dataesr/scientific-paragraphs-categorization huggingface dataset:
Cryo-SIM imaging and high-resolution data reconstruction Vitrified samples on grids were transferred to the cryo-SIM and brightfield imaging was first employed to generate mosaics, where individual cells were evaluated based on cell location (likely to allow data collection on both this instrument and the TXM) and overall state (no obvious grid surface or cell sample disruption). Samples were then imaged in both green and red fluorescence to identify individual cells within the population that both expressed fluorescent Gal-3 and were infected with fluorescent virus. 3D-SIM data was collected on a number of these representative cells (4 mock-infected controls, 8 at 1h PI, 11 at 2h PI, 11 at 3h PI and 5 at 4h PI). Data were reconstructed with SoftWoRX 6. 5. 2 (GE Healthcare) using real optical transfer functions generated from 3D-SIM images of 175µm single-colour fluorescent beads (PointSpec, Thermo Fisher Scientific) to produce super resolution image stacks.
Cryo-soft X-ray Tomography and X-ray data reconstruction X-ray data were collected with an UltraXRM-S/L220c X-ray microscope (Xradia now Carl Zeiss microscopy GmbH) at beamline B24 (DLS) using 500 eV X-rays. This instrument is fitted time with a capillary condenser, a 40nm zone plate objective (25nm for the BSC-1 work) and a 1024B Pixis CCD camera (Princeton instruments). Samples were loaded into the microscope chamber in batches of four and were assessed for structural integrity and alignment potential, inspecting them first with the in-line visible light 20x objective (images recorded on a Retiga 4000R camera; Teledyne) and then with the X-ray microscopy to generate 2D mosaics (visible light gives an overall map of the grid and using X-rays map areas in grid boxes that contained ROIs. The visible light microscopy setup benefits from a variable visible light LED which was used to confirm fluorophore presence and agreement with the fluorescence signal recorded in the cryo-SIM.
Tilt series were collected from -65° to +65° at increments of 0. 5° on 3 mockinfected cells, 4 cells at 1h PI, 8 cells at 2h PI, 4 cells at 3h PI and 5 cells at 4h PI (all previously imaged at the cryoSIM). All data was aligned and reconstructed automatically to tomograms using the in-house pipeline which employs Batchruntomo 51 via a number of directives to allow parallel and near real-time processing. Specifically, three protocols for alignment and reconstruction are currently provided via these directives: weighted back projection (WBP), simultaneous iterative reconstruction techniques (SIRT) and automatic patch tracking. Batchruntomo  51  is part of the IMOD package (version 4. 9. 2)  46. Where necessary, multiple adjacent sites were used for data collection for subsequent stitching and FOV expansion.
Correlative cryo-SXT/ cryo-SIM imaging Equivalent cryo-SIM and cryo-SXT data sets were overlaid with the multidimensional registration software eC-CLEM  52. 2D X-ray mosaics were aligned to 2D grid mosaics from the cryo-SIM data collection using the 2D rigid registration mode of eC-CLEM  52  (coarse alignment). The rotational matrix derived was applied to the 3D X-ray tomograms which were then 3D aligned to the SIM data from that same region using the 3D rigid registration mode of eC-CLEM  52  (fine alignment). Features used for alignment purposes included both the nanoparticles added on samples just before vitrification as well as grid patterns (grid bars, Quantifoil holes and imperfections) and cellular features that displayed good contrast in both X-ray and light microscopy (fluorescent endosomes in this case).
Acknowledgements We thank  Jeff Irwin  ( Carl Zeiss Inc ) for his dedicated and enthusiastic support with all technical issues and applications relating to the TXM,  Damian Reynolds  ( Carl Zeiss Inc ) for technical support,  Michael Schwertner  ( Linkam scientific ) for cryo-microscopy expertise and support advice, and  Eva Pereiro  ( Mistral, ALBA ) for guidance, encouragement and troubleshooting with issues on soft X-ray tomography. We also thank staff at both Micron, DLS as well as B24 team members and support personnel (past and present) for their help and perseverance. Special thanks to  Adam Taylor ,  Adam Prescott  for technical support on a day-to-day basis,  Martin Harrow  for IT support,  Stephen Graham  for contributing to our user protocols and  Liz Duke  for initiating the X-ray tomography project.
