dataesr/scientific-paragraphs-categorization huggingface dataset:
Immunophenotypic analysis Cells were washed, suspended in PBS containing 3% FBS and 0. 09% NaN 3 , and incubated with a panel of fluorochromeconjugated mAbs (obtained from BD Biosciences) specific for MDDC differentiation/maturation (anti-CD80, CD83 and CD38)  [16]. Isotype-matched Abs were used as negative controls. Cells were analyzed with a FACScan (BD Biosciences). Fluorescence data were reported as percentages of positive cells when the treatment induced the expression of the marker in cells that were negative (CD83). Median fluorescence intensity (MedFI) was used when the treatment increased the expression of the marker in cells that were already positive (CD80 and CD38).
Blots were washed in Tris-buffered saline 0. 1% Tween-20, incubated with horse-radish-peroxidase (HRP)-conjugated goat anti-rabbit IgG (Bio-Rad Laboratories, Hercules, CA) to reveal phosphorylated proteins or HRP-conjugated goat antimouse IgG (GE Healthcare, Little Chalfont, UK) to reveal control proteins. The proteins were developed with the enhanced chemiluminescence ECL reagents from Pierce (Rockford, IL). Densitometry was performed on scanned immunoblot imaging using Model GS-700 Imaging Densitomer (Biorad, Laboratories) and Multi-Analyst software to obtain OD. The relative OD for each band was calculated by normalizing the OD for the corresponding b actin OD.
Statistical analysis Statistical analysis was performed using the GraphPad Prism software (San Diego, California, US). For the overall comparison of median values, a statistical analysis, using Fridman test, was performed. For post-test pairwise comparisons, Wilcoxon matched pairs test, adjusting for 6 multiple comparisons (0. 05/6 = 0. 0083), was then used. P#0. 0083 was considered statistically significant.
These results indicate that, in our system, RSV triggers Figure  1. Impact of BPZE1 on phenotypic maturation of RSV-infected MDDC. MDDC were treated with BPZE1 (bacteria/cell ratio 100:1), RSV (MOI of 1) or both. After 24 h, cells were analyzed for the indicated surface markers associated with a mature phenotype. Mock-infected cells were used as control. Fluorescence data are reported as median fluorescence intensity (MedFI) for CD80 and CD38, and as percentage of positive cells for CD83. Values are expressed as medians with interquartile range of seven independent experiments performed with MDDC obtained from different donors. Statistical significant differences are indicated by * (P#0. 0083). doi:10. 1371/journal. pone. 0100166. g001 mainly a MyD88-independent pathway while BPZE1 a MyD88dependent pathway.
(2011) , the histone H3 using H3F -H3R primers  (Colgan et al. , 1998) , and the ITS region using ITS4  (Takabayashi et al. , 1998)  -A18S  (White et al. , 1990)  primers and the protocol by  Benzoni et al. (2011) , or alternately using 1S and 2SS primers  (Wei et al. , 2003)  and the protocol by  Kitano et al. (2014). Sequencing was carried out by Genomics and Bioscience and Technology Co. , Ltd, Xizhi City, Taipei County, Taiwan. Sequences obtained in this study have been deposited in EMBL, and accession numbers are listed in S1. Sequences were viewed, edited and assembled using CodonCode Aligner 4. 2. 5 (CodonCode Corporation, Dedham, MA, USA) and manually checked using BioEdit 7. 2. 5  (Hall, 1999). Alignments of the four separated datasets (three single gene trees and one concatenated) were carried out using the E-INS-i option in MAFFT 7.
0 (Guindon and    Gascuel, 2003), respectively. The best-fit substitution model for each locus was determined using the Akaike Information Criterion (AIC) as implemented in MrModeltest 2. 3  (Nylander, 2004)  in conjunction with PAUP4. 0b10 (Swofford, 2003). As most suitable models AIC selected the General Time-Reversible (GTR) model with a proportion of sites being invariable (+I) and the remainder following a gamma distribution (+I) for COI and rDNA, and the Kimura (K80) model with a proportion of invariable sites (+I) for histone H3. The Maximum Likelihood (ML) tree was calculated with PhyML and a total of 500 bootstrap replicates were performed to assess the robustness of each clade. Four independent Markov Chain Monte Carlo (MCMC) runs were conducted for 1. 4 × 10 7 generations for COI dataset (1.
