dataesr/scientific-paragraphs-categorization huggingface dataset:
It is similar in concept to HAWC, but ∼×4 larger (for the inner array), and would include a sparser, outer array of ∼1 km 2 to expand the energy range towards the highest-energy frontier. Planned for installation in South America, it will cover the central regions of the Galaxy with an unprecedented sensitivity, and will complement the CTA view. SWGO is not yet funded for construction. LHAASO, currently being deployed at high altitude (4410 m above see level) in the Sichuan Province, China, is a novel concept combining three interconnected detectors: an array of underground water Cherenkov detectors, a kilometre square array made of plastic scintillator and an array of wide field-of-view Cherenkov telescopes. Early data from LHAASO demonstrate the presence of at least twelve source of petaelectronvolt γ rays in the Galaxy  [40] , thus boosting the interest for the extremely high energy frontier.
The analysis of very large, heterogeneous data sets comprising observations spread over several years on very diverse positions has been implemented, leading to major developments in acceptance determination (Section 1. 3) and background subtraction techniques (Section 1. 4), and has led the way towards the first VHE source catalogues. Recent large-scale surveys of unexplored regions were the main driver for the discovery of new sources, and made the first population studies finally possible (Section 2. 4). At the same time, many developments in pattern recognition and image analysis led to elaborate reconstruction and separation techniques, which are now rather close to the fundamental limits, thus with only moderate improvements being possible in the future. Tremendous efforts were made to improve the shower and detector simulation codes, by including subtle instrumental and atmospheric effects.
ATGL-mediated lipid droplet breakdown drives production of eicosanoids and specialised pro-resolving mediators The data presented above demonstrate that transient storage of AA within lipid droplet TAGs followed by AA release from TAGs by ATGL are intermediate steps in basal and hGX-sPLA2stimulated production of PGE2, a major pro-inflammatory and pro-tumorigenic AA-derived eicosanoid. To find out if ATGL-mediated TAG lipolysis promotes the production of a wider range of eicosanoids and related lipid mediators (which might also derive from other PUFAs), we performed targeted liquid chromatography-tandem mass spectrometry-based metabololipidomics to examine the effects of ATGL silencing on the lipid mediator profiles of untreated and hGX-sPLA2-treated breast cancer cells. Here, pre-treatment of breast cancer cells with hGX sPLA2, which enriches PUFA-TAGs in lipid droplets, stimulated the starvationinduced production of various lipid mediators, biosynthesised from different PUFAs by various enzymatic pathways, including prostaglandins, specialized pro-resolving mediators and numerous hydroxylated PUFAs (Fig.
Cancer cells depleted of lipid droplets have impaired PGE2 production The data presented above demonstrate that lipid droplets and ATGL-mediated lipolysis promote the entry of exogenously added and hGX-sPLA2-derived PUFAs into lipid mediator production pathways in serum-starved cancer cells. To determine whether the incorporation of PUFAs into TAGs is indeed a prerequisite for PUFA use as substrates for lipid mediator biosynthesis, DGAT-mediated TAG synthesis was inhibited using a combination of specific inhibitors of DGAT1 and DGAT2 (DGATi), T863 and PF-06424439, and changes in lipid droplet turnover and PGE2 release were examined. DGAT inhibition during serum feeding strongly suppressed basal, hGX-sPLA2-induced and exogenous AA-induced lipid droplet accumulation (Fig. 4A, B, D ). There was an almost complete depletion of lipid droplets in serum-fed DGATitreated cells (Fig.
Neutral lipid quantification by flow cytometry Cellular neutral lipid levels were quantified by flow cytometry as described previously  (Pucer et al, 2013). Floating and adherent cells were harvested and centrifuged at 300× g for 10 min, and the pellets were resuspended in 500 µL 1 µg/mL Nile Red solution in DPBS. After a 10min incubation in the dark, cell analysis was performed by flow cytometry on a FACSCalibur system, equipped with a 488-nm Ar-ion laser, and using the CellQuest software (Becton Dickinson, USA) and an FL-1 (530/30) filter, for at least 2 ×10 4 events per sample.