M. A. P. was funded via the  Wellcome Centre for Human  genetics by the  Wellcome Trust   ISSF grant   105605/Z/14/Z. The  Wellcome Trust  grant  203141/Z/16/Z  funds the  Wellcome Centre for Human Genetics. I. M. D. was funded in part by  Wellcome   Strategic Awards 091911 and107457 (PI Ilan Davis ). P. P-G. acknowledges CROCOVAL  ANR-18-CE45-0015  and is part of the national infrastructure " France BioImaging " supported by the  ANR PIA1 ANR-10-INBS-04. J. M. G. was supported by  Wellcome   Investigator Award   200835/Z/16/Z. S. B. was supported by  Heisenberg grant :  BO 4340/1-1  and SFB1129: Project number  240245660  (Project  14  of  SFB 1129 ) from  Deutsche Forschungsgemeinschaft (DFG). M. L. S. was supported by the  Brigitte-Schlieben Lange Program  from the state of  Baden Württemberg ,  Germany  and grant number  STA 1536/2-1  from the  DFG.
MS analysis Gel bands were reduced, alkylated and digested with trypsin at 37 °C overnight  [18]. Extracted peptides were then analyzed using an Ultimate 3000 nano-RSLC (Thermo Scientific, San Jose, CA, USA) coupled in line with an Orbitrap ELITE (Thermo Scientific). In brief, peptides were separated on a C18 nanocolumn with a linear gradient of acetonitrile and analyzed in a top 20 collision-induced dissociation (CID) and a top 10 higher-energy C-trap dissociation (HCD) data-dependent MS. Data were processed by database searching using SequestHT (Thermo Fisher Scientific) with PROTEOME DISCOVERER 2. 4 software (Thermo Fisher Scientific) against S. aureus Swiss-Prot database. Precursor and fragment mass tolerance were set at 10 p. p. m. and 0. 6 Da, respectively, for CID and 10 p. p. m. and 0.
Background: Current research that combines radiation with targeted therapy may dramatically improve prognosis of pancreatic ductal adenocarcinoma (PDAC). We investigated preclinical outcomes of DNA repair inhibitor targeted therapy associated with radiotherapy. Methods: We searched Pubmed database to identify publications assessing DNA damage targeted therapies in preclinical models of PDAC in vitro and in vivo. Standard enhancement ratio, median survival and growth delay were extracted. Results: We identified fourteen publications using DNA repair targeted therapies in preclinical models of PDAC. Ten publications comprising twenty-eight experiments evaluated radiosensitization with different DNA repair inhibitors in vitro and displayed cell killing by a factor of 1. 35±0. 047. Moreover, 86% (24/28) of in vitro experiments showed radiosensitization with DNA damage response inhibitor.
2. Outcomes. The primary outcome was the standard enhancement ratio (SER) evaluating the in vitro radiosensitization effect of DNA damage response inhibitor on colony forming assay. For studies which did not specify SER in their manuscript, data were extracted from published plots using Web Plot Digitizer (https://automeris. io/WebPlotDigitizer) to convert datapoints into numerical values. Then SER was determined by calculating the ratio of doses in treated and control conditions for a given isoeffect (SF=0. 1). Secondary outcomes included the in vivo radiosensitization effect of DNA damage response inhibitors, the in vitro/in vivo radiosensitization effects of DNA damage response inhibitors on normal cell lines and the in vitro/in vivo effects of DNA damage response inhibitors on gemcitabine-based chemoradiotherapy.
3. Data extraction. Data were extracted independently and verified by two authors (WW and AP) using a predefined data extraction form. For each study, we extracted the type of cell line and the type of targeted therapy used. Concerning in vivo studies, the type of graft (xenotopic/orthotopic), the radiotherapy schedule (fractionation/single dose) and the criteria for assessing drug activity (tumor doubling time/tumor growth delay) were also reported.
Results: 1. Identified studies PubMed database searches yielded 2794 unique citations. All abstracts from PubMed were retrieved and using pubmed. mineR package we selected 96 abstracts containing one of the following terms: "ATM", "ATR", "PARP", "Wee1", "PP2A", "CHK", and "DNA-PK". After full-text review and evaluation of appropriate references, 14 unique citations were included in this review (Figure  1 ).