7 × 10 7 generations for histone H3 and 4 × 10 7 generations for ITS region) with trees sampled every 100 generation for each analysis. The 25% first trees were discarded as burn-in, and posterior probabilities were estimated from the remaining trees in each run (10,500 remaining trees for COI, 12,750 for histone H3, and 30,000 for ITS region). To determine if the runs had achieved stationarity, we visualized log-likelihood scores and model parameter values across each run using Tracer 1. 5  (Rambaut and Drummond, 2007). Finally, the three single gene datasets were concatenated in a single partitioned alignment and the phylogeny was reconstructed using Bayesian Inference and Maximum Likelihood analyses. Four independent Markov Chain Monte Carlo (MCMC) runs were conducted for 2. 2 × 10 7 generations with trees sampled every 100 generation and the 25% first trees were discarded as burn-in.
The phylogram based on the concatenated (COI, histone H3, and ITS) molecular dataset shows high ML and BI supports at all key nodes (Fig. 9 ). Clade I sensu  Arrigoni et al. (2014)  contains all species of Lobophyllia and Symphyllia analyzed so far and all our sequences of A. rowleyensis and P. vitiensis. The latter two species group together in a strongly supported lineage (MLs = 100 and BIs = 0. 9) and their genetic boundaries remain unclear being indistinguishable from each other with these molecular markers. The average genetic distance of A. rowleyensis from P. vitiensis is 1. 1 ± 0. 2%,  while genetic variability within A. rowleyensis is 1. 1 ± 0. 3% and within P. vitiensis is 0. 7 ± 0. 2%. The majority of the other species in this clade, L. costata  (Dana, 1846 ), L. robusta Yabe, Sugiyama and Eguchi, 1936 , S.
Utility of the examined molecular markers The three single gene trees gave congruent phylogeny reconstructions (Figs S2-S4), however higher resolu-tion at the species level was achieved by the ITS region (Fig. S4 ). The best overall BI and ML support was obtained for the concatenated dataset (Fig. 9 ). The scleractinian COI gene is usually characterized by low evolution rate and consequently by an overlap of intraspecific and interspecific divergences that do not allow this marker to be used as a barcoding gene in the order Scleractinia  (Hellberg, 2006; Shearer and Croffroth, 2008; Huang et al. , 2008). The main exception to this general scenario in scleractian corals is Stylophora pistillata Esper, 1797, for which  Keshavmurthy et al. (2013)  detected four deeply divergent lineages corresponding to four particular geographic regions.
Acknowledgments Collection during the CoralCal1 and CoralCal4 campaigns (IRD Noumea) was possible thanks to  C Payri ,  J Butscher ,  A Arnaud ,  F Folcher ,  JL Menou , and the  R/V Alis Captain R Proner  and crew. We are grateful to  E Karsenti  (EMBL) and  É Bourgois  ( Tara Expeditions ) and the  OCEANS consortium  for sampling during the Tara Oceans expedition. We thank the commitment of the following people and sponsors who made this singular expedition possible: CNRS, EMBL, Genoscope/CEA,  VIB ,  Stazione Zoologica Anton Dohrn, UNIMIB ,  ANR  ( projects POSEIDON/ ANR-09-BLAN-0348 ,  BIOMARKS/ANR-08-BDVA-003 ,  PROMETHEUS/ANR-09-GENM-031 , and  TARA-GIRUS/ ANR-09-PCS-GENM-218 ),  EU   FP7  (MicroB3/No. 287589 ), FWO ,  BIO5 , Biosphere 2, agnès b. , the  Veolia Environment Foundation ,  Region Bretagne ,  World Courier ,  Illumina, Cap L'Orient , the  EDF Foundation EDF Diversiterre, FRB , the  Prince Albert II de Monaco Foundation ,  Etienne Bourgois , the Tara schooner and its captain and crew.
Using data from two of the Cluster spacecraft, we examine the spatial characteristics and temporal evolution of the substorm current wedge and compare this with observations of the magnetic field deflections on the ground. Our observations show that the SCW is azimuthally structured into a series of near north-south aligned current sheets with widths of 10-500 km, in stark contrast to the picture of east-west aligned current channels generally assumed to be linked with preonset and postonset aurora.
