dataesr/scientific-paragraphs-categorization huggingface dataset:
2) attached to the coil. We used Praat software  (Boersma and Weenink, 2001)  to measure the response times for each item and each participant. Specifically, we delimited 160 periods of 2. 5 s from each audio file (one per participant) that correspond to the repetition time for scan acquisition. Within each identified scan, we delimited the beginning of oral response according to the audio-spectrogram (Figure  1 ). Response latencies were calculated based on the difference between the beginning of oral response and the beginning of the scan, given that pictures were presented at the beginning of each scan. Based on oral responses, the correct response rate (% of CR) and the mean reaction time (RT, in milliseconds) for correct responses were calculated. The run lasted 7. 06 min.
Functional MRI Acquisition The study was performed in a whole-body 3T MR scanner (Philips Achieva) with a 32-channel head coil. For functional scans, the manufacturer-provided gradient-echo/T2 *weighted EPI method was used. Forty-four interleaved axial slices parallel to the bi-commissural plane were acquired. Slice thickness was 3 mm. The in-plane voxel size was 2. 3 × 2. 3 mm (220 × 220 mm field of view acquired with 88 × 85 pixel data matrix, reconstructed with zero filling to 96 × 96 pixels). The main sequence parameters were TR = 2. 5 s, TE = 30 ms, and flip angle = 80 •. A T1-weighted high-resolution three dimensional anatomical volume was acquired, via a 3D T1 TFE sequence (field of view = 256 × 240 × 160 mm; resolution: 0. 89 × 0. 89 × 1 mm; acquisition matrix: 272 × 250 × 160 pixels; reconstruction matrix: 288 × 288 × 160 pixels).
Cerebral activation during object naming Pre-processing. Preprocessing was performed using SPM12 (Welcome Department of Imaging Neuroscience, London, UK 1 ) implemented in MATLAB 2014 (Mathworks Inc. , Sherborn, 1 http://www. fil. ion. ucl. ac. uk/spm/ MA, USA). As the use of a study-specific template (SST) is recommended for aging studies to avoid methodological bias attributable to morphometric inter-individual differences  (Huang et al. , 2010; Fillmore et al. , 2015) , we first created an SST. The T1-weighted anatomical volumes were coregistered to the mean image created by the realignment of the functional images and segmented via DARTEL using the six tissue probability maps. We generated an SST by matching all of the tissue class images provided during the segmentation step, and then normalized this template to the MNI space.
In this study, we investigated the detailed kinematics and relationships between the growth behaviors and starting sizes of clones in Arabidopsis sepals. First, we investigated the relationship between the initial sizes of the clones and their growth rates in developing Arabidopsis sepals. Here, a clone refers to the progenitor cell and all of its descendants, and hereafter we use 'an initially small (or large) clone' for a clone descended from a small (or large) progenitor cell. We tested whether the sizes of the clones within the sepal become more uniform (size uniformization) or more variable (size variability enhancement) over time. Live imaging data from two laboratories (five wild-type sepals), previously reported in  Hervieux et al. (2016) , were considered. In this study, cells were outlined with plasma membrane markers and the entire sepal was imaged every 12 h or 24 h.
We considered the growth of the entire clone as a unit, and ignored divisions of cells within the clone. The growth of individual cells will be discussed in the section headed 'Individual cell growth heterogeneity is positively correlated with the growth of clones at each time step'. To extract the outline and follow the growth of clones, we used analysis and visualization software, MorphoGraphX (MGX) (Barbier de Reuille et al. , 2015; see Materials and Methods), for cell segmentation, lineage tracking and area calculations. We defined the clone area at time t as A t , and calculated the relative areal growth of the clone as (A t+Δt -A t )/ A t ×100 (%) (Fig. 1 ). The growth patterns in all analyzed sepals were qualitatively similar (Fig. S1A-D ). Our live imaging of the sepals initiates shortly after the sepal primordia have formed on the flanks of the floral meristem, when the cells are relatively small in size and have not yet differentiated into specialized epidermal cell types.
