor intensities, respectively. Basic data analysis was carried out by scripts written in MATLAB, and all data fitting was performed by Origin 8. 0. All fluorescent spots in each movie were selected unless the trace showed a poor signal: noise ratio lower than 3:1 or the intensity changes of the donor and acceptor did not match well (the changes of the two fluorescent intensities were not strictly inverse correlated). The FRET histograms were constructed from more than 300 traces in most cases (Table  S3 ). The fluorescence of all molecules in each FRET distribution was recorded during the same amount of time. The number of Cy5 spots was counted to represent the number of remaining DNA molecules on the surface.
ACKNOWLEDGMENTS This work was supported by the  National Natural Science Foundation of China  ( 32071225 ,  31870788 , and  32071291 ) and the  Chinese Universities Scientific Fund  ( Z109021718 ). The research was conducted within the context of the  International Associated Laboratory  ''Helicase-mediated G-quadruplex DNA unwinding and  Genome Stability. ''We thank  Dr. Wen-Qiang Wu  at  Henan University  and  Dr. Xing-Hua Zhang  at  Wuhan University  for insightful discussions. This paper N/A GGGTGGGTGGGTGGG; see Table S1 This paper N/A GGGTGGGTAGGGTGGG; see Table S1 This paper N/A GGGTTGGGTTGGGTTGGG; see Table S1 This paper N/A GGGTTAGGGTTAGGGTTAGGG; see Table S1 This paper N/A GGGTTTTGGGTTTTGGGTTTTGGG; see Table S1 This paper N/A GGGTTTTTGGGTTTTTGGGTTTTTGGG; see Table S1 This paper N/A GGGGTTAGGGGTTAGGGGTTAGGGG; see Table S1 This paper N/A GGGGTGTGGGGACAGGGGTGTGGGG; see Table S1 This paper N/A GGGGGTTAGGGGGTTAGGGGGTTAGGGGG; see Table S1 This paper N/A Recombinant DNA Plasmid: p11d-tRPA Henricksen et al.
8° and 2. 3°, giving a Q range from 0. 014 to 1 Å -1. On SURF the data were collected using either two angles (0. 65° and 1. 52°) or one angle (1. 52°). All the instruments were calibrated using a pure D 2 O subphase. For the DPPG/TMCL system, the measurements were done using 4 isotopic contrasts of h-DPPG/h-TMCL and d-DPPG/h-TMCL in either First, a dynamic compositional analysis was performed during the interaction of G 4 with the mixed lipid monolayers at the initial surface pressures of 15 mN/m. The low-Q analysis approach of FIGARO was used to follow the compositional changes over time  [38, 39]. Dynamic changes in the composition of a binary mixture during antimicrobial peptide binding were measured on a Langmuir trough facilitating neutron reflection  [40, 41] , using the low-Q analysis method that was applied in our previous work to resolve kinetic compositions for the first time during the binding of G 4 peptide to saturated lipid monolayers of different charges  [31].
Reflectivity data used were only in the Q range 0. 01-0. 03 Å -1   where the details of the actual interfacial structure had minimal influence on the model. In order to follow the process of peptide binding in real time, reflectivity data were recorded at the incident angle of 0. 62° for both hydrogenous (hDPPG/hTMCL and hDPPG/hDPPE) and deuterated (dDPPG/hTMCL and dDPPG/hDPPE) lipid monolayers on the surface of NRW, before and after injecting the G 4 peptide. In each case, kinetic data during the interaction were recorded every 4 min for the former contrasts with the scattering contribution of the latter contrast interpolated from the equilibrium values before and after the interaction. For each system, the low-Q analysis method functions as follows: the difference in scattering between the two contrasts resolves the surface concentration of DPPG, the surface concentration of the other lipid is constrained from the stoichiometry of the spread lipid monolayer, and the absolute scattering of the other contrast reveals the surface concentration of peptide.
In practice, following careful calibration of the background using pure NRW data, we applied a single layer model to the reflectivity data recorded in both isotopic contrasts on NRW, and then used the following equation to calculate the surface concentration of each component: 𝜌𝜏 = 𝑁 𝐴 (𝛤 𝑙𝑖𝑝𝑖𝑑 𝑏 𝑙𝑖𝑝𝑖𝑑 + 𝛤 𝑝𝑒𝑝𝑡𝑖𝑑𝑒 𝑏 𝑝𝑒𝑝𝑡𝑖𝑑𝑒 ) = ( 𝑏 𝑙𝑖𝑝𝑖𝑑 𝐴 𝑙𝑖𝑝𝑖𝑑 + 𝑏 𝑝𝑒𝑝𝑡𝑖𝑑𝑒 𝐴 𝑝𝑒𝑝𝑡𝑖𝑑𝑒 ) ( 6 ) where ρ is the fitted scattering length density (Å -2 ), τ is the nominal thickness (Å), N A is the Avogadro's number, Γ is the surface concentration (mol/m 2 ), A is area per molecule (Å 2 ) and b is the scattering length (Å) for the lipid and peptide components, respectively. The batch fit function of Motofit was used  [42]. In order to apply Eq. 6 to calculate the surface concentrations of the peptide and the lipid mixture over time, we measured reflectivity profiles against time by subjecting each h/h lipid mixture monolayer to G 4 binding.
