dataesr/scientific-paragraphs-categorization huggingface dataset:
Impact of 100% HEFA-SPK on the apparent ice number emission index AEI With the quality-controlled data set described in previous sections (filtered for > 60% ice-NO y correlation), it is possible to compare AEI for Jet A-1 and HEFA-SPK. Figure  2  shows comparisons of AEI for Jet A-1 and HEFA-SPK (individual data points, medians and arithmetic standard deviations) versus a set of parameters relevant in ice particle formation. Figure  2  (a) relates contrail ice particle numbers to measured soot particle emission indices corresponding to the contrail measurement data while further panels compare AEI depending on the fuel parameters (b) naphthalene, (c) aromatics, and (d) sulfur. kg -1 to 6. 1 × 10 14 kg -1. Both fuels were probed at very similar conditions within a single flight, therefore it is reasonable to attribute these reductions to properties of the probed fuels, which are explored in the following.
This is also seen in the ICAO engine emissions data base, which delivers engines emission indices from different en- gines probed at four thrust settings for the landing-take off cycle. While to some extent, correlations between fuel constituents and apparent contrail ice particle emissions are suggested, other parameters such as engine cycle, combustion parameters, combustor design and atmospheric conditions may influence soot emissions as well. A direct cross-campaign comparison therefore 480 can give hints on trends but differences in ambient conditions and measurement setups impede a direct comparison based solely on the fuel effect as is done for HEFA-SPK and Jet A-1 in ECLIF3. Here, the reduction of 56% from the rigidly reduced data set within one flight can be seen as the reduction potential when a flight is conducted with 100% HEFA-SPK instead of Jet A-1.
The Nyquist plot (Figure  5c ) shows at high frequency, a semicircle associated with the bulk and grain boundaries resistivity followed by a fast increase of the impedance at low frequency related to the capacitive behavior of the blocking electrode. The fitting of the data using an equivalent electrical circuit leads to estimate a total ionic conductivity of 7. 00(4) Â 10 -4 S cm À1 at 25 °C close to the intrinsic bulk conductivity (3 Â 10 À3 S cm À1 ) reported for LLZO:Ta. [18, 19] The grain boundary conductivity σ GB =5(2) Â 10 À5 S cm À1 , estimated using Brick layer model,  [20, 21]  confirms not only their resistive character but also their weak contribution to the total conductivity. The electronic conductivity is determined from the resistance calculated using the current I measured at the steady state (when dI dt ! 0) after a DC voltage of 100 mV is applied (Figure  5d ).
X-ray diffraction (XRD) characterization is performed on both faces of the pellet as recovered, after polishing, and on the powder obtained after grinding. A Bruker D4 diffractometer or Bruker D8 Endeavor both using Cu Kα radiation (λ = 1. 5406 Å) were used to collect diffraction patterns over the range 2θ = 10-100°with a scanning speed of 0. 02 °sÀ1. Operando XRD during heating was carried out in an Anton Paar HTK1200N hightemperature chamber installed on the Bruker D8 diffractometer. The sample was heated up to 1000 °C with a heating rate of 30 °C min À1 and the XRD patterns were collected every 100 °C in the 2θ = 10-100°angle range. All XRD pattern analyses were performed using the FullProf software package. [13] he chemical composition of the samples was determined by inductively-coupled plasma emission spectroscopy (ICP-AES) using spectrometer Varian 720-ES optical emission spectrometer and estimation of the chemical formulae normalized to the La content.
Phase-differences were computed between each ROI pair (in this example between ROI A and B) at each time point (black circles in the figure). Using cosine similarity, we ranged the phase synchronies between -1 and 1; -1 indicating a complete phase opposition and 1 indicating a complete phase coherence between the two ROIs. Then, phase synchrony values were used to create a 42 by 42 interareal coordination matrix for each time-point (fMRI volume). Using k-means clustering, we classified the coordination matrices into 4 prototypical patterns. Finally, the fMRI data of each participant was expressed as a sequence of these 4 patterns. Stars indicate statistical significance after correction for multiple comparisons using the Bonferroni procedure. (B) Description of the differences between Pattern 2 (more frequent in movie condition) and Pattern 1 (more frequent in non-movie conditions).