Data sources and study selection. PubMed database was searched with the following search string: "Pancreatic cancer"[All Fields] AND ("radiosensitization"[All Fields] Fields] OR "radiation"[All Fields]) AND ("2007/01/01"[PDAT] :"2020/01/11"[PDAT]). Then, all titles or abstracts containing DNA damage response terms (ATM/ATR, CHK1/2, Wee1, PARP, DNA-PK, and PP2A) were identified. References of all included studies were reviewed for more eligible studies. The last search was run on January 11 th 2020. Each study was screened and reviewed for eligibility independently by two authors.
: 1. 1 Data sources and study selection. PubMed database was searched with the following search string: "Pancreatic cancer"[All Fields] AND ("radiosensitization"[All Fields] Fields] OR "radiation"[All Fields]) AND ("2007/01/01"[PDAT] :"2020/01/11"[PDAT]). Then, all titles or abstracts containing DNA damage response terms (ATM/ATR, CHK1/2, Wee1, PARP, DNA-PK, and PP2A) were identified. References of all included studies were reviewed for more eligible studies. The last search was run on January 11 th 2020. Each study was screened and reviewed for eligibility independently by two authors.
Dataset and simulated event samples Events are selected from proton-proton ( ) collisions collected by the ATLAS detector at the LHC at √  = 13 TeV in 2015 and 2016. Only collisions recorded when all relevant subsystems were operational are considered in the analysis. The dataset corresponds to an integrated luminosity of 3. 2 ± 0. 1 fb -1 recorded in 2015 and 32. 9 ± 0. 7 fb -1 recorded in 2016, for a total of 36. 1 ± 0. 8 fb -1  [21]. The uncertainty is obtained from the primary luminosity measurements using the LUCID-2 detector  [22]. The data used for this search were collected using the single-electron or single-muon triggers with transverse momentum ( T ) thresholds of 20  (26)  GeV for muons and 24  (26)  GeV for electrons in 2015 (2016)  [23]. 1 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the center of the detector and the -axis along the beam pipe.
Multĳet samples are used to compare the data identification efficiency of the  →  b decays with simulation. These samples were generated using P 8. 186, with the LO NNPDF2. 3 PDF set and the A14 tune. To increase the number of simulated events with semileptonically decaying hadrons used in this analysis, samples of multĳet events filtered to have at least one muon with  T above 3 GeV and || < 2. 8 were produced with P using the same version, PDF set, and underlying-event tunes as the unfiltered multĳet samples. Both the filtered and unfiltered multĳet samples produced with P were processed through the same ATLAS detector simulation.
Signal Regions Control Regions on-Z, 1HPC1LPC on-Z, 2HPC on-Z, 2LPC off-Z, 2HPC off-Z, 2LPC Limits on the production cross-section of  ,  →  → ( b) ( b) events are calculated using the test statistic   = -2 ln(L (, θ )/L ( μ, θ)), where L is the likelihood described above,  is the single POI and  is the vector of nuisance parameters (NPs). In addition, μ and θ are the values which maximize the likelihood function, and θ are the values of the NPs which maximize the likelihood function for a given value of   [17]. Upper limits at 95% C. L. on the production cross-section as a function of the mass hypothesis are determined using the asymptotic distribution for    [69] [70] [71]. The impact of systematic uncertainties on the upper limits is evaluated by varying the corresponding NP when building the Asimov dataset  [69]  used to estimate the asymptotic distribution for.
Since they are individually important for the modeling of the background yields, their impacts are reported separately. A correlation of 44% is observed between the two unconstrained nuisance parameters. The impact of the statistical uncertainty is defined as the 1 uncertainty in the expected upper limit after removing all nuisance parameters, both constrained and unconstrained, from the profile likelihood. Figure  10  shows the exclusion limits for the production cross-section times the branching ratio for  ,  →  → ( b) ( b) as a function of the -boson mass hypothesis. For comparison, the SM NNLO cross-section for   →   is  SM (   →  ) = 0. 88 pb  [66]. The figure also includes the expected exclusion limit calculated from an Asimov dataset when all the constrained nuisance parameters are fixed to their expected values and the unconstrained nuisance parameters that scale the  + jets and  t backgrounds are fixed to one.
