dataesr/scientific-paragraphs-categorization huggingface dataset:
MATERIALS AND METHODS Nine stable patients monitored under NIV (PSV mode with a backup frequency) in the Pneumology Unit Care of the Corporació Parc Tauli (Sabadell, Spain) were included in this study. The data were randomly selected from a study focused on the prevalence of AEs. This study was approved by the local ethics committee of Corporació Parc Tauli (CIR2010/015). Written informed consent was obtained from patients. Nine expert physicians from France, Germany, Italy, The Netherlands, Spain, Turkey, and the United Kingdom reviewed the automatic scorings. Two restrictive patients, one obstructive patient, three patients with chronic obstructive pulmonary disease (COPD), and three patients with amyotrophic lateral sclerosis (ALS) were selected. They were ventilated with different devices: three with a Vivo 40 (Beas Medical, Mölnlycke, Sweden), four with a Lumis 150, one with an Astral 150 (ResMed, North Ryde, Australia), and one with a Trilogy (Philips Respironics, Murrysville, USA).
Each patient used a full-face mask. The waveforms were recorded in napping patients in their most comfortable position during the initiation of mechanical ventilation or during routine controls in a quiet room. Flow Q, airway pressure P aw , and belt waveforms B thorax and B abdom were measured. The data were acquired using an external polygraph (Powerlab 16Sp, ADInstruments, Sydney, Australia), equipped with a pressure transducer (model 1050, ADInstruments, Sydney, Australia) and a pneumotachograph (model S300, instrumental dead space 70 ml, resistance r p = 0. 0018 cmH 2 O/l/s, ADInstruments, Sydney, Australia), both inserted in the ventilation circuit close to the mask and with respiratory inductance plethysmography belts (Pro-Tech, Canada). The polygraph was connected to a computer equipped with data acquisition software (Chart 7.
0, ADInstruments, Sydney, Australia). The sampling frequency of measurements was f s = 1, 000 Hz, but the data were then resampled at the frequency f V = 64 Hz, as used by SyncSmart software. The acquired data were read by the SyncSmart software in a text format. For each patient, we recorded a long session of NIV. One 20min window of data was extracted from each of the recordings: The selected data window starts at least 5 min after the beginning of the session to avoid transient patient-ventilator interactions as commonly observed. No other criterion was used to select these windows. The belt signals, only used by the expert physicians to review the automatic scoring produced by the SyncSmart software, were filtered to remove the long-term drift and to improve their readability. A typical excerpt of the four measured waveforms with the total (intentional and non-intentional) leakage computed by SyncSmart is shown in Figure  1.
The SyncSmart software is analyzes the pressure P aw and the flow Q, either measured by the ventilator or by the external sensors as in the present protocol. The SyncSmart software considers three ventilator settings: expiratory positive airway pressure (EPAP), inspiratory positive airway pressure (IPAP), and the backup frequency (f bck ) at which the ventilator delivers the pressure cycles. The software computes a leakage = 1 t end -t init t end t init Q(t) dt (1) where t init is the time at which the inspiratory, effort is initiated and t end is the time at the end of expiration (t end is also the time at which the next inspiratory effort is initiated). According to this equation, the leakage has a constant value over each cycle. This leakage is used to discard the excessively turbulent parts of the waveforms.
As exemplified in Figure  1 , it is impossible to reliably score these cycles, neither by visual inspection by expert physicians nor by an automatic algorithm. The corresponding ventilatory cycles are thus marked with a red cross and are discarded from the statistical analysis. The events scored by SyncSmart software are shown in Figure  2  and are defined as follows. DT Double-triggered ventilatory cycles for which there are two pressure rises during one inspiratory effort; AT Auto-triggered ventilatory cycles when the pressure rise is not triggered by an inspiratory effort and thus occurs during the expiratory phase; IE Ineffective efforts when there is a patient inspiratory effort, which is not followed by a pressure cycle delivered by the ventilator; Bck Backup cycles when the pressure rise is triggered by the ventilator according to the backup frequency f bck ;  N Ventilatory cycles are considered normal when there is absence of one of the events described above.