0001, after Bonferroni correction) and scrambled movie watching (median ISI = 0. 01, S. E. = 0. 004, t(39) = 6. 74, p < 0. 0001), indicating higher co-occurrence when participants watched the movie (Fig. 3A ). We further validated these results with our second clustering procedure using the Harvard-Oxford atlas (Figure  S2B ). Consistently, the ISI values exceeded chance-level ISI only during the moviewatching condition (V = 100. p = 0. 021). In contrast, during both resting-state (V = 33, p = 0. 14) and scrambled movie watching (V = 57, p = 0. 18), the ISI values remained around zero. We predicted that the co-occurrence of the patterns would be more important during the most engaging parts of the movie, during which the movie plot captures the viewers' attention. We took advantage of the suspenseful nature of the movie and utilized a pre-existing dataset where a third group of 15 participants had rated the suspense of the scenes on an 8-item scale outside of the scanner 1.
Method Participants and procedure In this study, we used a previously published dataset  5  in which 27 participants underwent functional MRI recordings. During the acquisition, 15 participants (18-40 years; 7 males) watched an 8-min black and white movie clip taken from a TV show entitled "Alfred Hitchcock Presents-Bang! You're Dead". The same participants also went through a resting-state scan. A second group of 12 participants (18-30 years; 4 males) watched the same movie but in a scrambled order. In this condition, the movie was cut into 1-s segments and shuffled, allowing participants to see all the scenes without understanding the plot of the movie. Please note that these participants only underwent the scrambled movie condition and did not undergo any resting state scans. To evaluate how suspense varied in the movie, a third group of 15 participants (19-29 years; 5 males) watched the movie clip outside of the scanner and were asked to rate how suspenseful every 2-s segment of the movie was using an 8-point scale.
MRI acquisition parameters MRI data were acquired on a 3T Siemens Tim Trio System. T2*-weighted whole-brain images were recorded during resting-state (256 volumes), movie (246 volumes) and scrambled movie (238) watching with a gradientecho EPI sequence (33 slices, slice thickness: 3 mm, interslice gap of 25%, TR/TE: 2000 ms/30 ms, voxel size: 3 × 3 × 3 mm, flip angle: 75°). A mirror box allowed participants to see the movie that was presented on a projection screen behind the scanner. Noise cancellation headphones (Sensimetrics, S14) were also used for sound delivery. An anatomical volume was also acquired using T1-weighted MPRAGE sequence in the same acquisition sessions (154 slices, matrix size: 240 × 256 × 192, TE: 4. 25 ms, voxel size: 1 × 1 × 1 mm, flip angle: 9°).
fMRI preprocessing Raw MRI data were preprocessed and denoised using CONN functional connectivity toolbox  36  implemented in MATLAB (The MathWorks). The first 5 volumes were discarded to ensure stable magnetization. The preprocessing procedure included realignment, slice-time correction, outlier detection, segmentation, normalization into the MNI152 space (Montreal Neurological Institute), and spatial smoothing using a Gaussian kernel of 6-mm full width at half-maximum. For outlier correction, images with more than 0. 3 mm framewise displacement in one of the z, y, z directions, more than 0. 02 rad rotational displacement, or global mean intensity exceeding 3 standard deviations were included as nuisance regressors in the generalized linear model (GLM). White matter and cerebrospinal fluid masks were also included as nuisance parameters in the GLMs.
Time-varying functional connectivity patterns All computations were performed in MATLAB. Following the preprocessing, extracted ROI time-series were represented in the complex space using their analytic representation that composed by the original signal (real part) and the Hilbert transform of the signal (imaginary part). The instantaneous phase is computed as the inverse tangent of the ratio of the imaginary and real components and wrapped into the [-π, π] interval using the angle function implemented in MATLAB. This allowed us to have a time-series of instantaneous phases for each ROI. Note that to avoid edge artefacts, the first and last 9 time points have been discarded from the time series. Then, phase differences between each ROI pair (861 in total) were calculated for every fMRI volume using cosine similarity.
K-means clustering procedure K-means clustering is an unsupervised classification method allowing to divide a dataset into distinct, nonoverlapping clusters. We selected K-means over other clustering methods because it doesn't require assumptions about the data's shape and has been previously used in similar studies  [23] [24] [25]. The number of clusters, denoted as 'k' , is determined in advance. In our study, we conducted the clustering process five times, considering k values of 3, 4, 5, 6, and 7. Subsequently, we employed the Silhouette method with elbow criterion to identify the optimal number of clusters our analysis revealed that 4 clusters provided the optimal classification. The algorithm operates as follows: Initially, the algorithm randomly selects K data points from the dataset as the initial cluster centroids.
