dataesr/scientific-paragraphs-categorization huggingface dataset:
Among other relevant information, teams were asked to fill out a questionnaire with details on their solution strategy, and their selfassessed experience based on the number of aneurysm cases they had segmented and simulated: high (> 100 cases); medium (11-100 cases); low (1-10 cases); or none (0 cases). The questionnaire and the instructions sent to the teams are included in an online data repository. 1  Teams were also asked to provide velocity and WSS fields (time-averaged and peak systolic for pulsatile simulations) for all five aneurysm cases. (Additionally, teams were asked to provide predictions of rupture status, and the geometric/hemodynamic parameters on which they were based; those results will be reported separately. ).
Response to the Challenge A total of 45 teams registered for the Challenge, of which 26 provided CFD datasets including WSS fields. Two of these teams provided CFD datasets from two different segmentations; as discussed later, there were non-negligible intra-team differences, and so we treated these as independent submissions, resulting in 28 CFD datasets. Datasets from three teams were incomplete: Team 20 did not provide WSS or velocity fields for Case 5; Team 21 did not provide any velocity fields; and Team 24 provided the velocity field only for Case 1. Most teams provided velocity data as vector fields; however three teams (10, 13, 17) provided velocity magnitudes only.
Centralized Data Analysis Despite being derived from the same DICOM image volumes, the lumen geometries provided by the participating teams were in different scales, coordinate systems, rotations, and even mirrored. These were therefore first scaled to consistent units (mm) and mirrored if necessary. Centerlines were computed automatically from the lumen surfaces using the Vascular Modelling ToolKit (VMTK; www. vmtk. org), albeit with manual correction for some non-manifold surfaces. These were then initially registered automatically via the origin of the bifurcation hosting the aneurysm. 33 Owing to the wide variability of the segmentations, surfaces were further manually rotated and translated to best match each other. The original and registered lumen surfaces, and the registered velocity and WSS fields are provided in the online data repository.
1  Besides simplifying the visualisation of the multiple datasets, an advantage of registering the fields is that we could delineate a consistent segment of the parent artery (MCA) and the aneurysm sac using the same clipping planes for all teams. From the velocity datasets, lumen areas and mean through-plane velocities were calculated and averaged from five transverse slices (one slice for Case 4) through the MCA segment (c. f. , cyan regions in Fig. 1 ). For the three teams that did not provide velocity vectors, we used their provided velocity magnitudes instead, after confirming that there was high correlation and no appreciable bias between velocities calculated from vectors vs. magnitudes from the other teams (R 2 = 0. 998, slope = 1. 02).
Statistical Analysis Almost all of the derived hemodynamic parameters did not have normal distributions according to D'Agostino & Pearson omnibus tests, and so are reported as median and interquartile range (IQR, the first (Q 1 ) to third (Q 3 ) quartile), with percent variability reported as the quartile coefficient of dispersion [CoD = (Q 3 À Q 1 )/(Q 3 + Q 1 ) ]. While most input parameters (flow rates, etc. ) were found to be normally distributed, we chose to report them also using medians, IQR and CoD to be consistent with the statistics of the output hemodynamic parameters. These descriptive statistics were calculated for each case individually, but also based on teams' averages across the five cases, referred to as the ''case-average'' statistics. Where there might be missing data for one or more cases from a given team for a particular parameter, that team's case-average value was not included.
Thicker symbols highlight the teams that contributed CFD datasets from two different segmentations. Superimposed horizontal lines, boxes, and whiskers identify median, IQR, and 90th percentile ranges for each case. laws, reduced-order models, or did not specify. Despite the variability in outflow schemes, however, the division of outflow to the dominant branch was remarkably consistent (case-average CoD = 5%), with Case 4 having the highest variability (CoD = 16%) owing to the presence of three similarly-sized daughter branches (c. f. , two branches for the other cases). As a result, there were significant differences (p < 0. 0001) in median outflow divisions among some cases, notably Case 3.
Intra-team Variability Although the present study was not designed to systematically separate the influence of segmentation variability from boundary condition or solver variability, we note that two teams (19 and 35) each submitted two CFD datasets which differed only in terms of segmentation and/or smoothing, i. e. , the inflow/ outflow schemes and CFD solution strategies were the same within each team. For (high-experience) Team 19, automated vs. more intensive manual segmentations were performed, also with differences in the number and lengths of outflow branches. For (low experience) Team 35, two different segmentation software tools were used. As reported in Table  4 , segmentation generally had small influence on case-average MCA diameter, although for Team 35 differences could be as high as 11% for individual cases.