Plant material and imaging by confocal microscope The five wild-type sepals were sampled from different laboratories (wt-a1, wt-a2: two sepals imaged every 12 h at ENS Lyon, France; wt-b1, wt-b2, wt-b3: three sepals imaged every 24 h at Max Planck Cologne, Germany). We reanalyzed data already used in  Hervieux et al. (2016) , in which the average sepal growth pattern was quantified. In the a1 and a2 series, we used the pUBQ10::myrYFP line kindly provided by Raymond Wightman (Sainsbury Laboratory, Cambridge University, UK). In this line, myrYFP corresponds to a YFP that is N-terminally modified with a short peptide that is myristoylated and probably acylated  (Yang et al. , 2016). Plants were grown under long-day conditions. Staging was determined as indicated in  Smyth et al. (1990). Main inflorescence stems (1-2 cm long) were cut from the plant.
We also have lineage information combining a data set observed at different times. In this study, we first detected a cell outline at the first time frame and kept track of the outline in which cells have the same lineage as the initial cell. The growth in a clone can be calculated as the total cell area of a clone at time t+Δt over the total cell area of a clone at time t, i. e. A t+Δt /A t. Bootstrap method to estimate the first and third quartiles of the CV We calculated the first and third quartiles of the CV using the bootstrap method. The bootstrap method can be summarized as 'random draw with replacement', which allows us to measure the accuracy of the statistics. For the sake of a general description, suppose that we have an observed valuable X (the sample number is N) and its CV (standard deviation of X/mean of X).
We first create a 'bootstrapped data set X B '. That is, we randomly draw samples N times from data set X with replacement and set it as X B-1 for the first trial. We continue in the same way and create the data sets (X B-1 , X B-2 , …, X B-1000 ). Using these data sets, we can obtain a bunch of 'bootstrapped CV' (CV B-1 , CV B-2 , …, CV B-1000 ). We then calculate the first and third quartiles of the distribution of the bootstrapped CV.
The fluorescent cDNAs were separated by capillary electrophoresis and the electrophoregrams were analyzed with the QuShape software. In the presence of K+, no specific RT stop was observed for Anxa2 RNA in the predicted G4 forming sequences whereas strong stops are observed in the well-characterized N19 fragment from the Fmr1 RNA (Supplemental Fig. 2 ). The folding of the region encompassing the G-motif into a G-quadruplex structure was also investigated by computa-tional analysis using the Vienna RNA package, which allows the prediction of the formation of G-quadruplexes alongside other possible competing secondary structures (http://rna. tbi. univie. ac. at;  Lorenz et al. 2013). The calculations executed by this package confirmed that the G-motif of Anxa2 does not form a stable G-quadruplex structure in contrast to a control sequence known to adopt a G-quartet conformation  (Menon and Mihailescu 2007; Lorenz et al.
Partial conservation of the G-motif in Anxa2 mRNAs from different organisms Given the peculiar position of the G-motif at the end of the coding region of the Anxa2 mRNA in mouse, we tested whether this motif is also present in the Anxa2 transcripts of human and other organisms. To this end, we performed multiple sequence alignment using the MAFFT server, and as shown in Figure  4A  and Supplemental Figure  1 , the G-motif is strongly conserved in human, but only partial conservation can be observed evolutionarily. Interestingly, the G-motif region in human does not present G-quadruplex features when scanned by the QGRS-mapper (Supplemental Fig. 1 ). Folding analyses performed with the RNAfold (http://rna. tbi. univie. ac. at/cgi-bin/RNAWebSuite/ RNAfold. cgi) and mfold (http://unafold. rna.
Database searches of the 18-nt-long motif did not allow identification of additional mRNAs carrying this primary sequence, neither in mouse nor in human. However, we cannot rule out that the size of the motif required for axonal localization is shorter than 18 nt. If so, database searches with shorter sequences should lead to the identification of mRNA subsets sharing the same motif. Along this line, it is interesting to point out that the localization of the FL-mut4 reporter, where the first 3 nt of the G-motif are mutated, is significantly decreased as compared to that of the wild-type reporter (Fig. 5 ). This indicates that the cis-acting sequence required for axonal localization is likely located in the 5 ′ part of the G-motif. Further experiments should help to identify the trans-acting factor(s) responsible for Anxa2 mRNP localization.