Statistical analyses Occurrence probabilities of the patterns and changes in the similarity index across conditions are assessed with linear models using lme4  39  , car  40  , and emmans 41 packages in R  42. Wilcoxon rank-sum tests on the suspense ratings and the temporal consistency values, as well as the Spearman's correlation between the suspense values and the entropy were performed on MATLAB (The MathWorks). All tests are corrected for multiple comparisons using the Bonferroni procedure except for the sign-tests on Pattern coherence levels which were corrected using the Benjamini-Hochberg procedure. We made this choice because our brain patterns consisted of 42 by 42 matrices, resulting in a total of 861 (42*41/2) values when comparing coherence values to zero. When dealing with such a substantial number of comparisons, applying the Bonferroni correction would have led to an exceedingly stringent significance threshold, potentially increasing the likelihood of false negatives.
Acknowledgements This study was funded by the  European Union  ( ERA PerMed   JTC2019  " PerBrain ", grant to JDS) and the  Canada Excellence Research Chairs (CERC)   program  (Grant No. 215063  to AMO). BT received a PhD grant from  French Ministry of Higher Education and Ecole Normale Supérieur. AMO is a Fellow of the  CIFAR Brain, Mind, and Consciousness program. Authors would like to thank  Garance Merholz  for her feedback on the manuscript and  Esteban Munoz Musat  for helpful discussions.
Purpose: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a predominantly type 2 inflammatory disease frequently coexisting with other type 2 conditions including asthma and non-steroidal anti-inflammatory drug-exacerbated respiratory disease (NSAID-ERD). Coexisting asthma leads to increased CRSwNP symptom burden. Dupilumab, a monoclonal antibody that blocks the shared receptor component for interleukin-4 and -13, demonstrated efficacy in adults with severe CRSwNP in the Phase 3 SINUS-24 (NCT02912468) and SINUS-52 (NCT02898454) studies, including in patients with coexisting asthma/NSAID-ERD. However, the impact of different asthma characteristics on dupilumab treatment in this population is unknown. We report CRSwNP and asthma outcomes with dupilumab in patients with CRSwNP and coexisting asthma according to baseline asthma characteristics.
16, 17 In the Phase 3 SINUS-24 (NCT02912468) and SINUS-52 (NCT02898454) studies in patients with severe CRSwNP, dupilumab significantly improved objective and patient-reported CRSwNP outcomes. 18 Additional post hoc analyses of the SINUS trials also demonstrated that dupilumab was equally effective in improving CRSwNP, asthma, and quality-of-life outcomes in patients with CRSwNP with or without self-reported asthma. 19, 20 However, the efficacy of different treatments can be influenced by the type of asthma, or by variation in other physiologic or immunologic characteristics. 21 The efficacy of dupilumab in patients with CRSwNP and varying asthma characteristics is currently unknown. Therefore, the aim of this post hoc analysis of the SINUS studies was to assess the effect of dupilumab on CRSwNP and asthma outcomes in patients with severe CRSwNP and coexisting asthma stratified by baseline asthma characteristics.
Study Design and Patients Data were included for patients with severe CRSwNP and coexisting asthma who received dupilumab 300 mg or placebo once every 2 weeks in SINUS-24 and SINUS-52; full details of these studies are published elsewhere. 18 Both study protocols were approved by 200 Copernicus Group IRB (Protocol # EFC14146, Tracking # SAN4-16-406; Protocol # EFC14280, Tracking # SAN4-16-417). Severe CRSwNP was defined in line with criteria from the European Forum for Research and Education in Allergy and Airway Diseases (EUFOREA), and in the eligibility criteria for the SINUS-24 and SINUS-52 studies. 18, 22 2] [13] [14]  Using baseline measures, patients were stratified into the following 5 groups, which were selected to represent relevant categories associated with the above asthma characteristics: 1) blood eosinophil levels ≥150 cells/µL or 2) ≥300 cells/µL (representing evidence of type 2 inflammation); 3) 5-item Asthma Control Questionnaire (ACQ-5) score <1.
12, 13 Interestingly, approximately one-third of patients with controlled asthma achieved the ACQ-5 MCID, which indicates that further Notes: a P values apply to LS mean difference vs placebo for Week 24 and Week 52. Data were analyzed using an ANCOVA model with change from baseline in the outcome at the corresponding visit as the response, and the corresponding baseline value, treatment group, prior surgery history, regions, and study indicator as covariates. b Other type 2 asthma characteristics, such as fractional exhaled nitric oxide or serum immunoglobulin E, were either not assessed or not captured for this analysis. c  The "all coexisting asthma" population comprised all patients from the original ITT populations of SINUS-24 and SINUS-52 who also had coexisting asthma (pooled for Week 24; SINUS-52 only for Week 52).
