dataesr/scientific-paragraphs-categorization huggingface dataset:
5 ppb each at 2-4 km, Fig. S14 ). The observed and modeled N 2 O enhancements agree within an order of magnitude for the profile, but the model underestimates the high-altitude (4-7 km) N 2 O enhancement by < 1 ppb and overestimates the lower-altitude enhancement (2-4 km) by ∼ 1 ppb. This difference in N 2 O enhancement could be due to a strong latitudinal gradient in N 2 O across this profile or the timing of N 2 O emissions sampled along this single profile compared to a monthly mean estimate from the inventory. Strong correlations between N 2 O and HCN (r 2 = 0. 95), CO, and CH 3 CN suggest that N 2 O from burning emissions also contributes to the N 2 O enhancement (Figs. 8  and S12 ). However, when we convolved the monthly mean fire contributions from the Global Fire Emissions Database (GFED, https://www.
Combining the information from N 2 O profiles and other chemical tracers such as CO, SF 6 , O 3 , and CFC-12, we traced the propagation of stratospheric air along the tropospheric column down to the surface. This transport dominates the N 2 O seasonal cycle and creates the seasonal surface minima 2-3 months after the peak stratosphere-troposphere exchange in spring. The high resolution of this data set (10 s) allowed us to study the factors influencing the enhancements in the N 2 O tropospheric mixing ratios, which are associated with biomass burning and human activities such as urban and industrial emissions. The highest N 2 O mixing ratios occur close to the Equator, where they extend throughout the tropospheric column. The strongest N 2 O enhancements were observed close to the Equator and at a number of mid-latitude locations.
Acknowledgements. We would like to thank the  ATom leadership team , the science team, and the  NASA   DC-8 pilot , technicians, and mechanics for their contribution and support during the mission. We thank  Karl Froyd  for the aerosol products during ATom that support this study. We also thank the  National Aeronautics and Space Administration  and the  National Science Foundation  for providing the financial support that made possible this study. Financial support. This research has been supported by the  National Aeronautics and Space Administration  (grant nos. NNX15AJ23G ,  NNX17AF54G ,  NNX15AG58A ,  NNX15AH33A , and  80NSSC19K0124 ) and the  National Science Foundation  (grant nos. 1852977 ,  AGS-1547626 , and  AGS-1623745 ). Review statement. This paper was edited by  Andreas Engel  and reviewed by three anonymous referees.
Data availability. Data from the ATom mission can be found in the NASA ESPO archive (https://espoarchive. nasa. gov/archive/browse/atom, last access: 10 February 2021), and in the ATom data repository at the NASA/ORNL DAAC (https://doi. org/10. 3334/ORNLDAAC/1581, Wofsy et al. , 2018). The QCLS N 2 O data is available at https://doi. org/10. 3334/ORNLDAAC/1747 (Commane et al. , 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-11113-2021-supplement.
Analysis of cleared samples The analysis of data obtained from light-sheet or confocal microscopes on cleared tissue is a daunting task that often represents most of the person-hours of these experiments. A midgestational mouse embryo (about 1 cm 3 ) scanned at a 5 µm resolution (about 1× magnification) uses about 10 GB of uncompressed image data for each channel. The size increases to about 200 GB at a 1. 6 µm resolution (about a 4× magnification). 3D imaging data impose challenges to store, move and display the data requiring good pipelines from the microscope to the experimenter's desk.
Stitching tiled acquisitions Often the sample is not captured in its entirety within the field of view. It is therefore often necessary to tile the scan across a grid of adjacent stacks. Tiled acquisitions require an image stitching step, to align and fuse adjacent stacks to reconstruct the full image. This is a complex operation; good image positioning and blending are crucial to insure a seamless 3D image devoid of striping or duplication effects. Imaris (Oxford Instruments) and Vision 4D (Arivis) offer basic stitching tools that scale well to large data sizes. The free software, TeraStitcher  (Bria and Iannello, 2012)  is also a fast stitcher for large data. However, the perfect stitching of a mosaic often calls for specific corrections for chromatic shifts, stage movements and spherical aberrations; BigStitcher is a suite of tools that addresses specific challenges of stitching data obtained from cleared samples  (Hörl et al.