Cluster Observations of the Substorm Current Wedge Changes in the Cluster orbit mean that between 2007 and 2013 the spacecraft passed over auroral latitudes at altitudes as low as 3000 km  [Forsyth and Fazakerley, 2012]. A subset of these orbits "skimmed" the auroral oval, crossing several hours of MLT at near constant latitude. The Cluster Northern Hemisphere auroral crossing on 15 January 2010 was one such orbit. During the perigee pass of Cluster tetrahedron on this day, data were available from Cluster 1, 2, and 4 but not from Cluster 3. Figure  3a  shows the magnetic foot points in geographic latitude and longitude of Cluster 1 (black) and Cluster 4 (blue) along with the locations of magnetometer stations in Canada  [Engebretson et al. , 1995; Mann et al. , 2008; Russell et al. , 2008; Peticolas et al.
The gradients are calculated from data from the fluxgate magnetometers (FGM)  [Balogh et al. , 2001]  on the spacecraft. Magnetic field gradients shown in Figures  3d-3g  from Cluster 2 and Cluster 4 point up the page and magnetic field gradients from Cluster 1 point down the page. The dayside auroral data were obtained around 02:40 UT from a single FUV (N2 Lyman-Birge-Hopfield band short: 140-150 nm) image compiled from F16 Defense Meteorological Satellite Program (DMSP) Special Sensor Ultraviolet Scanning Imager (SSUSI)  [Paxton et al. , 2002; Zhang et al. , 2008]. Also shown is the white light aurora observed by the THEMIS ASI at RANK projected to an altitude of 110 km. Auroral brightness is grey scaled such that darker grey corresponds to more intense auroral emission. Cluster 1 and Cluster 4 passed over Greenland at approximately 02:30 UT and altitudes of 6300 and 4900 km, respectively, within the expected altitude range of the quasi-static auroral acceleration region  [Lindqvist and Marklund, 1990; Lu et al.
Data from the Plasma Electron And Current Experiment (PEACE)  [Johnstone et al. , 1997]  instrument (not shown) shows a drop in electron flux above 32 eV, showing that Cluster 2 passed through the polar cap between 02:00 and 19:20 MLT (02:28 UT and 02:44 UT). Given that Cluster 2 was in the polar cap, and the unavailability of data from Cluster 3, we limit this study to using data from Cluster 1 and 4. Under these restrictions, we are unable to determine current densities using the multispacecraft curlometer technique  [Dunlop et al. , 1988; Robert et al. , 1998 ] but instead use a single spacecraft technique (described in Appendix A). Figures  4a  and 5a  show the residual magnetic field perpendicular to the T96 model magnetic field in the northward (red) and westward (black) directions; Figures  4b  and 5b  show the field-aligned current densities per unit magnetic field calculated from the magnetic field data using a single spacecraft method (see Appendix A); Figures  4c-4e  and 5c-5e show the PEACE electron differential energy flux spectra in the downward (Figures  4c  and 5c ) and upward (Figures  4e  and 5e ) field-aligned directions and the difference in differential energy flux between the downward and upward directions (Figures  4d  and 5d ).
The net downward electron energy flux in the downward region extended to higher energies at Cluster 1, and the net upward electron energy flux in the upward region was weaker at Cluster 1 than at Cluster 4. Examining the currents observed by Cluster 4 and Cluster 1 by eye, we can find a number of features in the two time series that we interpret as the same mesoscale current sheets drifting apart or together as a function of time. Figure  6  shows the FAC density per unit magnetic field from (Figure  6a ) Cluster 1 and (Figure  6b ) Cluster 4, with eight mesoscale regions that are delimited by dashed lines. Figure  6c   currents in black overlaid by the Cluster 4 currents in blue, with the Cluster 4 currents having been shifted by -00:16:30 MLT, determined as the MLT shift which gives the maximum cross correlation between the two data sets.
The linear cross correlation between the field-aligned current data from the two spacecraft was poor (r = 0. 325); however, we interpret this as being due to temporal variations of the currents. Comparing the individual mesoscale current regions shows that the variations in the current sheets were nonuniform. The upward current sheets close to 22:00 MLT moved eastward by 16 min MLT but had approximately the same widths during the two crossings, whereas the upward currents at the eastern edge (00:00 MLT when observed by Cluster 1) moved and narrowed in MLT. The currents observed by Cluster 1 in the net upward region were stronger, suggesting an increase in the current strength over time but in the net downward current region the currents observed by Cluster 1 and Cluster 4 were comparable. Minimum variance analysis (MVA)  [Sonnerup and Cahill Jr.