Acknowledgments This research was sponsored by  Sanofi  and  Regeneron Pharmaceuticals Inc. ClinicalTrials. govidentifiers:  NCT02912468  and  NCT02898454. Medical writing support was provided by  Olympia Gianfrancesco, PhD , of  Adelphi Group, Macclesfield, UK , funded by  Sanofi and Regeneron Pharmaceuticals Inc. in accordance with Good Publications Practice.
PROTACs consist of chimeric molecules that bridge any protein to an E3 ligase so as to induce its proteasomal degradation. There are currently several PROTACs in clinical trials for the treatment of cancer such as ARV-471 (breast cancer) expected to enter phase 3 soon or ARV-110 (prostate cancer) which is in phase 2  [65]. These PROTACs are also combined to other drugs like Everolimus for ARV-471 (NCT05501769) or Abiraterone for ARV-110 (NCT05177042) in some clinical trials in an attempt to improve the treatment of patients. Thus, multi-compound therapies represent promising tools for the management of hard-to-cure cancer patients and could apply to FASN in conjunction with inhibitors currently in clinical phase. FASN may also be responsible for the resistance to trastuzumab (Herceptin™) in HER2 overexpressing breast cancers.
PCR products were analyzed by capillarity electrophoresis on an ABl3130xl sequencer. The two-multiplex PCR system was tested and validated on eight reference P. triticina DNA extracts from isolates obtained from bread and durum wheat. Chromatograms were visually inspected for all markers and for all individuals with Peak Scanner software version 2. 0, before the final assignment of SSR alleles. The chromosome position of SSR markers was determined by performing a blastn analysis (with default parameter values) of the sequence of each primer against the two genome sequences of the Australian isolate Pt76  (Duan et al. , 2021)  with the NCBI blast tool (https://blast. ncbi. nlm. nih. gov/Blast. cgi). The genotyping of the 401 isolates revealed two major genotypes within each pathotype: 106 314 0-G1 and 106 314 0-G2 for pathotype 106 314 0, and 166 317 0-G1 and 166 317 0-G2 for pathotype 166 317 0.
triticina pathotypes expressed on 'neutral' and 'naive' cultivars Genotype 166 317 0-G2 was more aggressive than genotype 166 317 0-G1 in assessments of all three aggressiveness components on a 'neutral' cultivar, Apache, and a 'naive' cultivar Michigan Amber (in analyses of the pooled dataset for the two cultivars; Figure  6A, B  and C ). The data for assessments of aggressiveness components on both cultivars are provided in the supplementary data (Figure  S1 ). Infection efficiency (IE) was higher for genotype 166 317 0-G2 (53. 1 %) than for genotype 166 317 0-G1 (47. 6%). Sporulation capacity (SP) was also higher for genotype 166 317 0-G2, with 0. 120 mg/lesion versus 0. 114 mg/lesion for genotype 166 317 0-G1. Latency period (LP) differed significantly between the two genotypes; it was shorter for genotype 166 317 0-G2 (132.
6 vs 135. 0 degree-days). Significant differences were found between isolates of the two genotypes. Infection efficiency (IE), latency period (LP) and sporulation capacity (SP) did not differ between genotypes 106 314 0-G1 and 106 314 0-G2 assessed on cultivars Apache and Michigan Amber (in analyses of the pooled dataset for the two cultivars; Figure  6D, E  and F ). The data for assessments of aggressiveness components on both cultivars are provided in the supplementary data (Figure  S2 ). The IE for genotype 106 314 0-G1 was 43. 8%, which is not significantly different from the value of 43. 4% obtained for genotype 106 314 0-G2. SP was 0. 128 mg/lesion for genotype 106 314 0-G1, which is not significantly different from the value of 0. 124 mg/lesion obtained for genotype G2. Finally, LP differed by less than one degree-day between the two genotypes, at 133.
e. from inoculation to the spore dispersion) of the pathogen possible in a single season  (Rimbaud et al. , 2018). Although only latency period differed significantly between 106 314 0-G2 and 106 314 0-G1, the data for the other aggressiveness components also supported the notion that 106 314 0-G2 was more aggressive, for every component, on all cultivars, except for infection efficiency on Premio. Measurements of aggressiveness components on seedlings in controlled conditions (only one reproductive cycle of the pathogen) have a limited capacity for the detection of small phenotypic variations. The competitive advantage of one particular genotype within a pathotype, expressing a difference in fitness for the same virulence profile, may account for the replacement of one genotype with another.