Data visualization The data visualization is best carried out with software using pyramidal dynamic rendering, in which the resolution of the data displayed changes automatically with the zoom, such as Imaris (Oxford Instruments) or the Virtual-Reality only SyGlass (IstoVisio). A vital function for data visualization is the capability to quickly draw 3D arbitrary volumes to highlight regions of interest or carve out signals from an unwanted region (Fig. 5A ). This can be done plane-wise (e. g. with Imaris or Vision4D) or with virtual reality (SyGlass).
Quantification: segmentation Automated object segmentation, the capacity of an algorithm to recognize voxels in an image belonging to a structure (axons, vessels, tubes etc. ) or cells of interest, is also an essential aspect of quantitative analysis. Commercial software offers several tools ready out-of-the-box to segment cells, axons, vessels and others. These make use of either tracing methods or parametric filters, which do not scale well to large data, so often have to be used on a small crop of the data. Vision 4D integrates learning-based segmentation from Ilastik using Random Tree Forest classifiers, which can scale to large data and is very fast to train  (Berg et al. , 2019; Sommer et al. , 2011). However, this strategy only works on simpler tasks when the quality of the background and objects are relatively homogenous.
Registration In order to facilitate statistical comparisons between samples, 3D datasets can be aligned either to each other or to a reference template. A reference template enables the overlay of an anatomical annotation of the structures. This is essential to obtain automated region-based statistics of the distribution of cells, vessels or axons  (Friedmann et al. , 2020; Kirst et al. , 2020; Renier et al. , 2016). While the registration of adult samples is relatively straightforward, embryos develop quickly and reference templates are usually not available for them. For the mouse brain, for example, very precise atlases are available for adults  (Wang et al. , 2020). As generating standard atlases is a labor-intensive task, developmental atlases have been generated for only a few developmental stages  (Young et al.
Acknowledgements We are thankful to all the authors that kindly shared their images for this work. The image of the short-tailed fruit bat was made by  Idoia Quintana-Urzainqui ,  Paola Bertucci ,  Peter Warth ,  Chi-Kuo Hu  and  Richard Behringer  during the Embryology Course at Woods Hole 2014. Invertebrate and zebrafish images were shared by  Marko Pende  and  Florian Raible. The images of human embryos, African house snake embryos and spotted gar were kindly shared by  Alain Chedotal.
Funding This work was supported by the  program 'Investissements d'avenir'  ( Agence Nationale de la Recherche ;  ANR-10-IAIHU-06 ), the  European Research Council   starter grant  ' NeuroRemod ', the ' Paris Emergences' program  ( Mairie de Paris ) and the  UK Hearing Loss Foundation  awarded to N. R. ; A. V. -P. is a  Marie Sklodowska Curie Action  fellow. Deposited in PMC for immediate release.
Sleep is classically measured with electrophysiological recordings, which are then scored based on guidelines tailored for the visual inspection of these recordings. As such, these rules reflect a limited range of features easily captured by the human eye and do not always reflect the physiological changes associated with sleep. Here we present a novel analysis framework that characterizes sleep using over 7700 time-series features from the hctsa software. We used clustering to categorize sleep epochs based on the similarity of their features, without relying on established scoring conventions. The resulting structure overlapped substantially with that defined by visual scoring and we report novel features that are highly discriminative of sleep stages. However, we also observed discrepancies as hctsa features unraveled distinctive properties within traditional sleep stages.
Visual inspection of PSG data The AASM sleep scoring manual provides guidelines for categorizing sleep and wake states in five discrete stages (Wake, N1, N2, N3, REM). These rules are based on the visual inspection of neurophysiological signals. Sleep technicians typically examine patterns of characteristic rhythms in the neural signals as well as specific physiological events to classify 30-second segments of sleep. For example, REM sleep is defined by the AASM as a stage of low amplitude, mixed frequency activity accompanied by low chin muscle tone and rapid eye movements. These rules, based on visually recognizable markers, are easy to describe and to standardize across laboratories, playing a significant role in the development of sleep research over the decades.