Data acquisition was carried out using the MassLynx 4. 1 software (Waters), and the lipid classes were analysed with the Lipid Data Analyser 1. 6. 2 software. The data were normalised for recovery, extraction and ionisation efficacy by calculating analyte/internal standard ratios (AU) and expressed as percentage composition. To determine the changes in the phospholipid and TAG profiles in ATGL-depleted and cPLA2α-depleted cells, MDA-MB-231 cells were seeded in complete medium on 6-well plates at 3 ×10 5 cells/well and reverse transfected with ATGL-specific and/or cPLA2α-specific siRNAs, as described above. After 24 h, the cells were washed and grown for 48 h in complete medium in the presence or absence of 10 nM sPLA2. The cells were then washed twice with DPBS and serum starved for the following 24 h in RPMI-1640 medium containing 0.
1% formic acid, 8 µM phosphoric acid) to 100% solvent B (2-propanol; 10 mM ammonium acetate, 0. 1% formic acid, 8 µM phosphoric acid). The column compartment was kept at 50 °C. A Q-TOF mass spectrometer (6560 Ion Mobility; Agilent, Santa Clara, CA, USA) equipped with electrospray ionisation source (Dual AJS) was used for detection of the lipids in positive and negative Q-TOF mode. Data acquisition was carried out using the MassHunter Data Acquisition software (B. 09; Agilent, Santa Clara, CA, USA). Lipid species were manually identified and lipid data were processed using MassHunter Quantitative Analysis (B. 09. 00; Agilent, Santa Clara, CA, USA). Data were normalised for recovery, extraction and ionisation efficiency by calculation of the analyte/ internal standard ratios, and are expressed as fmol/µg protein.
Lipid droplet counting and diameter analysis Confocal microscopy images of BODIPY 493/503 stained lipid droplets were used for computer image analysis with the ImageJ software (National Institutes of Health, USA) and the Lipid Droplet Counter Plugin (https://doi. org/10. 5281/zenodo. 2581434). Analysis was performed on 32-bit two-dimensional images, whereby the numbers and sizes of the lipid droplets were determined according to the plugin instructions. Lipid droplet diameters were calculated from the lipid droplet surface areas. Analyses were performed on at least 40 cells/sample. Data were loaded into the Prism 9. 0. 2 software (GraphPad Software, USA), with the geometric means of the lipid droplet diameters and numbers calculated per individual cell in the samples, followed by the statistical analysis.
02% EFAF-BSA. After 6 h, the cells were treated with 10 nM recombinant hGX sPLA2 in complete medium, with 25 µM (A549) or 50 µM (MDA-MB-231) indomethacin, 25 µM nordihydroguaiaretic acid, and/or 20 µM each of the T863 and PF-06424439 DGAT1 and DGAT2 inhibitors, respectively. A549 cell proliferation was determined after 48 h using the direct cell proliferation assay kits (CyQUANT; Invitrogen, USA). MDA-MB-231/Luciferase-2A-RFP cell proliferation was determined by measuring RFP fluorescence emission (excitation, 558 nm; emission, 583 nm) on a microplate reader (Infinite M1000; Tecan, Austria). The final data were obtained after subtracting the background signal of the blank samples that contained culture medium without cells.
Acknowledgements We are grateful to  Ana Temprano Lopez ,  Petra Hruševar ,  Ana Kump ,  Ema Guštin ,  Belen Vilanova Baeza  and  Špela Koren  for their technical help,  Dr. Mojca Pavlin  ( University of Ljubljana, Slovenia ),  Dr. Brett McKinnon  ( Berne University Hospital, Switzerland ),  Dr. Merce Miranda  ( Joan XXIII University Hospital Tarragona, Spain ) and  Dr. Boris Rogelj  ( Jožef Stefan Institute, Slovenia ) for kindly providing cell lines. We thank  Dr. Chris Berrie  for critical reading of the manuscript. This work was supported by a  Young Researcher grant  ( 1000-15-106 ) and a postdoctoral grant ( Z3-2650 ) to E. J. J. , a  Research Programme grant  ( P1-0207 ) and a  Research Project grant  ( J7-1818 ) to T. P. , from the  Slovenian Research Agency ; by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ),  Project-ID 239748522 -SFB 1127 ChemBioSys , and the  EU COST Action   CA19105  EpiLipidNet.
Optical and Near-infrared Imaging We observed eMACS J0252 with the GMOS camera on the Gemini-North 8. 1-m telescope atop Maunakea on September 9, 2015 (Program ID GN -2015B-Q-42, PI Ebeling). Three dithered exposures of 90s duration each were taken in each of the  ,  , and  passbands. We used standard data reduction procedures as provided by the Gemini data analysis pipeline to create bias-subtracted, flat-fielded, and fringe-corrected co-added images for each filter. A color image of a 1 Mpc square region around the cluster, based on our GMOS data, is shown in Fig. 1. On Dec. 13, 2018, eMACS J0252 was observed with the Wide Field Camera 3 (WFC3) aboard the Hubble Space Telescope (HST) for the SNAPshot program  GO-15307 (PI Gladders). WFC3 obtained images in the F200LP and the F110W filters, using the camera's UVIS and IR channels, respectively.