This highlights the imperfections of the protocol linked to the constraints of working on datasets and biological material acquired over several decades with ever-changing techniques. It would have been better to pathotype and phenotype the 401 isolates from the same purified batch of urediniospores, as we did later on for the 44 isolates for which aggressiveness was characterized (Figure  1 ). However, given the size of the sample, this limitation did not prevent us from obtaining a correct overview of changes for the most common genotypes. Our results revealed rapid genotype evolution within the pathotypes that was not detectable if only virulence phenotypes were considered. These findings demonstrate the value of analyzing population dynamics not only from the pathotype standpoint, but also with genotype data, to obtain a more informative picture of pathogen diversity.
Acknowledgments We thank  Nathalie Retout  for technical assistance in the preparation of aggressiveness experiments. We thank the  French Wheat Breeders groups 'Recherches Génétiques Céréales' and 'CETAC' , and ' ARVALIS-Institut du Végétal'  for their help collecting strains of P. triticina from their nurseries and field trials. We thank  Dr. Julie Sappa  for editing the manuscript.
Objectives Interstitial lung disease (ILD) is a severe extra-articular manifestation of rheumatoid arthritis (RA). The objectives of this study were to estimate mortality rate in patients with RA-ILD and identify factors affecting mortality. Methods Data from a French national claims database (Système National des Données de Santé) from 2013 to 2018 were analysed. Adults with an RA diagnosis (International Classification of Diseases (ICD)-10 codes M05, M06. 0, M06. 8 and M06. 9) were included. ILD diagnosis was defined with ICD-10 code J84. Mortality rates were compared between patients with RA with and without ILD, using Cox proportional hazards regression, after matching 1:1 for age, sex, age at RA-ILD onset and RA duration. Results Among 173 132 patients with RA, 4330 (3%) also had ILD (RA-ILD).
INTRODUCTION Rheumatoid arthritis (RA) is a systemic inflammatory disease with an estimated global prevalence of 0. 5% among adults. 1  Extra-articular manifestations are observed in about 50% of patients with RA, with interstitial lung disease (ILD) being one of the most frequent. 2 3  Frequency estimates of ILD among patients with RA (RA-ILD) vary considerably in the literature, from 2% to 50%. [4] [5] [6] [7] [8] [9]  This variability is due to several factors, such as the lack of consensus regarding the definition of RA-ILD (ie, clinical vs subclinical ILD), heterogeneity of the tools used to assess ILD (eg, chest X-ray, high-resolution CT (HRCT), pulmonary function tests), heterogeneity of the RA population and different methodological approaches (ie, small cohort studies vs national healthcare database).
WHAT THIS STUDY ADDS ⇒ The current data highlight an increased mortality associated with RA-ILD compared with RA alone, for patients aged <75 years, in patients whose ILD preceded RA onset and in male patients. ⇒ RA treatment strategies are largely discordant by ILD status. RMD Open RMD Open RMD Open disease activity, lower baseline diffusing capacity of carbon monoxide and lower forced vital capacity and some chest HRCT scan features (usual interstitial pneumonia pattern, presence of fibrotic features and extension of abnormalities) have been associated with an increased mortality in patients with RA-ILD. 13-15 17 18  However, most of the studies dedicated to the evaluation of mortality in RA-ILD are small, retrospective case-control studies, thereby limiting interpretation of results. Existing healthcare databases provide accessible, realworld observations that can be used to advance our current knowledge of RA-ILD.
Study design and data source This retrospective analysis used the Système National des Données de Santé (SNDS), a French national healthcare claims database containing data from approximately 97% of the French population. 19  The SNDS database includes: (1) data regarding diseases, discharges, diagnoses and disability status allowing full expenditure reimbursement, coded using the International Classification of Diseases, 10th revision, Clinical Modification (ICD-10-CM) codes, (2) mortality date and (3) medical treatment information made available from drug reimbursement. Investigators had access to patient records as defined by the patient population below. Data from 1 January 2013 to 31 December 2018 were included in the current analysis. No data cleaning methods were required.