dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors acknowledge helpful discussion with  F. Gastaldello ,  S. Molendi , and the members of the  ByoPiC  project  16. We thank the referee for the useful comments. This research has been supported by the funding for the  ByoPiC  project from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  grant agreement  ERC-2015-AdG 695561  and  CNES. This publication used observations obtained with XMM-Newton 17 , an  ESA  science mission with instruments and contributions directly funded by  ESA Member States  and  NASA. This research has made use of data obtained from the  3XMM XMM-Newton  serendipitous source catalog compiled by the 10 institutes of the  XMM-Newton Survey Science Centre  selected by  ESA. This research has made also use of the application package CIAO (v4.
032). 11 This suggests that the benefits of sacubitril/valsartan observed in patients with HFrEF in PARADIGM-HF  12  could extend to patients with mildly reduced (i. e. lower than normal) LVEF and to women. In RCTs, inclusion/exclusion criteria are applied to ensure enrolment of the targeted study population, to enrich for modifiable events, and to reduce the risk of competing events, and in PARAGON-HF, to ensure the presence of HF, because the diagnosis can be difficult in the presence of a preserved or normal LVEF. The strict selection criteria might reduce external validity of trial findings, affecting drug labels, guideline recommendations, payer reimbursement, and clinical implementation. The PARAGON-HF trial had numerous selection criteria, in particular the presence of left atrial enlargement (LAE) or left ventricular hypertrophy (LVH), which are often unavailable in clinical datasets.
Statistics Eligibility according to PARAGON-HF in KaRen was assessed in the whole cohort, separately in women and men, and separately in LVEF ≤ 57% and >57%. Calculations are reported as number and percentages representing the remaining cohort after applying the respective inclusion/ exclusion criteria. The criteria were not ordered and were not mutually exclusive; that is, the percentage shown for each criterion is the percentage eligible considering only that criterion, disregarding all other criteria. Because there were some missing data for variables to be considered for eligibility assessment, and to avoid bias due to data missing not at random, the main analysis was per-formed on an imputed dataset. Missing data were handled by multiple imputations using the R package 'mice' 17 with 10 datasets generated and 10 iterations.
Variables included in the imputation models are specified in Supporting Information, Table  S2. For the eligibility calculations, because one specific value is needed for each patient, the median was used for the continuous variables and the mode for categorical variables. If multiple modes existed, the lower value was used. For consistency, eligibility calculations were also performed on a complete case dataset, where only patients with non-missing information for all eligibility variables were included and in patients in whom missing values for eligibility variables were assumed to be eligible for inclusion. Patient characteristics were compared in eligible vs. noneligible patients by Wilcoxon-Mann-Whitney test and χ 2 test for continuous and categorical variables, respectively. The eligibility analysis was performed considering four scenarios: (i) a trial scenario, where all the PARAGON-HF selection criteria that could be investigated in our dataset were applied, (ii) a pragmatic scenario including only those PARAGON-HF trial criteria that are more likely to influence real-world treatment decisions, that is, inclusion criteria: age ≥50 years, New York Heart Association (NYHA) II-IV, and elevated NT-proBNP criteria (defined as in the trial), and exclusion criteria: systolic blood pressure <110 mmHg, estimated glomerular filtration rate (eGFR) <30 mL/min/ 1.
Eligibility according to four scenarios Eligibility based on (i) PARAGON-HF trial criteria, (ii) PARAGON-HF pragmatic criteria, and symptomatic HF (NYHA II-IV) and LVEF below normal assessed as (iii) LVEF below lower limit of normal range and (iv) LVEF below mean of normal range in the whole cohort and separately in women vs. men and in LVEF ≤ 57% vs. >57% is presented in Figure  1  and Supporting Information, Table  S2. Overall, in the imputed dataset, considering both inclusion and exclusion criteria, 34% were eligible according to the trial scenario, 63% according to the pragmatic scenario, 5. 4% according to the LVEF below lower limit of normal range criteria, and 41% according to the LVEF below mean of normal range criteria. Applying only the inclusion criteria, eligibility in the trial scenario was 71% and in the pragmatic scenario 78%.