In addition, reflectivity profiles were measured from each d/h lipid mixture just before addition of G 4 and then after its binding equilibrium. As explained previously, this contrast does not offer reliable information about the dynamic binding of G 4 because of the high value of scattering length from the d/h lipid  [31]. However, the limited data measured from the d/h lipid runs helped us to extract dynamic changes in the concentrations of both lipid mixture and G 4 from the low-Q data analysis  [40, 41] , with the procedures adopted in the low-Q data analysis further described in Section S4 of the Support Information. Second, an equilibrium structural analysis was performed before and after the binding of G 4 with the mixed lipid monolayers at the initial surface pressures of 8, 15 and 28 mN/m.
Equilibrium structural analysis after peptide binding G 4 was subsequently injected under the monolayers at an initial surface pressure of 15 mN/m, and a structural analysis was conducted over the full Q range for data recorded in all 4 isotopic contrasts. On the basis of the 2-layer model used to fit the lipid-only systems, we have tried to fit each monolayer incorporating the G 4 peptide using a similar 2-layer model by allowing the peptide to penetrate both the tail (1 st ) and solvated head group (2 nd ) layers. The 2-layer model was satisfactory in the case of the DPPG/DPPE monolayer due to the low amount of the peptide associated with both tail and head group layers. However, fits to this model were found to be unsatisfactory for the DPPG/TMCL monolayer. It was found that to fit the 4 isotopic contrasts simultaneously an unrealistically high thickness had to be used for the 2 nd layer under water.
Acknowledgements The authors acknowledge support from a  Marie Curie Fellowship ITN  grant (grant number  608184 ) under SNAL (small nano-objects for alteration of lipid bilayers). We also acknowledge the awards of the neutron beam times on FIGARO at the ILL (https://dx. doi. org/10. 5291/ILL-DATA. TEST-2552 ) on  INTER  ( RB 1610224 , Doi:  10. 5286/ISIS. E. 81735367 ) and SURF (RB 1520191, Doi:  10. 5286/ISIS. E. 73943720 ). We thank  Simon Wood  for assistance during the experiments at the ILL.
We would like to acknowledge Professor Herv e Bachelez for helpful discussions and valuable advice. We are thankful to the French National Research Agency (ANR-17-CE14-0025-03 Tfh Atopy and ANR-19-CE17-0017-01TARGET-NS) and Imagine Institute (Imagine Cross-Laboratory Project) for their support. To the Editor: Use of emergency department (ED) services for skin conditions has increased significantly at a rate outpacing that of general ED use. 1, 2 rior studies of skin conditions in the ED find that infections represent half of dermatologic ED diagnoses. 3 In this study, we aimed to identify common ED prescriptions, rates of inpatient admission, and factors associated with high ED use the Medical Expenditure Panel Survey, a nationally representative database of health care expenditures and use.
We identified all patients who visited an ED for skin conditions from 1996 to 2012 using the clinical classification system. Among patients with dermatologic clinical classification system codes, we included patients with International Classification of Diseases, Ninth Revision, Clinical Modification codes 172-173, 232, and 680-709 as their primary diagnosis to ensure identification of dermatology-specific visits (Supplemental Methods; available via Mendeley at https://doi. org/10. 17632/2f8yj776x5. 1). To identify factors associated with high ED use, defined as 2 or more dermatologic ED visits per year, we created multivariable regressions using SAS 9. 4 (SAS Institute, Cary, NC) survey methods to model high utilization (binomial dependent variable) against socioeconomic characteristics. We identified 2377 ED visits with a primary dermatologic diagnosis, reflective of 1.
From  Institut national de la sant e et de la recherche m edicale, Unit e Mixte de Recherche 1163, Laboratory of Genetic Skin Diseases, Imagine Institute, Paris, France a ; Department of Dermatology, Centre Hospitalo-Universitaire Centre-Ville, University of Li ege, Li ege, Belgium b ; Private pediatric practice, Li ege, Belgium c ; Department of Dermatology, Saint-Louis Hospital (Assistance Publique -Hôpitaux de Paris), Paris, France d ; University of Paris, Paris, France  e ; and  Department of Genetics, Necker Hospital for Sick Children (Assistance Publique -Hôpitaux de Paris), Paris, France. f  Dr Barbieux  and  Author Bonnet des Claustres  contributed equally to this article. Funding sources: Supported by the  National Research Agency  ( ANR-17-CE14-0025-03   Tfh Atopy  and  ANR-19-CE17-0017-01TARGET-NS ) and  Imagine Institute (Cross-Lab Project ).