As a result, these teams were consistently among the outliers for LSA and LSA*. That rank-ordering of cases by the hemodynamic parameters (i. e. , Fig. 8 ) improved consensus suggests that, even if a team over-or underestimated flow rates or WSS, as long as it was being done consistently, the relative ordering of cases by some WSS parameter could be more robust. Finally, we do not mean to single out some of the above teams as the only outliers. Considering the 5 aneurysm cases and 14 (inflow, outflow, and sac) parameters investigated in the present study, every team had data points outside of the 10th-90th percentile range (i. e. , ''outliers'') for at least one of those 70 comparisons, and all teams were outside the IQR for at least 14 of those 70 comparisons. We do note, however, that low-experience teams contributed 43% of the ''outlier'' data points, compared to 40 and 17% from medium-and high-experience teams, respectively.
Looking Beyond IQR and CoD In this study, we focused on IQR and CoD as standard descriptive statistics for datasets having nonparametric distributions. This however, makes it more difficult to compare against the standard deviations (SD) and coefficients of variation (i. e. , CoV = SD/ mean) typically reported in the literature (albeit often without testing for normality). To give some context, CoD was 23% for case-averaged MCA flow rates, which could be considered negligible or at least tolerable in light of an early report that ± 25% variations in flow rate had only a modest impact of aneurysm flow patterns. 8 This, however, ignores that fact that IQR and CoD include, by definition, only half of the 28 datasets. Expanding to the 10th and 90th percentiles (the ''whiskers'' in Figs. 4  and 7 ) brings in 22 of the 28 datasets.
The resulting inter-decile range for MCA flow rates is 2. 29, greater, corresponding to a percent variability of 44%. Similarly, for case-averaged AWSS and AWSS*, the inter-decile ranges were 2. 29 and 3. 19 wider than their respective IQRs, corresponding to percent variabilities of 85 and 63%, vs. their respective CoDs of 48 and 18%. We therefore recommend some caution in relying solely on IQR and CoD as measures of variability, since they will tend to paint a more optimistic picture of the breadth of the variability. A good rule of thumb for our data would seem to be that 2 9 IQR or 2 9 CoD encompass the variability of most teams.
TIME DELAY AND MAGNIFICATION CALCULATIONS We estimate the arrival times and magnifications of putative future appearances of GW151226 and LVT151012 due to the three cluster lenses discussed in Section 3. The detailed mass models referred to above are all constrained by spectroscopically confirmed multiply-imaged galaxies, thus breaking the redshift space degeneracies. The mass distribution of each cluster core was modelled as a superposition of mass components that represent the large-scale cluster mass distribution, and the cluster galaxies, and optimized using the publicly available Lenstool software  (Jullo et al. 2007) , following methods initially developed by  Kneib et al. (1996)  and  Smith et al. (2005). Full details of the models are presented in  Christensen et al. (2012 ), Ho, Ebeling & Richard (2012 ), and Smit et al.
ACKNOWLEDGMENTS We thank an anonymous referee for comments that helped us to improve this article. GPS thanks  Alberto Vecchio  for numerous stimulating discussions on strong-lensing of gravitational waves. We also thank  Marceau Limousin ,  Christopher Berry ,  Keren Sharon ,  Danuta Paraficz , and  Ilya Mandel  for assistance and comments. GPS, MJ, and JR thank  Jean-Paul Kneib  and  Eric Jullo  for creating Lenstool and teaching us how to use it. We acknowledge support from the  Science and Technology Facilities Council  through the following grants:  ST/N000633/1  ( GPS, WMF ),  ST/P000541/1  (MJ), and  ST/K005014/1  (JV). MJ also acknowledges support from the  ERC  advanced grant LIDA, and RM acknowledges support from the  Royal Society. This work used the  LIGO Open Science Centre data releases  for  GW150914 ,  GW151226 , and  LVT151012 , with  DOI 10.
The capacity of ElrA-expressing E. faecalis to be internalized into macrophages was evaluated by determining the percentage of invasion as the ratio of internalized bacteria 1 hour post-infection (p. i. ) relative to the initial inoculum. Professional phagocytic Raw264. 7 macrophages engulfed 50% of the initial inocula of the wild type and P + -ΔelrA bacteria (Fig. 3 ). Strikingly, less than 20% of the P + -elrA-E bacteria were internalized, showing that, although forcing contact, the efficiency of P + -elrA-E bacteria adhesion or internalization was lower than that of the wild type. These data indicated that ElrA still confers an anti-phagocytic activity even when bacteria are in contact with macrophages. During infection, E. faecalis also encounters non-professional phagocytes. We investigated whether ElrA-mediated escape was a more general mechanism.