Conclusion A search for Higgs bosons decaying into a pair of new spin-0 particles that subsequently decay into a final state with four -quarks is presented. The search uses 36 fb -1 of 13 TeV proton-proton collision data collected by the ATLAS detector at the LHC. A dedicated strategy for reconstruction and identification of  →  b candidates in the mass range 15 GeV ≤   ≤ 30 GeV is introduced. The measurement of the acceptance and efficiency of this strategy is described in detail and used to compare data with simulated events in regions with two The ATLAS Collaboration.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaĳan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRT, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russia Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MICINN, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
In addition, individual groups and members have received support from  BCKDF ,  CANARIE ,  Compute Canada  and  CRC ,  Canada ;  ERC ,  ERDF ,  Horizon 2020 ,  Marie Skłodowska-Curie Actions  and  COST ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex  and  ANR, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF, Greece; BSF-NSF  and GIF, Israel;  La Caixa Banking Foundation ,  CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden ;  The Royal Society and Leverhulme Trust ,  United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from  CERN , the ATLAS Tier-1 facilities at  TRIUMF (Canada) ,  NDGF (Denmark, Norway, Sweden) ,  CC-IN2P3  (France),  KIT/GridKA (Germany) ,  INFN-CNAF (Italy) ,  NL-T1  (Netherlands),  PIC (Spain) ,  ASGC (Taiwan) ,  RAL (UK)  and  BNL (USA) , the Tier-2 facilities worldwide and large non-WLCG resource providers.
Numerical modelling approach The experimental results obtained with the French experimental research program  [1, 7, 8, 12, 13]  were used to calibrate and validate a numerical model simulating the thermo-mechanical behaviour of the tested connections. The validation is based on the comparison between the calculated and the measured values of temperatures, charring rates and fire duration resistances. In this section, the main assumptions considered in the numerical modelling of the tested connections are presented. The model is developed in two steps using the MSC-MARC software  [17].
These values retained for the variable factors of the experimental design defined the maximal and minimal borders of the considered parameters, frequently used in timber construction. To take into account the variable parameters (3 diameters, 6 thicknesses of timber and 2 load ratio), 36 configurations were simulated to obtain a complete factorial design considering the influence of each parameter. Table  1  summarizes the predicted fire resistance durations (t fi,sim ) obtained from 3D finite element simulation. For each configuration, the simulation concerned the mechanical analysis to obtain the load bearing capacity in normal conditions (see Section 3. 2). It was followed by the thermo-mechanical analysis as described in Section 3. 3 to obtain the fire resistance. From the calculated values of fire resistance, a polynomial function is fitted according to the three selected parameters, using the least squares method on Matlab.
Agent-based simulation of human diet In order to attempt a reconstruction of the diet of the Mesolithic human skeleton of Aizpea, the Bayesian mixing model open source (https://sourceforge. net/projects/fruits/) software FRUITS  (Food Reconstruction Using Isotopic Transferred Signals; Fernandes et al. , 2014a)  was used. This helps handle dietary routing and allows the incorporation of prior information, including data from ecological, archaeological, biochemical or physiological sources to model different dietary scenarios. Diet was modeled using diet-collagen offsets of 4. 8 ± 0. 2‰ for δ 13 C and 5. 5 ± 0. 5‰ for δ 15 N, taking into account varying macronutrient concentrations as suggested by Fernandes and colleagues  (Fernandes et al. , 2012 (Fernandes et al. , , 2015) )  and conservative routing values for carbon of 75 ± 10% from protein and 25 ± 10% from carbohydrates/lipids (Supplementary Table  A.
1). Following recent practice, a +2‰ tissue fractionation offset was added to faunal collagen δ 15 N values to estimate flesh values  (Fernandes et al. , 2014b; Sjögren, 2017). The raw source/food values used in the model were mainly obtained from the mean isotope values (± 1 SD) measured for the fauna and plants. For the macronutrient composition of foods, modern data obtained from the United States Department of Agriculture Food Composition Database (https://ndb. nal. usda. gov/ndb/) were used. A prior delimiting the proportion of protein consumed for a healthy human diet, expected to be between 4 and 40% (see  Sjögren, 2017, after Noli and Avery, 1988) , was also entered into the model.