6, 2019. Three dithered 970s integrations were obtained in 0. 95 seeing for program 0103. A-0777(A) (PI Edge) in WFM-NOAO-N mode. The MUSE data were reduced using v2. 7 of the MUSE data reduction pipeline  (Weilbacher et al. 2020) , following standard reduction recipes for instrumental calibration and sky subtraction, including specific improvements for self-calibration and masking of interstacks as described in detail in  Richard et al. (2020). The field of view of the final combined datacube covers 1×1 arcmin 2 centered on the cluster BCG, sampled at a 0. 2 pixel scale. The wavelength range covers 4750-9350Å at 1. 25Å per pixel, with a spectral resolution  ∼ 2700. We extracted spectra from continuum sources using the HST/F110W image as an input catalog. We also searched for line emitters in the reduced datacube using the muselet software, which is part of the MPDAF python package (Piqueras et al.
g. , McNamara & Nulsen 2012)) would constitute, to the best of our knowledge, the first direct kinematic confirmation of the cooling-flow picture advanced by  Fabian et al. (1984). In addition, the extreme ellipticity of both the BCG and the clusterscale dark-matter distribution of its host cluster (as constrained by our strong-lensing mass model) deserves special emphasis. At  = 0. 8, the ellipticity of eMACSJ0252 provides a rare glimpse of the extreme end of the cluster-halo shape distribution for comparison with predictions from numerical simulations  (Harvey et al. 2020). Although the existing observational data have established eMACS J0252 unambiguously as an exceptional cluster, dominated by an equally exceptional BCG, our knowledge of the system remains preliminary in several important areas.
ACKNOWLEDGEMENTS IRS and ACE acknowledge support from  STFC  ( ST/T000244/1 ). Based on observations obtained at the  international Gemini Observatory , a program of  NSF 's NOIRLab, which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation  on behalf of the Gemini Observatory partnership: the  National Science Foundation  ( United States ),  National Research Council (Canada) ,  Agencia Nacional de Investigación y Desarrollo (Chile) ,  Ministerio de Ciencia, Tecnología e Innovación (Argentina) ,  Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brazil) , and  Korea Astronomy and Space Science Institute (Republic of Korea ). Also based on observations made with the  NASA/ESA Hubble Space Telescope , obtained from the  Data Archive at the Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc.
Numerical and Experimental Details 2. 1. Governing Equations Accounting for Solid Body Motions The governing equations of fluid flow (RANS) are as follows  [26, 27] : ∇ • u = 0, ( 1 ) ∂u ∂t + ∇ • (uu) = - ∇p ρ + ∇ • (ν e f f ∇u) + g + f σ , (2) where u, ρ and p are the velocity, density and pressure of the fluid. The fluid is treated as incompressible (ρ = const). The open-source code OpenFOAM is used throughout this work. The pressure and velocity are decoupled using the merged-PISO-SIMPLE scheme, which allows larger time steps and thus accelerates the simulations. ν e f f = ν + ν t is the effective dynamic viscosity, consisting of the fluid viscosity (ν) and the eddy viscosity (ν t ) calculated from a certain turbulence model. The SST kω model is adopted in this study. It is a two-equation eddy-viscosity model which combines the advantages of the kand kω models.
B. Hydrodynamic simulations Plasma formation was simulated using radiation hydrodynamic codes with the goal of defining the optimal interaction conditions, that is, creation of a sufficiently homogeneous plasma with required density and temperature. The experiment is three-dimensional, and it cannot be fully simulated using the available hydrodynamic codes accounting for the laser-plasma interaction and foam structure. Consequently, the numerical simulations were performed in two steps. First, interaction of heating beams with the target was simulated using the twodimensional (2D) axisymmetric Eulerian radiation hydrodynamic code XRL2D,  32, 33  which describes laser energy deposition accurately but models foam as a homogeneous material. Second, the propagation of the ionization front in the foam was simulated using the 2D axisymmetric arbitrary Lagrangian-Eulerian hydrodynamic code PALE (Prague Arbitrary Lagrangian Eulerian),  34  which accounts for the foam's porous structure but considers normal incidence of laser beams.