Consistency analysis In consistency analyses, patients with and without missing data did not substantially differ in eligibility. Calculations on the complete case dataset (n = 84) displayed a slightly higher eligibility applying the trial scenario (39%) and similar applying the pragmatic scenario (62%), the LVEF below lower limit of normal range criteria (8. 3%), and the LVEF below mean of normal range criteria (52%) compared with the analyses on the imputed dataset (34%, 63%, 5. 4%, and 41%, respectively). As expected, eligibility in patients with missing eligibility data assumed as eligible (n = 425) was slightly higher, 38% according to the trial scenario, 70% according to the pragmatic scenario, 22. 4% according to the LVEF below lower limit of normal range criteria, and 51% according to the LVEF below mean of normal range criteria (Supporting Information, Table  S2 ).
Conflict of interest C. H. reports consulting fees from Novartis and Roche Diagnostics and speaker and honoraria from MSD, supported by the Swedish Research Council (Grant 20180899). C. L. reports research grants, speaker honoraria, and consulting fees from Medtronic, Abbot, Impulse Dynamics, Novartis, Bayer, Vifor, MicroPort, Boston Scientific, AstraZeneca, and Orion Pharma. G. S. reports financial support from Novartis for the current research; grants and personal fees from Vifor and AstraZeneca; grants and non-financial support from Boehringer Ingelheim; personal fees from Società Prodotti Antibiotici, Roche, Servier, GENESIS, Cytokinetics, and Medtronic; and grants from Boston Scientific, outside the submitted work. L. H. L. reports personal fees from Sanofi and Lexicon, during the conduct of the study; personal fees from Merck, Bayer, Pharmacosmos, Abbott, Medscape, and Myokardia; grants and personal fees from Vifor Fresenius, AstraZeneca, Relypsa, Boehringer Ingelheim, and Novartis, outside the submitted work; and grants from Boston Scientific.
To statistically assess if SHI effects were significant, k 0 -values from experimental data were compared against a null distribution generated by repeating the same analysis but with shuffled tone order within each sequence (k 0 shuff ), corresponding to the null hypothesis that there is no systematic integration of previously presented tones. Widespread sensor clusters exhibiting significant SHI effects were identified for all tone duration conditions and across all time windows (see Supplementary Fig. 4 ). Next, we quantitatively assessed the dependence of neural SHI underlying prediction on stimulus presentation rate, and adjudicated between the informational bottleneck and temporal bottleneck hypotheses. Prediction relies on integrating a stable number of tones across stimulus presentation speeds.
4 -III, red: duration line corresponding to Hypothesis 1; blue: information line corresponding to Hypothesis 2). To test these hypotheses, we investigated where neural data lie in relation to these two hypothesis-derived orientation lines. To this end, we adopted two complementary approaches. First, we tested sensors within predictive processing clusters (i. e. , sensor clusters showing a significant p * 34 prediction effect) to focus on neural SHI underlying sensory prediction. Second, we made no a priori sensor selection and tested the two hypotheses across the entire sensor array (with cluster-based permutation test to correct for multiple comparisons) to check whether there is positive evidence for each hypothesis. To focus on SHI underlying the p * 34 prediction effect, comparison of k 0 -values across different tone duration conditions was performed for predictive processing clusters defined from the 300 ms tone duration data (analysis schematic in Fig.
Methods Participants. Twenty six healthy right-handed subjects with normal hearing took part in the experiment. Seven subjects were prescreened for behavioral performance in a training session prior to the MEG recording. Six subjects were excluded due to either poor performance (i. e. , not using the full range of the rating scale) or excessive MEG artifacts, yielding a final group of 20 subjects (11 females; mean age 25. 0, 19-34 y). A reduced version of the behavioral and neural prediction results obtained from the 300 ms tone duration condition of this dataset was previously reported as an independent replication of main results in ref. 25. The study was approved by the Institutional Review Board of the National Institute of Neurological Disorders and Stroke (protocol #14-N-0002). All subjects provided written, informed consent.