, 1967; Sonnerup and Scheible, 1998 ] can be used to determine the orientation of the individual current sheets that make up the substructure of the SCW. From the magnetic field gradients we identified 26 small-scale current sheets in the Cluster 4 data and 34 small-scale current sheets in the Cluster 1 data. Figure  7   greater than 5 for 56 of the identified current sheets, indicating that the maximum variance direction was well defined in nearly all cases. Figure  8  shows the orientations and thicknesses of the individual current sheets as calculated using MVA (see Appendix A). In Figures  8a-8d , red indicates upward current sheets and blue indicates downward current sheets. Figure  8a  shows the angle of the current sheets away from the northward direction plotted Journal of Geophysical Research: Space Physics 10.
In contrast, the opposite sense currents (i. e. , upward current sheets in the net downward current region) were less well aligned with the north-south direction, leading to the peak in the upward current sheet orientations between -30 • and -60 • in the Cluster 4 data in Figure  8c. The orientations of the current sheets observed by Cluster 1 were less well ordered, particularly in the net downward current region. The Cluster 1 data in Figure  8c  shows an almost uniform distribution of downward current sheet orientations across the 30 • degree bins between +30 • and -90 •. The orientations of the upward current sheets still peaked within 30 • of the north-south direction. Comparing the Cluster 1 data in Figures  8a  and 8c  show that the orientations of the currents sheets had a greater spread of values in the net downward current region but were within ±45 • of the north-south direction in the upward current region.
The distribution of the thicknesses of the current sheets were similar between the Cluster 4 and Cluster 1 crossings. Both the upward and downward current sheet thicknesses peaked at 100 km (Figure  8d ), which maps down to ∼50 km in the ionosphere. The range of thicknesses of the downward current sheets was up to 200 km for both the Cluster 4 and Cluster 1 crossings, whereas the thicknesses of the upward current sheets extended up to 300 km. Figure  8b  shows that current sheets up to 200 km thick were observed in both the net upward and net downward current regions. The wide (200 km to 300 km) sheets were only upward current sheets and were only observed in the net upward current region. During the relatively short time between the two spacecraft crossings, the data show that the orientation of the current sheets became less well ordered, particularly in the downward current region.
Further observations are needed to examine the link between preonset auroral forms, substorm current wedge substructure, and late-substorm auroral perturbations. In this study, we have interpreted the magnetic field perturbations as being related to field-aligned current sheets that drifted dawnward and varied between the two spacecraft crossings. The magnetic field data from Cluster 4 and 1 could alternatively be interpreted as being due to wave activity, particularly in the net downward current region. In the upward current region, the electron distributions show evidence of quasi-static acceleration processes at both Cluster 4 and Cluster 1 and not the broadband acceleration associated with inertial Alfvén waves  [Chaston et al. , 2007; Lysak and Song, 2011; Marklund et al. , 2011] , strongly suggesting that the observations are of drifting current sheets, as opposed to being due primarily to Alfvén waves.
Conclusions Using a combination of multispacecraft observations from Cluster 1 and 4 during an AAR perigee pass, along with ground-based magnetometer and optical auroral data, we have provided new insights into the structure and temporal evolution of the substorm current wedge, revealing a plethora of north-south aligned current sheets at ∼100 km scales. Using data from Cluster 1 and Cluster 4 as they crossed auroral latitudes at 4000-7000 km, along with ground-based magnetometer and optical data, we have shown that the substorm current wedge consists of a number of upward and downward current sheets that, when spatially averaged, reduce to the simple line current model of the substorm current wedge of  McPherron et al. [1973]. During the two spacecraft crossings, we identified 34 and 26 current sheets in the data from Cluster 1 and Cluster 4, respectively.
Data acquisition and analysis system The Nexus32 TM QEEG system and a cap were used, with 19 electrodes, two references (an electrode under each earlobe close to retrahens Auricular), and 1 ground (an The sampling rate was 2,048 Hz. The high sampling rate was used to allow flexibility when analyzing event-related potentials. Subjects were prepared by first cleaning the electrode positions on the scalp using alcohol pads. The cap was then mounted, adjusted, and the electrodes were filled with appropriate amount of skin prepping gel. After ensuring that all signals are of high quality (electrode offset is between AE50; 000), the session starts. During the session, a screen indicating the quality of the signal for each electrode was used to monitor signal quality during data collection. A green color was used to indicate high quality signals, while a red color was used as an alarm for possible problems.