The migration assay was performed with bone-marrow macrophages (BMDMs) differentiated from C57BL/6 J wild type or FHL2 -/-mice. In both cases, we observed that BMDMs did not migrate toward ElrA-expressing E. faecalis (Fig. 5A ). These data substantiate the effect of ElrA in inhibiting sensing and migration of macrophages, albeit independently of FHL2. Because deletion of the FHL2-interacting domain of ElrA impacts infection in vivo  10  , we investigated the role of FHL2 during E. faecalis infection by challenging FHL2 -/-mice versus wild-type C57BL/6 mice upon intravenous infection for 24 h with E. faecalis. Both mice were challenged with OG1RF and the ΔelrA strain since ElrA is expressed in vivo  5. Different trends were observed depending on the organ invaded by E. faecalis (Fig. 5B ). FHL2 was found to play a role in defense against E.
To our knowledge, we report for the first time an inhibition of macrophage sensing triggered by an internalin-like protein. An inhibition of chemoattraction of macrophages towards bacteria has been observed Figure  3. Effect of ElrA-expressing E. faecalis on host cells. Exponentially growing E. faecalis strains were used to infect Raw264. 7 macrophages and placental cells (Jeg-3), hepatic cells (HepG2), epithelial cervical (Hela) cells, fibroblastic kidney cells (ACHN), colonic epithelial cells (Caco-2), ileocecal epithelial cells (HCT-8), and epithelial kidney cells (A-704). Percentage of invasion was determined as the ratio of intracellular bacteria to the initial inoculum. Data are represented as mean with SEM of at least 3 independent experiments for each cell line. Statistical analysis was performed using unpaired Student's t test.
Biofilms developed for 6 h were stained using SYTO 9 (cell permeant nucleic acids dye, Life Technologies) diluted 1:100. Images were acquired using a Leica SP8 confocal laser scanning microscope (Leica Microsystems). The green SYTO 9 emitted fluorescence signal was collected on an hybrid detector in the range 500-550 nm after excitation at 488 nm with an Argon laser set at 20% of its maximal intensity with a z-step of 1 µm and at 600 Hz. 3D reconstructed images were obtained using Imaris (Bitplane) software. Biomass was calculated directly from the raw images using Image J software and Comstat 2 plugin  34. Percentage of cohesion was calculated by normalizing the remaining biofilm after the three consecutive washes to the initial non-washed biofilm. Statistical tests were performed using unpaired Student's t test on GraphPad Prism.
Acknowledgements We thank  Emilie Doz-Deblauwe ,  Christine Longin ,  Marie-Anne Nahori ,  Javier Pizzaro-Cerda ,  L. Rigottier-Gois  and  Nathalie Winter  for technical advices or assistance and  C. Nielsen-Leroux  and  J. P. Nougayrede  for fruitful discussions. We are grateful to  Mélanie Hamon  and  Alexandra Gruss  for critical reading of the manuscript and fruitful discussions. We thank the animal care facilities (IERP, INRA, 78352 Jouy-en-Josas, France). This work has benefited from the facilities and expertise of MIMA2  MET-GABI (INRA, AgroParisTech , 78352 Jouy-en-Josas, France; www6. jouy. inra. fr/mima2). N. N. was supported by a fellowship from the  French Ministère de l'Enseignement Supérieur et de la Recherche. P. S. thanks the  French-Brazilian USP   COFECUB  project  Uc Me 147/13  for traveling support.
Primers were designed to coamplify different paralogs of the same gene. RT-qPCR assays were performed on the Roche LightCycler 480 II system (Roche Diagnostics, Neuilly sur Seine, France). The assays were carried out using a reaction mix of 6 μL per sample containing 2 μL of 76 times diluted cDNA, 0. 24 μL of each primer (10 μM), 3 μL of LightCycler 480 SYBR Green I Master mix and 0. 52 μL DNAse/RNAse free water (5 Prime GmbH, Hamburg, Germany). The PCR protocol was initiated at 95˚C for 10 min for initial denaturation of the cDNA and hot-start Taq-polymerase activation, followed by 45 cycles of a three-step amplification program (15s at 95˚C, 10s at melting temperature Tm (60-65˚C), 15s at 72˚C), according to the primer set used. Melting curves were systematically monitored (5 s at 95˚C, 1 min at 65˚C, temperature gradient at 0.