Sphericity was tested with Mauchly's test and sphericity violations were corrected by means of Greenhouse-Geisser correction. For the seven subjects who underwent behavioral prescreening, results reported in Figs. 2  and 6  used their behavioral data from the training session, ensuring that assayed task performance was always based on the initial task encounter. For the remaining subjects who performed the task only during MEG recording, behavioral data from the MEG session were used. Data calculation based on behavioral data from the MEG recording session for all subjects produced qualitatively similar results for all analyses. MEG data acquisition and preprocessing. Whole-head neuromagnetic activity was recorded during the task with a 275-channel CTF MEG scanner (VSM MedTech, Coquitlam, BC, Canada).
Three malfunctioning sensors were excluded from analysis (leaving 272 channels in total). Scans were completed at a sampling rate of 600 Hz, with an anti-aliasing filter applied at <150 Hz. Custom-written MATLAB scripts and the Fieldtrip Toolbox (http://www. fieldtriptoolbox. org/; 53 ) were used for all preprocessing and analyses steps. MEG data from each block were demeaned and detrended. No high-pass filter was applied, in order to retain lowfrequency information (see refs. 25, 29 ). A Butterworth band-stop filter for 58-62 Hz and 118-122 Hz was applied to remove line noise. Independent component analysis was applied on filtered data to remove artifacts due to eye blinks and ocular motion, heartbeat, breathing, and movement-related slow drift. Computing neuromagnetic correlates of prediction.
05. Significant sensor clusters are referred to as predictive processing clusters. To visualize neuromagnetic activity during p 33 as a function of p * 34 , trials were binned into low, medium, and high p * 34 for each subject. For each timepoint, neuromagnetic activity was averaged across each group of trials for sensors within a predictive processing cluster, and then averaged across subjects. The time course of predictive information buildup in slow arrhythmic neural activity over the course of the tone sequence presentation was investigated by comparing time-resolved neural activity time-locked to tone presentation between sequences converging on low vs. high p * 34. Sensor-wise time-locked neural data were low-pass filtered at 35 Hz and subtracted by the mean amplitude from a 500 ms time window preceding the first tone.
Based on these average M100 amplitude values, the relative contribution of each sensor towards the overall signal was determined. The resulting sensor weights were multiplied with the nonsquared neuromagnetic data and subsequently averaged across sensors, yielding a weighted spatial filter for each subject. Neural activity projected through these spatial filters highlight tone processing in auditory cortex areas, referred to as early sensory filter. Computing neuromagnetic correlates of SHI. Effective prediction requires accumulation and integration of past sensory history information (SHI). To assess SHI, we analyzed the dependence of neural activity during the second half of a tone sequence on the pitch of preceding tones (Fig. 4 ). p 33 was excluded since it was constantly presented at 440 Hz and p 34 was excluded since its pitch was determined independently of the preceding tone sequence.
Model selection was based on the minimized sum of squared errors. The winning k 0 -value indicates how many previous tones in a sequence best explain the recorded MEG activity per sensor, time window, and tone duration. After computing k 0 for all folds, we averaged the k 0 -values across folds to calculate the final k 0 -value computed for experimental data for each subject, at each sensor and time window. Statistical significance of sensor clusters showing SHI effects was assessed by means of a nonparametric permutation test comparing k 0 against a null distribution based on shuffled tone order. By shuffling tone order within each sequence, the temporal dependence between neural activity and tone sequence was destroyed, in line with the null hypothesis that there is no systematic integration of previously presented tones.
To assess significance of the (across-subject) average k 0 -values against this null hypothesis, we repeated the abovementioned crossvalidation regression analyses but with randomly permutated tone order within each sequence in the training set, while leaving tone order in the test set unperturbed. The order of the first 32 tones in the training set was shuffled, with the exception that the value of the current (i-th) tone pitch was kept the same as in the original sequence. Thus, information about current tone pitch was retained, whereas information about tone sequence history was destroyed. This procedure was repeated 100 times for each dataset (i. e. , for each of the six folds, separately for each tone duration condition and each subject). Tone pitch order was shuffled differently for each of the nine unique tone sequences, but was kept constant across the 12 repetitions of each unique tone sequence.