The asynchrony index (AI) is defined as AI = 100 N DT + N AT + N IE N tot ; (2) where N tot is the number of ventilatory cycles (N, Bck, DT, AT, IE, and discarded cycles). AI is expressed as a percentage. All the physicians have been working with NIV for 21. 5 ± 7 years and, consequently, are considered as experts with NIV. All of them were asked to independently review the automatic scoring of PVA produced by SyncSmart according to their skills and knowledge. Expert physicians were reviewing the scoring by visual inspection of the pressure and flow as used by the SyncSmart software in addition to the belt signals as recommended by Gonzales-Bermejo et al. (6)  and Longhini et al. (32) , providing a reliable gold standard scoring. Since there is a long experience in sleep scoring for reviewing automatic scoring, we chose to reproduce the corresponding methodology: starting from the automatic scoring, we asked each expert physicians to individually correct it, and then, we compared the corrected scoring among each other and with the automatic scoring  (33) (34) (35).
The software contains a functionality that allows the physician to review the automatic scoring and to correct the type of ventilatory cycle. The physician may also add or remove a ventilatory cycle. Since the expert physicians have the belt signals, they can identify UAO which may be defined by the decrease in patient flow during a pressure cycle delivered by the ventilator  (6). By definition, there is no effective IE and the pressure rises are triggered by the backup frequency f bck : The SyncSmart software thus scored these ventilatory cycles as Bck cycles. Consequently, backup cycles scored as obstruction by physicians are retained as Bck cycles for the statistical analysis. Physicians were thus asked to focus on the main PVA events, that is, on IE, AT, and DT. Physicians were moderately under "time pressure" since they had approximately 2 min to review 1 min of tracing.
RESULTS Characteristics of the patient at enrollment and ventilator settings are reported in Table  1. Five patients were well-established on mechanical ventilation, and four were recently initiated. A total of 4,201 ventilatory cycles were analyzed. The nine physicians performed the analysis of the flow, pressure, and belt waveforms using the SyncSmart graphical interface. The number of AEs as scored by SyncSmart and as scored by the experts is reported for each patient in Table  2. On average, the SyncSmart software reported the same smaller numbers of DT (4. 1 ± 3. 6 vs. 6. 6 ± 13. 0, p = 0. 31) and IE (24. 1±21. 7 vs. 22. 0±21. 5, p = 0. 39) as the by experts. Conversely, the automatic scoring reported significantly more AT than the experts (71. 7 ± 88. 0 vs. 25. 5 ± 35. 3, p = 0. 0036).
The Se, Sp, PPV, κ-coefficient, and agreement are reported in Table  3  in which the results provided by SyncSmart software are successively compared with each expert, and where each expert is compared to one another. The AI assessed by SyncSmart is not significantly different from the at assessed by the experts (18. 9 ± 17. 7% vs. 12. 8 ± 9. 4%, p = 0. 19) as shown in Figure  3. Se and Sp provided by SyncSmart for N, Bck, DT, and IE are significantly greater than The mean numbers of AE according to the expert physicians are also reported with the SD between parenthesis. The breathing frequency f breath (in breath per min) as evaluated by the SyncSmart software is also reported. S, SyncSmart; E, experts; Ntot, total number of detected ventilatory cycles in the automatic scoring (s) and the mean number of ventilatory cycles once the scoring is reviewed by the experts (E); IE, ineffective effort; AT, auto-triggering; DT, double-triggering; AI, asynchrony index; Bck, backup cycles; X, discarded from the analysis due to too high leakage; mod, modified by the expert; f breath , breathing frequency.
To our knowledge, this pilot study is the first attempt to the assess performance of software to automatically detect DT, AT, and IE from the pressure and flow waveforms. In most of the previous studies, only IEs were investigated  (24, 25, 27, 31). These different algorithms were detecting IEs with Se and Sp greater than 0. 90 as observed with SyncSmart. One study investigated DT in 67 patients but there is no information on the validation of the algorithm  (29). Two studies investigated AE without distinguishing them. One reported the AI with a Se and a Sp at about 0. 90  (28). The second one investigated the asynchrony between patient and ventilator  (30). The latter study was, in fact, devoted to neurally adjusted non-invasive ventilator (NAVA), and the algorithm uses the diaphragm electrical activity, pressure, and flow waveforms.