11˚C/s from 65 to 97˚C) at the end of the last amplification cycle to confirm the specificity of the amplification reaction. Each PCR assay included replicate samples (duplicate of reverse transcription and PCR amplification) and negative controls (RT-and cDNA-free samples, respectively). Relative quantification of target gene expression was determined using the E-Method from the LightCycler 480 software (version SW 1. 5; Roche Diagnostics). The NORMA-Gene algorithm not using housekeeping genes was used for the normalization by calculating the average deviation from a sample to the mean of specific genes  [66]  as the expression of the 3 reference genes tested (β-actin, EF1, 18 S) was not consistent between different sampling time points. In all cases, PCR efficiency (E) measured by the slope of a standard curve with serial dilutions of cDNA ranged between 1.
Forward primer Reverse primer Genoscope 1 or Genbank accession number Paralogue 1 Paralogue 2 Applied Biosystems, ThermoFisher) according to manufacturer's instructions. Synthetic cel-miR-39 mimic (miScript miRNA Mimics, QIAGEN) was added to each RNA sample and used as an exogenous control for normalization. It exhibited a constant level in liver samples at the different sampling time points. Briefly, 80 ng of total RNA were used for the poly(A) tailing, ligation and reverse transcription reactions to synthesize the cDNA of all miRNAs followed by a pre-amplification step. qPCR was then performed in a reaction mix of 6 μL containing 2 μL cDNA (200 times diluted), 2. 67 μL 2X Fast Advanced Master mix (Applied Biosystems, USA), 0. 27 μL TaqMan Advanced miRNA Assay (20X) (Applied Biosystems, USA) and 1.
Statistical analysis Results are expressed as means ± SD. Statistical analyses were carried out using one-way ANOVA to detect significant differences, followed by a Turkey test for post hoc analysis. Normality was assessed using the Shapiro-Wilk test, while homogeneity of variance was determined using Levene's test. In cases where data were nonparametric or not homoscedastic, data transformations, such as logarithms, were used to meet ANOVA criteria. For all statistical analysis, the level of significance was set at P<0. 05. Pearson correlation coefficients were calculated based on data of normalized genes or miRNA expression calculated by the E-Method from the LightCycler 480 software (version SW 1. 5; Roche Diagnostics). All statistical analyses were performed using R software  [67].
qPCR of il-1β qPCR was performed on 1dpi Mm infected (or PVP control) embryos as previously described  (35). The following primers were used: il-1β, accession number NM_212844, forward primer: GAACAGAATGAAGCACATCAAACC, reverse primer: ACGGCACTGAATCCACCAC, ppial control, accession number AY391451, forward primer: ACACTGAAACACGGAGGCAAG, reverse primer: CATCCACAACCTTCCCGAACAC.
Acknowledgements The authors would like to thank the  Bateson Centre Aquarium Team at the University of Sheffield  for fish care. We gratefully thank  Georges Lutfalla  ( Montpellier University ) for providing the Tg(mpeg1:mCherry-F)ump2Tg line, Lalita Ramakrishnan ( University of Washington, Seattle ) for M. marinum strains and  Astrid van der Sar  ( VU University Medical Center, Amsterdam ) for the pSMT3-mCherry vector. P. M. E. is funded by a  Sir Henry Dale  Fellowship jointly funded by the  Wellcome Trust  and the  Royal Society  (Grant Number  105570/Z/14/Z ). S. A. R. is funded by an  MRC   Programme Grant  ( MR/M004864/1 ). A. H. M. is funded by a  Smart Mix Program  of the  Netherlands Ministry of Economic Affairs  and the  Ministry of Education, Culture and Science. NVO  and HLW are funded by  British Heart Foundation (BHF)  project grant ( PG/13/80/30443 ) and  Biotechnology and Biological Sciences Research Council (BBSRC)  project grant ( BB/L000830/1 ).