Importantly, to enable a valid comparison across-tone duration conditions, shuffle order for each unique sequence was preserved across tone duration conditions. Tone order was shuffled anew for every test/training fold combination. Identical to the computation of k 0 -values, the same six-fold cross-validation technique was applied to extract the optimal k 0 -value computed for shuffled data (k 0 shuff ) for each sensor and time window in each tone duration-specific dataset per subject. This yielded a shuffled null distribution of 600 k 0 -values per sensor, time window, tone duration condition, and subject. k 0 shuff -values were subsequently averaged across folds to yield 100 shuffled values per subject. Effects at the group level were assessed by first averaging k 0 -values across subjects for each sensor and time window within each tone duration separately.
05. To allow for an easier visualization of data in relation to the two hypotheses, we additionally projected k 0 -values onto a two-dimensional plane (k 0 2ÀD ), defined by the two hypothesis-derived orientation lines. To this end, we computed the crossproduct (Eq. (  5 )) of ṽdur and ṽinfo , resulting in the vector ÑdurÀinfo normal to a plane spanned between ṽdur and ṽinfo. Next, we projected both k 0 -and k 0 shuff -values for each sensor and time window from the 3-D space to the nearest point on this plane (Eq. (  8 )). k 0 2ÀD ¼ k 0 3ÀD þ jð0 À ∑ðk 0 3ÀD : Ã ÑdurÀinfo Þ=∑ð ÑdurÀinfo : Ã ÑdurÀinfo Þj Ã ÑdurÀinfo ð8Þ k 0 -and k 0 shuff -values projected to this 2-D space were plotted, and vector norm and angle in the 2-D space were computed in the same manner as in the 3-D space. Effects of vector norm and angle were also analyzed across the entire sensor array.
Vector norm and angle were computed for each sensor and for time windows from 0-150 ms. Sensor-and time window-specific norm and angle derived from k 0 -values were compared against a null distribution computed from k 0 shuff -values. Cluster correction was applied to determine significant sensor clusters. Correlating behavioral and neural sensory history tracking effects across subjects. Finally, we tested whether neural and behavioral effects of SHI correlated across subjects. To increase analytic power and to assess general task performance, we computed correlations for data reflecting general effects across tone durations, resulting in one data point per subject. For each subject, we used the F-statistic for the interaction effect of p * 34 and p 34 derived from a three-way repeated-measures ANOVA (factors: tone duration, p * 34 , p 34 ) to indicate how strongly sensory history affected a subject's prediction responses.
Acknowledgements This work was supported by the  Intramural Research Program  of the  National Institute of Neurological Disorders and Stroke (National Institutes of Health) , an  Irma T. Hirschl Career Scientist Award  and a  Klingenstein-Simons Neuroscience Fellowship  (to B. J. H. ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 795998  ( MSCA-IF-GF  awarded to T. J. B. ).
The experimental setting in the urban and peri-urban sub-catchment of Oued Fez The urban and peri-urban sub-catchment of Oued Fez covers an area of 397 km 2 (Fig. 1b ). Since 2008, our team has collected discharge and rainfall data in this zone. In September 2008, a stream gauge was installed at the catchment outlet. The water level is automatically recorded every five minutes by means of a pressure transducer (PDCR 1830, 700 mbar, Druck) and subsequently converted into discharge using a rating curve established by our research team. Over the 2008-2017 period, three rating curves were established. The first one was valid from 01/10/2008 to 30/11/2010, the second one from 30/11/2010 to 01/01/2014 and the last one has been valid since 01/01/2017. Eight stream flow gauging campaigns were used to establish the first two rating curves, which were obtained by fitting a second-degree polynomial.