So, its purpose was different from our pilot study. None of these studies, whose main characteristics are reported in Table  5 , investigated AT, which is, as revealed in this study, the most difficult AE to determine, even when the belt signal is provided. In the study of Mulqueeny et al. (31) , DTs were defined as two pressure cycles separated by less than 500 ms. This definition does not allow to distinguish a DT from a combination of AT-N. Distinguishing these two types of AEs is indeed sometimes very difficult, thus explaining the low Se found for these two types of AE, from the SyncSmart software as well as from the experts. In DT, the first oscillation of the flow has an amplitude that is larger than the second one  (7, 37). During AT, the patient is still expiring: the inspired volume should be therefore smaller than for that normal cycle, thus inducing lower amplitude flow waveforms  (7, 37).
Limitations of the Study There are possible limitations in this study. The first being that the experts were not blind to automatic scores and had, in fact, to reviewed them. This may create a bias by reducing the inter-rater variability compared to scoring from raw tracings. Nevertheless, even with this bias, the statistics (κ-coefficients and agreement as reported in Table  3 ) are still in favor of automatic scoring since the expert scoring better matches with the automatic scoring than with each other. Indeed, the modified scores very often differ from one expert to the other. With blind scoring, the results becomes intractable as we observed in a preliminary study (nonpublished), leading us to adopt the procedure commonly used to assess the performance of automatic sleep scoring. As the aim of the present pilot study is to evaluate the SyncSmart software as a proof of concept, further study is warranted to evaluate it for longer NIV sessions and for a larger cohort of patients.
There is not yet a standardization for coding AEs for patients using NIV. There are few contributions in that direction which are based on visual inspection  (6, 7, 41)  and, consequently, which use qualitative arguments that may sometimes lead to subjective interpretation. Another problem is related to obstructions that are not considered in the present study. Two of the patients have a noticeable number of obstructions. These events were merged with backup cycles. In this study, as it is considered by the SyncSmart software, the ventilatory cycles are discarded (< 5% of the number of breaths) from the analysis when leaks are too large: This avoids inappropriate tracings that could prevent physicians from correctly detecting PVA. The presence of high leakage is to be considered as a primary event during NIV, which needs to be corrected before any other action, adjustment of treatment, or settings that can be considered or recommended.
It should be noted that, in the daily clinical practice, physicians use "visual" signs as chest movement or signs of discomfort and possibly feedback provided by the patient. In addition to the waveforms (pressure, flow, and belt signal), the SyncSmart software provides the patient and ventilator frequencies, which are useful to identify PVA. These last two limitations are assumed to be a source for increasing the inter-rater variability  (32). Since "minor" AEs as advanced and delayed cycling (pressure release)  (42, 43)  are not detected by the SyncSmart software, they were not considered in this study. As they are suspected to be important for inducing major AEs, detecting them would lead to a better understanding of the causes of the main events. When the AI is typically greater than 10%, an adjustment of the ventilator settings is required  (9, 11, 20).
e. inter-trial) and between-sample (i. e. between subjects) variability in a number of assessment modalities, including MEG and EEG. They introduced so-called "power contours"-plots that depict statistical power as a joint function of the number of trials and the number of subjects. These power contours reveal the level of statistical power reached for given trial and subject numbers. We illustrate the findings of the present study using this power contour plotting approach. Here, we used MEG data from a large sample of subjects to reliably examine the behavior of statistical power measures. We simulated "experiments" with the open resting-state MEG dataset of the Human Connectome Project (HCP,  Larson-Prior et al. , 2013)  , varying the numbers of trials and subjects to assess the effects of within-and between-subject sample 5/46 sizes, respectively.
In an initial exploration, we used detailed individual source models and observed that detectability varies drastically according to the anatomical location of the source. Accordingly, we explored the spatial properties of the sources across the brain, focusing on their distance with respect to the closest sensor, their orientation with respect to the closest point on the sphere encompassing the subject's head, and the cross-subject variability of these parameters. For this, we undertook two types of simulations. First, we examined changes of detectability across different anatomical areas, simulating sources in the real anatomy of individual subjects in our HCP dataset. In doing so, we observed that the four measured properties were impossible to properly disentangle due to anatomical constraints.