Characterization of the Catalyst Layer Distribution The samples have been characterized by X-ray tomography followed by image analysis and 3D reconstruction. X-ray tomography was performed in the MATEIS laboratory at INSA-Lyon, on v|tome|x s apparatus manufactured by Phoenix X-ray, a GE company (Wunstorf, Germany). After 3D volume reconstruction, we obtain a 3D volume of 1300 × 1300 × 1300 voxels with a voxel size of 12 µm. Image processing analysis was performed using Matlab (version R2017b). The first step is to threshold images. In the example presented in Figure  2 , three grey level zones can be differentiated. As the catalyst fills only a few pixels, it is difficult to perform the thresholding automatically. Thus, the two threshold levels used to separate the three zones are selected manually by visualisation checking.
Images processing Many software are available to process and visualize 3D micro-tomographies, such as Avizo3D, myVGL, Paraview and ImageJ. However, our choice was to use the Python programming language which is widely used in the scientific world and can provide account to more specific developments. Indeed, the use of a programming language for tomography processing allows finer control and more complex analysis possibilities. Gouillart et al. [11]  show the ability of Python and the scikit-image library to analyse micro-tomography data. The goal of the images processing is the segmentation of the different types of inclusions in the material. The segmentation consists of partitioning the tomography into several regions, in which the voxels satisfy a certain homogeneity criterion, based for example on the gray level.
For the ROI analysis, the average PE of the BGN was extracted for each subject within a mask including caudate nucleus, putamen, and globus pallidus, from the Harvard-Oxford probabilistic atlas (threshold 30%) as a measure of FC. The analyses were then performed with SPSS (version 24. 0) to evaluate the variance in FC explained by the variables explored in our study. All independent variables entered into the regression models at the same time (enter method or forced entry). Further analyses were performed on the significant predictors on the single BG structures (with Bonferroni correction across structures). Whole-brain voxel-wise analyses of the BGN maps were performed to investigate the possible relationships between the explanatory variables and FC of the BGN with no restriction to the basal ganglia, to allow exploring possible associations also in cortical areas.
Acknowledgements This work was funded by the " Lifelong Health and Wellbeing" Programme Grant : " Predicting MRI abnormalities with longitudinal data of the Whitehall II Substudy " ( UK Medical Research Council :  G1001354 , PI: KPE), and supported by The HDH Wills 1965 Charitable Trust (PI: KPE), by  Monument Trust Discovery Award  from  Parkinson's UK  (LG, JK, MH), by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC)  based at  Oxford University Hospitals NHS Trust , and by the  NIHR Oxford Health BRC. NF is funded by the  Gordon Edward Small's Charitable Trust  (Scottish Charity Register:  SC008962 ). MK, ASM and KPE are supported by the  UK Medical Research Council  ( K013351 , PI: MK) and MK additionally by  NordForsk  and the  Academy of Finland  ( 311492 ).
IV. COMPARISON BETWEEN THE PROPOSED ESTIMATION METHOD AND SOGI BASED ESTIMATION METHOD To prove the superiority of the proposed AC voltage sensorless control algorithm, comparative study between the AN estimator and the recently developed SOGI based estimation method  [9] ,  [10]  has been performed. First, the SOGI method is presented. Thereafter, simulation tests are carried out in MATLAB/Simulink environment. The SOGI based estimation method uses the VF concept where a virtual AC motor is assumed from the behavior of the grid voltages and the AC filter. Then, the grid voltages are induced by a virtual magnetic flux  [9] ,  [10] ,  [17]. In α-β reference frame, the VF ϕ αβ can be estimated from the integration of the grid voltages as follows: The integration in (31) induces saturation and DC offset in the estimated VF.
V. RESULTS The laboratory setup used for experimenting the developed AC voltage sensorless control algorithm, illustrated in Fig. 4 , is presented in Fig. 8. It consists of a three-phase insulategate-bipolar-transistor based PWM rectifier with antiparalleling diodes. Two current sensors and one voltage sensor are used to measure two input currents (i a and i b ) and DC-link voltage V dc , respectively. It should be noted that, three voltage sensors are used for measuring the grid voltages. These measured voltages are not used in the closed-loop control scheme. Therefore, they are only used to perform comparison between the estimated grid voltages and the actual ones. The developed AC voltage sensorless control algorithm is implemented in MATLAB-Simulink environment and executed on a dSPACE DS1104 board using Euler resolution method.
Therefore, the acquisition of such signals allows the monitoring of the emotional and behavioral state of the human being. In our work, we will use four physiological signals (Galvanic skin response GSR, Blood Volume Pulse BVP, Electromyogram activity EMG and Respiratory Volume VR). Healey  [10]  used these signals during her thesis work to set up a database of physiological signals for eight emotions. Table  1  lists some studies on the detection of emotions using physiological signals.