Image acquisition and processing Images were acquired on a DMRA microscope equipped for epifluorescence and with a 100× PlanApo objective. Digital images were recorded with a 12-bit C4795-NR CCD camera (Hamamatsu). The camera and microscope were controlled by MetaMorph software and images were treated as previously described  (Rage et al. 2013). For quantitative analyses of the RNA granules and SMN protein colocalization, we used the Spot function of Imaris (Bitplane), which allows to consider only the signal present in foci on three-dimensional images  (Rage et al. 2013). The significance of the colocalization data was determined with the unpaired Student t-test (Prism software). Image acquisition and analysis were performed on workstations of the Montpellier RIO Imaging facility of the CNRS campus.
ACKNOWLEDGMENTS This work was supported by the  CNRS  and by grants of the ARSLA to F. R. and the  AFM  (no. 17760 ) to J. S. We thank  Edouard Bertrand  and members of his team for helpful discussions. We also thank the  Montpellier RIO Imaging facility  for help with the imaging experiments. K. R. was supported by a fellowship of the  Lebanese Association.
Data sources and searches We searched MEDLINE (since 1966; Supplementary Table  1 ), EMBASE (since 1980), CINAHL (since 1982), and the Cochrane Central Register of Controlled Trials (CENTRAL). We also searched reference lists of identified articles and review articles. We included relevant studies selected in our previous systematic review on risk factors for severe outcomes from influenza (search up to  March 25, 2011 [3] ), and updated the search using the same search strategy through April 25, 2014 (Fig. 1 ).
Study selection Studies reporting on pregnancy as a risk factor for the following severe outcomes following influenza: community-acquired pneumonia, death from all causes or related to influenza, hospitalization from all causes or related to influenza, admission to an intensive care unit (ICU) related to influenza, and/or need for mechanical ventilatory support. Study designs included observational studies with a comparator arm of non-pregnant patients with evidence of influenza virus infection. Ecologic studies, also included, were defined as studies that collected data at a group rather than at an individual level, or in which numerators or denominators were imputed or estimated. Non-English language articles were excluded in the search update, based on the limited value demonstrated in the first search  [3].
Data extraction and quality assessment Two reviewers independently screened titles, abstracts and full text articles, extracted data using a standardized and pilot-tested database, and assessed risk of bias. Any disagreement between reviewers was resolved by consensus or arbitration by a third reviewer. We used the Newcastle Ottawa Scale (NOS) to assess risk of bias for individual-level studies  [4]. With this scale, a maximum of 9 points was allocated in four domains: a maximum of 4 points for selection of study groups, 2 points for comparability of groups, and up to 3 points for ascertainment of exposure and outcomes. In order to evaluate publication bias, funnel-plots were made if ten or more studies had been included. The overall quality of evidence was assessed using the recently published GRADE framework for evidence about prognosis  [5].
Risk of bias and grading the quality of evidence Individual-level studies achieved an average of 6 out of a maximum of 9 points on the Newcastle-Ottawa Scale (interquartile range 6-7, range 3-8) (Supplementary Table  1 ). There was no evidence of publication bias in Funnel plots (data not shown). We identified several biases that may have overestimated risk estimates in ecological studies. For all ecological studies, the number of pregnant women was estimated rather than known. Also, the comparator group included both patients with and without influenza in 8/10 ecological studies (80%). Three ecological studies (30%) included outcomes occurring during the postpartum period. Two studies each (20%) relied on passive surveillance systems or were based on enhanced surveillance for at-risk populations.