Software All data were processed using MATLAB (The Mathworks, Natick, MA, USA; Version 2018a) and the FieldTrip toolbox  (Oostenveld et al. , 2011 , github release of October 25 2019). All analysis and visualization scripts and toolboxes, including the FieldTrip toolbox and megconnectome toolbox used, are available online at https://gitlab. com/icm-institute/meg/solid_meg.
Input data We used MEG data from 89 subjects from the Human Connectome Project (HCP) open data repository, described in detail in  Larson-Prior et al. (2013). Of the 100 recorded young adult subjects, 95 were included in the latest release (V3. 0) of the MEG HCP data, 6 subjects with missing data in one or another of the components described below were discarded, resulting in the final number of 89 subjects for our analyses. A whole series of preprocessing steps are already performed on the HCP data. For complete details, see the descriptions of the pre-processing pipelines for MEG  (Larson-Prior et al. , 2013)  , structural MRI  (Glasser et al. , 2013)  , as well as the online resources available at https://www. humanconnectome. org/software. We henceforth refer to these two preprocessing pipelines as the HCP-MEG pipeline and the HCP-structural pipeline.
7/46 Briefly, the MEG data were acquired at 2043. 5101 Hz (bandpass filtering: DC-400 Hz) with a whole-head 248 magnetometer device (MAGNES 3600, 4D Neuroimaging, San Diego, CA). The HCP includes data from several activation tasks and three 6-minute resting state periods (eyes open, fixating). We used the latter dataset in this study. MEG fiducials were obtained with a Polhemus system for coregistration to structural MRI data. Each subject was scanned with a high-resolution structural MRI on a 3T Siemens Skyra MRI scanner  (Van Essen et al. , 2012). Within the HCP-MEG pipeline, the structural MRI was used to create a single-shell volume conduction model using FieldTrip. Furthermore, to define anatomical labels in our study, we used the output of the HCP-structural pipeline, where a high-definition segmentation and anatomical labelling (Destrieux Atlas) of the cortical mantle was performed using Freesurfer  (Version 5.
Source models constrained by anatomy We performed a first set of simulations constrained by individual subjects' anatomy, where the signal to be added to the resting-state data was generated from one source (i. e. one vertex) of the cortical mesh provided by the HCP. This source was oriented orthogonally to the cortical mesh. Across participants, we identified sources of similar areal origin by means of their index in the cortical mesh provided by the HCP. It is worth mentioning that we do not imply that these indices map strictly homologous portions of cortex across subjects. The mesh topology is, however, identical in all 89 subjects (all 64,984 vertices, spaced on average by 1. 5 mm, are connected in the same way in every subject), and it is used in practice to make cross-subject source-level comparisons and averaging  (Fischl, 2012).
No within-or between-subject variability other than the one already present in the HCP data was added. Between-subject variability thus occurred because of variations in position and orientation of the source vertices across subjects. For high-resolution rendering of the cortical surface in figures, we used the high-definition segmentation with 163,842 vertices, spaced on average by 0. 5 mm, per hemisphere found in the HCP-structural pipeline  (Glasser et al. , 2013). All segmentations used in this study are linearly coregistered to MNI coordinates (same origin, scale, and orientation), but not "warped" (i. e. non-linearly transformed to closely map to the MNI template brain) as is often performed for mapping individual source reconstructions across participants in MEG. This non-linear mapping would have been inappropriate here since we were interested in between-subject anatomical variability.
Simulations In all of our simulations, we used the same procedure based on a Monte Carlo resampling strategy. The Monte Carlo procedure uses repeated random selection from a data sample to approximate a property of the general population, that is, here, the probability of finding a statistically significant effect in the general population (a. k. a. statistical power). As illustrated in Figure  1 , for each simulation with a given number of subjects and trials, we first randomly chose the required number of subjects from the HCP database, sampling without replacement. For each subject, we then randomly selected our "trials". Each trial consisted of 25-ms (50 samples) time segments of the continuous resting state data, at least 2 s apart from each other. We then split these trials randomly in two sets of equal size, added signal (according to the procedure explained above) in one set and averaged the data across time points and trials separately for each set.