6 mm. h -1 for dt = 5 min) and discharge between 7. 9 and 38. 7 m 3. s -. Of the events in our sample, 1% occurred in the summer. The most important flood events in terms of runoff depth and peak flow are recorded in winter. To investigate the catchment's hydrological behaviour and signature, we investigated the rainfall-runoff relationship  (McMillan et al. , 2017; Addor et al. , 2018). Hydrological signatures may be calculated based on a variety of data sources, but in practice, they are usually based on discharge time series  (Addor et al. , 2018). Since we only have streamflow measurements and simulations at the catchment outlet, we have carried out a comparison on the rainfall-runoff relationships between 2008-2012 and 2017-2018 in order to determine the hydrological similarity or changes across the two periods.
The models The ATHYS modelling platform (http://www. athys-soft. org; Bouvier and Delclaux, 1996)  was chosen for this work. It is a spatially distributed hydrological modelling platform which includes several production functions that assimilate the soil to a linear reservoir. The reservoir is fed through infiltrated rain and the drainage losses represent both evaporation and percolation. A portion of the drainage losses may contribute to sub-surface flow (Fig. 5a ). A literature review of the available models in the ATHYS platform was carried out in order to identify the ones most adapted to the urban and semi-arid nature of the catchment. Another important consideration was the data available on the study zone: since we did not have access to data on the soils' hydrodynamic properties, we had to resort to empirical models.
Calibration phase The investigation of the models' performance is based on the Nash and Sutcliffe criterion (NSE is presented in Fig. 8 ). The boxplots represent the distribution of the criterion for the hourly data (2008-2018, 26 events) and 5-minute time steps  (2011-2018 data, 17 events). They indicate that the results of the simulations are not spread out. At the hourly time step, the median NSE values range between 0. 73 and 0. 85 and the percentage of events having NSE > 0. 6 varies from 84 % to 88 %. Thus, the models' performance can be considered satisfactory. When comparing the ranking of the four models, it can be seen that the SCS-LRm model is the most appropriate for the calibration database, while the Reservoir3-LRn model shows a lower performance and has the lowest NSE median values.
The same study showed that reducing the time step has the opposite effect under high flow conditions. This finding is consistent with our results, as the rainfall-runoff events simulated at the 5-minute time step are characterised by higher flow conditions than those modelled at an hourly time step. The models' capacity to reproduce the catchment's hydrological response was assessed by analysing the relationship between rainfall and simulated runoff for all events (Fig. 11 ). This analysis was carried out at an hourly time step to be able to use the largest available dataset. It can be seen that the linear relationship is correctly reproduced for the 2008-2011 events, with slightly higher values for the correlation coefficient (0. 81 ≤ R 2 ≤ 0. 85) and, save for the Reservoir3-LRm model, similar values for the slope of the regression line (0.
Effect of rain gauge network density and rainfall event type on the models' performance The modelling results were further investigated to highlight any possible effect of the rain gauge network density on the models' performance. The rainfall events were divided into two groups: single gauge events, i. e. events recorded when only a single rain gauge was implemented, and multiple gauge events, i. e. events recorded when two or more rain gauges were implemented. The analysis was only carried out at the hourly time step because our database did not contain any single gauge events at the 5-minute time step and only 5 events for which rainfall was measured using two gauges. When examining the models' results based on the rain gauge density using the NSE criterion, it was found that, for the calibration phase, all the tested models reproduce the observed hydrographs well when rainfall information is provided by two or four rain gauges (Fig.
Thus, the median values of the calibrated parameters converge towards the optimum values of the 2008/2012 period, which constitutes 65 % of the total number of events in our database. We further wanted to investigate the influence of the rainfall type. We used the 75th and 25th percentiles value of total rainfall to separate the events. All events where the total rainfall was below the 25th percentile were considered as low rainfall events and those where the total rainfall was above the 75th percentile were considered as high rainfall events. The results at the hourly time step (Fig. 12b ), both in calibration and validation, indicate that the selected models are able to reproduce the high rainfall events (0. 72 < NSE median < 0. 78) and perform less well on low rainfall events (0. 63 < NSE median <0.