950 Studies excluded in full-text screening/data collection: Records identified through database searching: Additional records identified through EMBASE n = 13,408 other sources MEDLINE n = 10,013 From bibliographies of relevant studies GLOBAL HEALTH n = 4,962 and review articles CINAHL n = 1,586 n = 0 CENTRAL n = 67 TOTAL n = 30,036 of records after duplicates removed n = 19,980 # of records after duplicates with original search removed n= 17,013 # of records meeting eligibility criteria for full text # of records excluded based on screening of titles screening; n = No original data provided n = 39 and abstracts; n = 16,063 # of studies included from original search * No comparison of interest reported n = 669 n = 63 Not meeting diagnostic criteria n = 3 (57 cohort, 1 case control, 5 Other reasons for exclusion n = 6 ecological studies) Language other than English n = 144 # of articles excluded n = 861 # Cohort or # Case control design # Ecological ‡ cross-sectional design † ‡ n = 4 n = 10 [3] 138# of studies with data collected n = 152 (89 + 63 from original search † )Fig.
2. 3. 3 at NLO accuracy in QCD for zero, one, two additional jets, merged with the FxFx scheme  [66, 75] , using the NNPDF30_nlo_as_0118 PDF set  [76]. This MG5_aMC@NLO_FxFx sample is interfaced to Pythia 8 for Higgs boson decay, parton showering, hadronization and multiple partonic interactions using the A14 parameter set  [77]. The data are also compared to ggF SM Higgs boson production in the 4 decay channel simulated with HRes v2. 3  [63, 78] , using the MSTW2008 NNLO PDF set  [79]. The HRes program computes fixed-order cross sections for ggF SM Higgs boson production up to NNLO in QCD and describes the p T,4 distribution at NLO. All-order resummation of soft-gluon effects at small transverse momenta is consistently included up to next-to-next-to-leading logarithmic order (NNLL) in QCD, using dynamic factorization and resummation scales (the central scales are chosen to be m H /2).
The program implements top quark and bottom quark mass dependence up to next-to-leading logarithmic order (NNL) + NLO in QCD. At NNLL + NNLO accuracy only the top quark contribution is considered. HRes does not perform parton showering and QED final-state radiation effects are not included. Both the MG5_aMC@NLO_FxFx and the HRes predictions are normalized using the LHCXSWG cross section. A ggF sample used to study deviations from the SM predictions within the pseudo-observable framework  [15, 80]  is generated with MadGraph5 at LO using FeynRules 2  [81]  and the NN23PDF PDF set. The sample is interfaced to Pythia 8 using the A14 parameter set. It is normalized using the LHCXSWG cross section. The ZZ ( * ) continuum background from quark-antiquark annihilation is simulated with Sherpa 2. 2  [82] [83] [84] , using the NNPDF3.
The fiducial cross sections are shown separately for each decay channel, and for sameand opposite-flavour decays. The inclusive fiducial cross section is measured as the sum of all channels, as well as by combining the per-channel measurements assuming SM ZZ * → 4 branching ratios. The LHCXSWG prediction is accurate to N3LO in QCD for the ggF process. For the fiducial cross-section predictions, the LHCXSWG cross sections are multiplied by the acceptances determined using the NNLOPS sample for ggF and the samples discussed in Section 3 for the other production modes. For the total cross section, the cross-section predictions by the generators NNLOPS, HRes, and MG5_aMC@NLO_FxFx are also shown. The cross sections for all other Higgs boson production modes XH are added. The error bars on the data points show the total uncertainties, while the systematic uncertainties are indicated by the boxes.
Conclusion Measurements of the inclusive and differential fiducial cross sections of Higgs boson production in the H → ZZ * → 4 decay channel are presented. They are based on data extracted from 36. 1 fb -1 of √ s = 13 TeV proton-proton collisions recorded by the ATLAS detector at the LHC in 2015 and 2016. The inclusive fiducial cross section in the H → ZZ * → 4 decay channel is measured to be 3. 62 ± 0. 50 (stat) +0. 25  -0. 20 (sys) fb, in agreement with the Standard Model prediction of 2. 91 ± 0. 13 fb. The inclusive fiducial cross section is also extrapolated to the total phase space which includes all Standard Model Higgs boson decays. Several differential fiducial cross sections are measured for observables sensitive to the Higgs boson production and decay, including kinematic distributions of the jets produced together with the Higgs boson.