Arbitrariness of scoring guidelines Because the scoring guidelines have undergone only minimal revision since they were established, they mostly reflect the state of knowledge and practice from past decades  (2). A typical example is the 30-second window rule, which was introduced at the time brain signals were recorded on 30 cm wide polygraph pages moving at 1 cm/s (3). Clearly, our current technology makes this obsolete. Regarding the PSG features that were selected for sleep scoring, some of them were chosen based on their visual clarity in preprocessed time series, with minimal contribution from modern data analysis methods. In fact, at the time the AASM sleep scoring guidelines were published, 26 of the 29 visual scoring rules were determined by consensus among human scorers and were proposed without a complete validation of their biological relevance to sleep  (4, 5).
Purpose of this study The goal of the present study is to organize sleep in a data-driven way, based on a wide range of their time-series properties. To achieve this goal, we first apply a massive feature extraction tool to sleep time series, using the highly comparative time-series analysis approach implemented in the hctsa software package which contains a diverse set of over 7700 time-series features  (18, 19). hctsa encodes scientific algorithms for time-series analysis in the form of features that each summarize a structural property of a time series in the form of a real number. It includes methods that were developed in and applied to a wide range of research areas, including Fourier and wavelet transforms, entropy, self-correlation and predictability, nonlinear time-series analysis, and fractal scaling.
This large set of analyses has never before been applied systematically to PSG data. Based on the results of these hctsa time-series analyses, we sought to organize sleep (i. e. , intrasleep wakefulness, N1, N2, N3 and REM) independently of pre-defined scoring rules. To this end, we approached sleep scoring in an unsupervised way by clustering sleep time series based on the similarity of the dynamical properties captured by hctsa. Furthermore, we analyzed the data on an epoch-by-epoch basis to minimize subjective criteria related to the incorporation of contextual information. By taking this data-driven approach, which we refer to as feature-based clustering, our aim was not to replicate or improve AASM visual scoring, but to go beyond it. We examined this data-driven organization by comparing it to the traditional structure defined by AASM rules.
Part 1. Correspondence between feature-based clustering and traditional sleep stage architecture To objectively assess the correspondence between how sleep epochs are organized using (i) time-series features and (ii) AASM labels, we developed an automated procedure that combines unsupervised clustering, cluster matching, and cross validation (Figure  1 ; see Methods; Featurebased clustering). We applied hctsa to 12 full-night PSG datasets, each containing a preprocessed EEG, EOG, and EMG derivation. Through this process, the EEG, EOG and EMG time series of each 30-second epoch is converted into a single vector containing the output values produced by all hctsa features for this epoch (Figure  1a ). The feature values which generated non-real numbers or errors were filtered out and the remaining valid values were normalized.
After forming a balanced dataset (Figure  1b  & c), we grouped 70% of the data (training set) into 5 clusters based on their hctsa feature values (Figure  1d ). Using our sequential maximum matching algorithm, we assigned each unlabeled training cluster to one of the five AASM sleep stages (Figure  1e ). We named the five training clusters C1, C2, C3, CR and CW, as those that most closely mapped onto N1, N2, N3, REM and wake, respectively. Then, we assigned the remaining 30% data (testing set) based on their distance to the training clusters (Figure  1f ). We used the proportion of match between the AASM labels and the cluster labels of the test data (cross-validation) through 100 iterations to assess the correspondence between the two methods. To visualize how the large number of hctsa time-series features vary across a night of sleep, we show a color-coded feature matrix representing the feature values for each 30-second EEG, EOG and EMG time series from one subject over one night (Figure  2 ).
Feature-based clusters substantially overlap with visual sleep scoring To quantify the degree of correspondence between the two approaches, we computed the mean overlap between the two classifications across datasets (Figure  3a ). While the highest overlap was observed for N3 (77. 4% of the N3 epochs were assigned to C3), the lowest degree of agreement was obtained for N2 (only 43. 1% of the N2 epochs were attributed to C2). The majority of N2 mismatch was due to the assignment into C3 (20. 9%). We also note high offdiagonal overlap between Wake, N1, and REM epochs (e. g. , 27. 6% of N1 assigned to CR, 26. 6% of Wake to C1, 21. 3% of REM to C1). Overall, this level of overlap demonstrates the ability of our stage mapping algorithm to attribute each cluster to the corresponding AASM stage without a priori information on how sleep stages are defined.