No alarm was flagged during any of the experiments reported in this paper. EEG analysis was done on a second-by-second basis then averaged over 30 s for each IVA-CPT block. The IVA-CPT responses were time-tagged and were saved on the computer. The same machine was used for EEG data collection to ensure appropriate clock synchronization between the EEG data and responses from IVA. All EEG signals were band-pass filtered between 1 and 42 Hz. Artifacts were visually detected from the phenotype of the signal. Matlab 2014 and Excel 2007 were used for the processing of the data and the generation of table summaries, including statistical analysis, respectively. The data acquisition and real-time analysis software was written in C3 and the EEGlab Matlab code (http://sccn. ucsd. edu/eeglab/) was used for visualization.
Conclusion The reaction time and performance of air traffic controllers were assessed using the IVA-CPT. Nineteen EEG channels using the 10-20 standard were used to monitor EEG during the task. Three main findings of this study can be summarized as follows: -Reaction time to visual targets was slower than to auditory ones and was positively correlated to performance on both visual and auditory targets. -The same brain areas that get activated in the events associated with correct responses to visual targets also get activated when incorrect responses occur. However, three additional areas are activated in the latter case. We hypothesize that alpha wave activities in the medial-more specifically F z and P z -can potentially be used to infer a wrong response. -While qualitative differences may exist when analyzing the EEG data using narrow bands, no quantitative advantage was seen in our analysis.
Therefore, the claim that narrow-band analysis will provide different results is questionable. The conclusion of this study for air traffic control is twofold. First, overloading air traffic controllers with visual cues can cause an increase in their workload because of the more complex pathways used in visual processing causing slower response to visual targets. Second, QEEG has the potential to be used as an auxiliary mechanism to monitor workload, and possibly detect incorrect reactions to stimuli during an ATC task. For future work, we will calibrate the results obtained from this study, where reaction time was analyzed, with the wider massive dataset we collected during the experiment. Further analysis will be conducted to gain insight into the performance of air traffic controllers through objective QEEG data.
VCM does not aim to achieve the same level of detail as natural language texts, but rather a broader level of detail. VCM icons can be used in medical applications for visually filtering information or for graphical summary. It has been implemented by Vidal®, the leader in drug databases in France, in its on-line guidelines a and it is used by Sherbrooke Health Expertise Center for e-learning. To allow this, the terminology used in the medical application has to be aligned to VCM language, i. e. each concept of the terminology has to be aligned to one or more VCM icon. For example, associating VCM icons with patient conditions coded in EHR with the tenth revision of the International Classification of Diseases (ICD10), requires iconic representation of each ICD10 code using VCM language. These alignments may also ease indexing and information retrieval, EHR visualization, as well as reading of Summary of Product Characteristics etc.
Acknowledgements We thank  Eric Kuflik  and  Neal Weiner  for helpful conversations, and  Howie Haber  for useful discussions and for a secret version of the spinor bible. AF is supported by the  ERC  advanced grant Higgs@LHC. The work of YH is supported by the  U. S. National Science Foundation  under Grant No. PHY-1002399. YH is an Awardee of the  Weizmann Institute of Science -National Postdoctoral Award Program for Advancing Women in Science. J. T. R. was supported in part by the  Miller Institute for Basic Research in Science.
The significant increase in luminosity allows the measurement of variables with three jets, not accessible with earlier data, and the latter four observables are analysed in CMS for the first time. Therefore this analysis is sensitive to features of the event generators that were not probed in the previous CMS result. The paper is organized as follows. In Section 2, elements of the CMS detector relevant to this analysis are described. Section 3 introduces the event-shape variables studied in this work. The data and MC simulated event samples are summarized in Section 4 along with the event selection criteria. Section 5 describes the unfolding technique employed and the propagated systematic uncertainties. Section 6 compares the five event-shape distributions in data with several QCD event generators.
Events of interest for this analysis should be well-balanced in p T and hence have jets on both sides of this line. Table  1  shows the numbers of events, as well as the fractions of events with two, three, four, or more jets, for various ranges of the leading jet p T,1 , along with the effective integrated luminosity for each data sample. The effective luminosities differ due to variations of the prescale factor of the trigger paths associated with each p T,1. The average number of additional pp interactions per bunch crossing (pileup) on the collected dataset is ≈8. The effect of pileup in the distributions of event-shape variables has been studied by grouping the events in different ranges of number of reconstructed primary vertices, and no bias has been found. This is expected due to fact that after the jet energy calibration, there is no residual pileup dependence.