Good agreement is found between the data and the predictions of the Standard Model. The extracted cross-section distributions are used to constrain anomalous Higgs boson interactions with Standard Model particles using the pseudo-observable framework. Hong Kong SAR, China; ISF, I-CORE 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, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, 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, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, ERDF, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Région Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom.
Acknowledgements 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; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; SRNSF, Georgia; BMBF,  HGF , and  MPG , Germany; GSRT, Greece; RGC,.
Of course, spatial bias and/or trial-by-trial fluctuations in the direction of spatial attention may well also contribute to the choice of which disk appears brighter on a given trial. Thus, for each subject we divided all trials into two datasets based on their spatial choice (left-side choice vs. right-side choice), and we only investigated the relation between pre-stimulus EEG phase and 3D-shape/random-lines choice within each dataset. As the correlates of choosing the prediction-consistent stimulus (3D-shape) were expected to be strongest on electrodes contralateral to that stimulus, which would map onto opposite hemispheres for the two datasets, before plotting any scalp topographies we permuted the electrode positions (symmetrically across the midline axis) of all right-side choice trials.
This procedure resulted in a mapping of ipsilateral effects to the spatial choice onto left electrodes, and contralateral effects onto right electrodes. We estimated the relation between EEG phase and predictive coding via the phase opposition product (POP, see Methods). This measure should be maximal when 3D-shape choice trials (prediction-consistent) and random-lines choice trials (prediction-inconsistent) tend to have opposite phase values. For each subject, dataset, electrode, time point and oscillatory frequency, we obtained surrogate POP values (80,000,000 surrogates) by randomly permuting the trial outcomes, keeping the number of trials constant. Both real and surrogate POP values were averaged across datasets and subjects. The significance was determined as the proportion of surrogate POP values that were more extreme than the observed value.
P-values were corrected for multiple comparisons across time points, frequencies and electrodes (100 × 30 × 64) using the FDR method (FDR α = 0. 05, corresponding to a P value threshold of 9. 53 × 10 -6 ). To show the overall POP in the time-frequency domain, a z-score was computed by comparing the real POP values (combined across all subjects, datasets, and electrodes) to the mean and standard deviation of a null-hypothesis distribution with 10,000 surrogate POP values (generated using the same procedure described before, and also combined across all subjects, datasets, and electrodes). This analysis revealed a significant phase-behavior relation between the post-stimulus 3D-shape/random-lines choice and two pre-stimulus oscillations (Fig. 2A ): one theta-frequency oscillation (~3. 1 Hz to 7.
To quantify the influence of pre-stimulus oscillations on post-stimulus choice, we binned single trials according to the phase at the optimal time-frequency point (for the theta oscillation: -397 ms, 5. 4 Hz; for the beta oscillation: -68 ms, 16. 5 Hz). Single trials were thus sorted in 13 phase bins based on the average phase of the significant electrodes for each oscillation (four frontal electrodes for the theta oscillation, three occipital electrodes for the beta oscillation). For each phase bin we then computed the post-stimulus choice probability of the 3D-shape disk. These choice probabilities were normalized by dividing them by the overall 3D-shape choice probability across all phase bins. For each experimental dataset (left-vs. right-side choice), phase bins were rotated such that the phase at which 3D-shape disk choice probability was largest was aligned to a phase angle of zero.
) We then focused on the remaining variability in luminance judgement as a trial-by-trial marker of the efficiency of predictive coding. On the other hand, systematic spatial biases (e. g. a general tendency to respond to the left or right stimulus) and/or trial-by-trial fluctuations in the direction of spatial attention can also be expected to affect the luminance judgement  26. As a matter of fact, spatial attention itself appears to involve a periodic process  12, 18, 27, 28  which could potentially influence the luminance judgement. We carefully avoided these potential confounding factors by dividing the trials into two datasets based on the post-stimulus spatial response (left/right) and performing the analysis within each dataset. If spatial attention biases, for example, were the only cause of the perceived luminance changes, the left-response dataset would pool all trials with a left-side attention bias (and similarly for the right-response dataset), and within each dataset pre-stimulus oscillatory phases would not bear any relation to post-stimulus luminance judgments.