State funding from the  Agence Nationale de la Recherche  under '' Investissements d'avenir'' program  ( ANR-10-IAHU-01 ). Conflicts of interest: None disclosed. IRB approval status: Reviewed and approved by the  IRB of Assistance Publique, Hôpitaux de Paris, France. Reprints not available from the authors. Correspondence to:  Alain Hovnanian, MD, PhD ,  Imagine Institute, Institut national de la sant e et de la recherche m edicale, Unit e Mixte de Recherche 1163 ,  24 , boulevard du Montparnasse, 75015 Paris, France E-mail: alain. hovnanian@inserm. fr.
HP22 Is Part of a Unique Protein Export Pathway from Chloroplasts that Operates during Leaf Senescence The protein isolation data presented in Figure  1  suggested that HP22 is not involved in the import of transit sequence-less precursors into chloroplasts. In order to explore the role of HP22 in planta, T-DNA insertion lines were identified (Figure  4A ). Of the four potential Athp22 mutant alleles present in the SALK and Gabi-Kat collections  [47, 50]  (cf. Figure  4A ), only two (SALK_001823 and SALK_047513) lacked HP22 transcript (Figure  4B ). These were designated Athp22;1 and Athp22;2 and characterized further. On protein gel blots carried out on chloroplasts from T 2 plants, Athp22;1 (SALK_047513) still contained some HP22 protein, whereas Athp22;2 (SALK_001823) did not. Thus, mainly Athp22;2 was used in subsequent experiments and crosses with the Athp20;1 and Athp20;1 mutants (cf.
Acknowledgments: We thank  N. Roland ,  CEA Grenoble, France ,  D. J. Schnell , The  University of Massachusetts, Amherst, USA , and  F. Kessler ,  Université de Neuchatel, Neuchatel, Switzerland , for gifts of cDNA clones and antisera. This work was supported by the  Chaire d'Excellence Program  of the  French Ministry of National Education and Research  (to CR).
Western Blotting Western blotting was performed as previously described  [49]. The primary antibodies were mouse monoclonals against human MGMT (Santa Cruz Biotechnology, Heidelberg, Germany; catalogue #sc-56157) and against β-actin (Sigma-Aldrich; catalogue #A5441). The secondary antibody was an HRP-conjugated goat anti-mouse (Jackson ImmunoResearch, Huissen, The Netherlands; catalogue #115-035-003). The staining was revealed and images were acquired on an Amersham Imager 600 (GE Healthcare, Diegem, Belgium). The Image J software version 1. 8. 0 (NIH, Bethesda, MD, USA) was used for quantification.
It represents the maximal capacity of the cells to use a given oxidative fuel when alternative anaplerotic pathways are blocked. For experiments with TMZ, the protocol was similar, except that 1500 to 3000 cells per well were plated 72 h before experiments, which allowed us to obtain the same final confluence than for TMZ-untreated cells. On the day of analysis, the culture media were replaced by DMEM containing 10 mM glucose, 2 mM glutamine, and 1. 85 g/L NaCl, pH 7. 4. The cells were incubated for 1 h in a CO2-free incubator before analysis. Using the above protocols, olaparib or talazoparib were delivered to the cells as a bolus through an injection port A of the Seahorse device. All of the data were normalized to cell numbers that were measured right before oximetry and pH-metry on a SpectraMax i3 spectrophotometer equipped with a MiniMax imaging cytometer.
Trial oversight This trial was conducted following the Good Clinical Practice Guidelines of the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for human use (ICH-GCP; ref. 8) and approved by ethics review committees at each participating institution. All patients provided written informed consent. Trial conduct was supervised by an IDMC. The trial registration number (clinicaltrials. gov) is NCT01905592. The trial was conducted in collaboration with the European Organisation for Research and Treatment of Cancer (EORTC) and the Breast International Group (BIG), with the participation of BIG member Groups in different countries, as well as independent sites from the United States of America, Hungary, Poland, Israel, and Canada, and sponsored by TESARO.
A futility interim analysis of the primary endpoint of PFS was planned after 93 (68%) centrally confirmed PFS events, using a gamma family beta spending function with a non-binding gamma (g ¼ À5) stopping boundary. Statistical design was computed using PROC SEQDESIGN in SAS and confirmed with EAST software. The primary PFS analysis was performed using a stratified log-rank test (1-sided alpha level ¼ 0. 025) for the difference in the distribution of PFS between niraparib and physician's choice groups. Randomization factors were used as the strata for this test. A non-stratified logrank test was performed to assess the robustness of the primary analysis. HRs and their 95% confidence interval (CI) were estimated on the basis of a Cox proportional hazard model with the randomized treatment as a factor and stratified for the randomization factors.
The number and proportion of patients achieving a response are presented with their corresponding 95% Pearson-Clopper CI. All safety and tolerability evaluations were conducted in the safety population, composed of randomized patients who received at least one dose of study medication. Adverse events are reported from start of treatment up to 30 days after the last treatment administration date. Hematology and biochemistry events were determined on the basis of laboratory values and are reported from start of treatment up to the last administration of study medication. For some hematology and biochemistry tests, to distinguish between grade 0 and 1, normal values were required. In the case of missing normal values, CTCAE grade was categorized as grade 0/1. Serious adverse events are reported on the basis of the safety database, including all randomized patients.