To make the simulations compatible with these two codes, the laser power in the PALE simulation was adjusted to obtain the same ionization front velocity as in the XRL2D simulation with the same homogeneous material. XRL2D is a 2D Eulerian radiation hydrodynamic code for studying laser interactions with solid-density targets. The electron and ion energy equations are solved with Kershaw's nine-point diffusion difference scheme, and the electron heat flux is determined by the flux-limited diffusion model. The radiation transfer equation is solved by the flux-limited multigroup diffusion method. The laser energy deposition via inverse bremsstrahlung is calculated with a 3D ray-tracing package. The thermodynamic quantities are derived either from the ideal gas model or from data on the realistic equation of state.
Among the different numerical descriptions of foam homogenization,  [27] [28] [29]  we implemented in our 2D axisymmetric hydrodynamic code PALE the hydrothermal wave model described in Ref. 29 and enhanced by random scattering of the laser light in the nonhomogenized foam region. PALE  34  is a hydrodynamic Lagrangian code for laser-generated plasma simulations. It solves hydrodynamic Euler equations for mass, momentum, and energy conservation laws, accounts for the Spitzer-Härm flux-limited electron heat conductivity, and assumes normal incidence of the laser light. It describes laser propagation and energy deposition by a ray tracing model including inverse bremsstrahlung and resonance absorption at the critical surface. The state of the plasma is described by the quotidian equation of state (QEOS).
The front velocity is delayed, and the calculated value of the front velocity of 350 μm/ns-450 μm/ns was used for choosing the foam thickness. The foam of thickness of 0. 8 mm was expected to be ionized in 1 ns while being irradiated by laser beams from two sides. Unfortunately, the ionization front velocity observed in the experiment was smaller than expected, about 300 μm/ns-350 μm/ns, and so the foam was not fully ionized. The numerical simulation with the PALE code was used for defining the plasma hydrodynamic parameters of the expanding hot plasma and characterization of the parametric instabilities. The plasma characteristics along the symmetry axis for the foam density of 10 mg/cm 3 are shown in Fig. 2. The plasma temperature is nearly constant in space and in time, with a characteristic value of T e ≃ 1.
A. Experimental setup and targets The overall scheme of the experimental setup and plasma diagnostics is shown in Fig. 4. In the experiments, we used five laser FIG. 2. Spatial profiles of plasma electron temperature (a), density (b), and flow velocity (c) at four time moments of 0. 25 ns (solid blue), 0. 5 ns (dashed blue), 0. 75 ns (dotted blue), and 1 ns (solid red) calculated using the PALE code for a foam density of 10 mg/cm 3. The laser propagates in the negative z direction, and the foil edge is at z 0. 2] [3]  Each beam delivered ∼800 J at the third harmonic (wavelength 351 nm) in a trapeze-like pulse of 1 ns duration with a rise and fall time of 0. 15 ns. All the beams had an elliptical shape so that their footprints on the target surface were circular. Four laser beams were used for the plasma creation.
ACKNOWLEDGMENTS The authors are grateful to the engineers and technicians operating the  SGIII-P facility  for their support of this experimental campaign. This project was partially supported by the  Advanced Research Using High Intensity Laser Produced Photons and Particles (ADONIS)  project (Grant No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000789 ) and the  CAAS  project (Grant No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000778 ), both from the  European Regional Development Fund. The results of the  LQ1606  project were partially obtained with the financial support from the  Ministry of Education, Youth and Sports  as.
37; for Phe, 2. 13 s; and for Ser, 2. 0 s. Processed spectra were obtained by applying an apodization function (exponential function with LB = 1. 5 Hz) prior to Fourier transformation, with zerofilling (SI = 262144). A baseline correction was applied with a specific correction for sub-parts of the spectra. After baseline correction, a curve fitting was carried out with a Lorentzian mathematical model using the rnmrfit R package  (Sokolenko et al. , 2019).
ACKNOWLEDGMENTS Thank you to  A. -M. Schiphorst  ( CEISAM ) for EA-IRMS analysis and to  M. Grand  ( CEISAM ) for analytical support. Thank you to  Dr. Cherie Achilles  for her thoughtful comments on the manuscript. This material is based upon research supported by the  National Aeronautics and Space Administration  through the  NASA Astrobiology Institute  under Cooperative Agreement No. 80NSSC18M0094  issued through the  Science Mission Directorate. A. C. Fox was supported by  Chateaubriand Fellowship of the Office for Science & Technology of the Embassy of France in the United States.