Overall variations of statistical power throughout the brain Before getting into the description of the explored spatial properties, we describe here the overall variations of statistical power for detecting sources throughout the brain. We considered three ways to express the signals that are commonly used in sensor-level analyses: amplitude, squared amplitude (or power), and standard deviation of amplitude across sensors (or global field power). Here, we present statistical power as computed from the amplitude of evoked activity at the sensor level (Figure  2 ). The results for squared amplitude and standard deviation of amplitude across sensors are shown in Supplementary Figure  1. We calculated the average statistical power for detecting a source at each possible cortical location for a highly detailed source model following individual brain anatomy, in a simulated dataset of 25 human subjects and 50 trials per condition.
Orientation variability across subjects Figure  9a  shows cross-subject variability in orientation for every source location on the cortical surface, plotted on the average brain of the 89 subjects in the HCP dataset. The histogram of these vector lengths across all vertices is plotted in Figure  9b , which highlights the heterogeneity of orientation variability across the brain. Overall, values ranged between 0. 0019 and 0. 74, with maximal values in lateral occipital regions, and minimal values along the central sulcus, the ventral prefrontal cortex, dorsomedial prefrontal cortex and insular cortex. Dipoles in the most variable regions of the lateral occipital cortex could be oriented up to 180° apart (i. e. in opposite directions) across subjects, dramatically reducing the net contribution to the signal at sensor level across a group of subjects.
In other words, the poor detectability of some cortical regions seems to be mitigated by their anatomical consistency across subjects (and conversely some highly detectable regions become less visible at group level). Most importantly, overall, we believe that there is no unequivocal answer to the question of an appropriate sample size in MEG (or EEG) experiments. Our study highlights the importance of considering between-subject variability in addition to the long-known spatial properties of sources when it comes to deciding how many subjects and trials to include in an experiment. An important point to note regarding the decreased detectability with higher between-subject source variability is that it is standard procedure in MEG research to localize sources in individual subjects on their own anatomy before combining data for group analysis in source space  (Hari & Puce, 2017; Jas et al.
Acknowledgements Maximilien Chaumon  and  Nathalie George  work on the CENIR MEG-EEG platform at the  Paris Brain Institute , which has received infrastructure funding from the programs "  Investissements d'avenir  "  ANR-10-IAIHU-06  and  ANR-11-INBS-0006. Aina Puce received support from the  College of the Arts & Sciences at Indiana University, Bloomington (Sabbatical Leave ).
, 2017; Pritchard, 2019). The major Central Asian river basins Syr Darya, Amu Darya and Tarim will reach maximum glacier meltwater input within the next decades  (Huss & Hock, 2018; Rounce et al. , 2020). In combination with rapidly growing economies, this poses the risk of freshwater scarcity  (Varis, 2014)  and might trigger conflicts  (Munia et al. , 2016; Krasznai, 2019). The synthesis of regional mass balance dynamics on annual to seasonal time scales for Tien Shan and Pamir is challenging because glaciological datasets are sparse  (Unger-Shayesteh et al. , 2013; Hoelzle et al. , 2019; Barandun et al. , 2020). Therefore, most hydrological models for glacier melt quantification are calibrated on auxiliary datasets rather than actual measurements, hampering their reliability  (Pritchard, 2019).
Data and Methods Our methodology combines (i) transient snowlines (transition between ice and snow surfaces approximating the zero-mass-balance-line of the glacier;  Dyurgerov et al. (1992) ), (ii) geodetic estimates, and (iii) distributed mass balance modelling to provide annual mass balance time-series  (Barandun et al. , 2018). The snowlines are used to calibrate a temperature-index and distributed accumulation model  (Braithwaite, 1995)  for each glacier and year separately. Geodetic mass balances then constrain the modelled multiyear mass balances to reach agreement between the two observational datasets.