While such foods would probably make only a relatively minor contribution to the overall diet, some studies suggest that they could have been an important resource among some hunter-gatherer communities (e. g. Rizner et al. 2009; Raubenheimer et al. , 2014). In this regard, the presence of hundreds of shells from land snails in layer b is noteworthy; considerable evidence for their use has been documented in other Mesolithic circum-Mediterranean and Atlantic sites (e. g. Lubell, 2004; Gutiérrez-Zugasti, 2009; Thomas, 2017; Álvarez-Fernández et al. , 2020). The dietary simulation created using the FRUITS software (Supplementary Table  A. 1) is consistent with the scenario presented above, though, of course, estimations are not exact and very much depend on the various different parameters and offsets used to build the models  (Makarewicz and Sealy, 2015).
Acknowledgements We are grateful to  J. Sesma  ( Archaeology Section, Government of Navarre ),  M. Jover  and  A. Redín  ( Museum of Navarre ) for access to the samples, and to  I. Barandiarán ,  A. Cava ,  P. Castaños ,  A. Sánchez-Marco  and  A. Moreno-Larrazábal  for providing useful information on the site and its faunal and plant collections. We thank  C. Meiklejohn  and the reviewers for their useful comments and suggestions, which have enhanced the quality of the paper. Funding: This work was supported by the  British Academy ,  United Kingdom  ( Newton International Fellowship ,  NF170854 ,  2018 , to T. F-C. ).
Patients will be followed up for 28 days. Patients will be randomised to receive whether awake PP and nasal high flow therapy or standard medical treatment and nasal high flow therapy. Primary outcome is defined as the occurrence rate of tracheal intubation or death up to day 28. An interim analysis plan has been set up on aggregated data from the participating research groups. Ethics and dissemination Ethics approvals were obtained in all participating countries. Results of the metatrial will be submitted for publication in a peer-reviewed journal. Each randomised controlled trial was registered individually, as follows: NCT04325906, NCT04347941, NCT04358939, NCT04395144 and NCT04391140.
The authors highlighted that mortality was lower than in a previously reported cohort study of patients with ARDS performed at the same institution prior to the pandemic,  18  although is not clear if the two populations were comparable in terms of disease severity. ] [21] [22] [23]  However, none of them provided high-level evidence of the effects on patients' outcome. Based on the potential beneficial mechanisms of HFNC and PP, early use of PP combined with HFNC to avoid the need for intubation in COVID-19 patients with moderate to severe ARDS needs to be further investigated. Due to the urgent need to find effective treatments for COVID-19, this meta-trial will gather together several trials launched independently at the beginning of the COVID-19 pandemic. As of 6 May 2020, eight randomised trials evaluating the efficacy of PP in patients with COVID-19 were registered on ClinicalTrials.
Other measures In the PP groups complications will be recorded; complications include skin breakdown, device removal or desaturation during position change (within 28 days of randomisation). Plans for assessment and collection of outcomes Protocol explanation will be provident to study sites during a dedicated online or physical meeting. Assessment and collection of outcomes will be performed by investigators, physicians, nurses and research assistants trained and used to deal with patients with hypoxaemia without additional training required. SpO 2 /FiO 2 ratio assessment requires the SpO 2 to be equal of less than 97%. The primary outcome (intubation or death) is easily retrieved from patients' charts. Bedside sheets are made available to simplify data recording. Each individual study coordinator is responsible for data quality control.
Statistical methods for primary and secondary outcomes We plan a prospective meta-analysis of individual data. Common variables from all datasets will be gathered and combined to conduct the analysis. A detailed analysis plan will be a priori defined. The primary analysis will be performed on an intent-to-treat basis. A sensitivity analysis will be performed on a per-protocol set described below. Baseline patient characteristics will be presented by country and treatment group. The comparison between intervention arms will be synthesised using mixed-effects models with a random effect on the trial: a mixed-effects logistic regression for the primary outcome and any binary outcome. A survival analysis will be performed on mortality and any other time-to-event outcome, using a gamma-frailty term on each trial in a Cox regression model providing that the assumption of proportional hazards is verified.