MATERIALS AND METHODS A detailed protocol for the literature search strategy (Online Supplemental Data) was designed and implemented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. 14 The article was prepared in accordance with the Guidelines for Reporting Reliability and Agreement Studies. 15 The research ethics board at the University of Alberta provided consent to access patient clinical and angiographic data. The electronic search was performed using the Cumulative Index of Nursing and Allied Health Literature, EMBASE, EBM, Cochrane Database of Systematic Reviews, and MEDLINE data bases with no start date specification, capturing English and French publications up to August 28, 2019. One author tested the ability of the search strategy to recover pertinent articles.
Statistical Analysis Results were analyzed for all raters according to the arterial segment, first using all 3 categories and then dichotomized into nonsevere/severe using 50% vessel narrowing as the threshold. This analysis was then repeated for the raters who responded to the portfolio with the dichotomized higher (two-thirds) narrowing threshold. An overall per-patient assessment of severe vasospasm was considered to have been made when the rater assessed narrowing to be "severe" for at least one of the ICA, M1, or basilar segments. Isolated A1 segment vasospasm was not included in this definition to avoid the uncertainty regarding pre-existent A1 hypoplasia. Responses were then stratified according to training background and years of experience. Interrater and intrarater agreement regarding the degree of vasospasm for each arterial segment was assessed using Fleiss k statistics for dichotomized data using 95% bias-corrected confidence intervals obtained with 1000 bootstrap resampling.
For nondichotomized data, agreement was determined using the Krippendorff a. The strength of agreement was interpreted according to Landis and Koch (0-0. 2, slight; 0. 21-0. 4, fair; 0. 41-0. 6, moderate; 0. 61-0. 8, substantial; 0. 81-1. 0 almost perfect). Differences between point estimates or between k values were considered to exist when confidence intervals did not overlap. All analyses were performed by statisticians (M. C. , J. Z. ) using STATA, Version 16. 0 (StataCorp) and SPSS Version 25 (IBM), with a significance level set at. 05.
Moreover, the leaf nodes correspond to specific Common Vulnerabilities and Exposures (CVEs) whose CVSS score is calculated by the US National Vulnerability Database. Therefore, the CVSS is applied in the entire attack tree, and the most threatening tree is computed. Finally, it is worth mentioning that the authors evaluate their method with a similar one, which relies on the Bayes method and CVSS. Based on this evaluation, first, the proposed method is verified since both methods compute similar results. Secondly, the proposed method calculates higher attack probabilities than that using the Bayes methods and CVSS. In  [20] , E. Rios et al. present a continuous quantitative risk management methodology for SG environments. While the paper is focused on SG, the proposed method can be applied in any Information Technology (IT) and IoT ecosystem.
It is worth mentioning that the alerts are stored in an Elasticsearch database on the server-side. Next, the IEC-104 Flow Extraction Module extracts the TCP/IP network flow statistics used by the outlier detection mechanism for detecting possible anomalies. On the other hand, the server consists of (a) the Anomaly Detection Module and (b) the Response Module. The Anomaly Detection Module applies the outlier detection algorithm, which will distinguish whether an IEC 60870-5-104 is anomalous or not. To this end, three outlier detection algorithms were tested: (a) One-Class Support Vector Machine (SVM), Local Outlier Factor (LOF) and Isolation Forest under different flow timeout thresholds: 15 s, 30 s, 60 s and 120 s. Finally, the Response Module informs the security administrator via Kibana.
Based on the evaluation results, Isolation Forest achieves the best performance when the flow timeout is defined at 120 s. In our previous work  [22] , we developed DIDEROT. DIDEROT is an Intrusion Detection and Prevention System (IDPS) capable of detecting and mitigating cyberattacks against the DNP3. The architectural model of DIDEROT consists of three modules, namely (a) Data Monitoring Module, (b) DIDEROT Analysis Engine and (c) Response Module. The Data Monitoring Module undertakes to capture the DNP3 network traffic and generate bidirectional network flow statistics. To this end, Tshark  [23]  and CICFlowMeter  [24]  were utilised respectively. Moreover, the Data Monitoring Module is responsible for normalising these statistics by applying the min-max scaling function. Next, the DIDEROT Analysis Engine is composed of two ML classifiers that operate complimentarily.