To illustrate what each data-driven cluster looks like, we show exemplar EEG time series located near the cluster centers from one dataset (Figure  3b-f ). Feature-based clusters exhibit visual features that are descriptive of AASM sleep stages (e. g. , CW and C1 are characterized by high alpha activity, C2 by the presence of K-complexes, C3 by slow-wave activity). This analysis demonstrates that our data-driven approach reveals-without any use of AASM labels-a cluster structure in the data, and this structure reflects the key visual aspects that characterizes traditional scoring.
Contextual information does not fully explain the observed discrepancies While the feature-based clusters and traditional sleep-stage labels largely overlap, our novel framework also revealed interesting divergences. Our visual inspection of the epochs with mismatched labels suggested that instances of disagreement may stem from the use of contextual information in AASM scoring. While human scorers are encouraged to use neighboring epochs to assist in labeling ambiguous epochs, our algorithm analyzes each 30second epoch in isolation. To test the possibility that the observed divergences are due to the use of contextual information in human scoring, we recruited 4 expert sleep scorers ('No-context scorers') and asked them to score a subset of the epochs from the same data and under the same conditions as our featurebased clustering approach.
Refining traditional sleep stages In Part 2, we took an initial step towards the discovery of sub-stages that are overlooked with the traditional scoring. We focused on N2 because this stage had the lowest level of agreement between our approach and AASM scoring (43. 1%; Figure  3a ) and represented the largest amount of the data available in each dataset (Figure  4 ). t-SNE projection exhibited heterogeneity within N2 in a visually striking way and in the majority of datasets (Figure  5 ). Variability within N2 was previously reported. Sleep spindle activity does not occur uniformly within N2, but their occurrence is modulated across and within sleep cycles  (27)  and is dependent on local temporal context  (28) , which may reflect fluctuations in sleep depth within N2  (29). Along this line, based on autonomic and hormonal activity, the division of N2 has been proposed as a quiet type (before the transition into N3) and an active type (preceding REM sleep)  (30).
Pre-processing and selection of CCSHS data We selected full-night polysomnographic recordings from 12 participants (6 female & 6 male) from an open-source dataset from the Cleveland Children's Sleep and Health Study (CCSHS)  (65) (66) (67) (68) (69). The dataset contains recordings from adolescents aged 16 to 19 years and is available for download at https://www. sleepdata. org. Each recording contained electroencephalograms (C3/C2 and C4/C1, A1, & A2), bilateral electrooculograms, a bipolar submental electromyogram, sampled at 256Hz for the EMG and 128Hz for the EEG. We selected 1 EOG derivation (LOC-A2: electrode placed next to the left canthus and referenced to the opposite mastoid, A2), 1 EMG derivation (EMG1-EMG2, bipolar derivation over the chin) and 1 left central scalp EEG derivation reference to the opposite (right) mastoid (C3-A2).
Feature extraction and selection We performed feature extraction on MATLAB 2015b using hctsa v0. 95. This version includes 7749 features that are derived from diverse scientific methodological literatures (such as physics, seismology, economics, neuroscience, and artificially generated simulated data). Among the 7749 hctsa features, some returned special values (non-real values or fatal errors). We excluded these features using the hctsa function TS_Normalise  (19). Using this function, we removed the features that generated special values in more than 20% of the time series of each dataset. On average, we removed 1747 features per dataset (min=1174, max=1981). The range of values generated by the remaining hctsa features varied across features. Therefore, we normalized values using the Scaled Robust Sigmoid (SRS) transformation implemented in TS_Normalise.
Balanced datasets Based on the AASM labels provided by the trained scorers, we trimmed the 12 selected datasets (Table  S1 ) to exclude any periods of wakefulness that occurred directly before sleep onset (defined as the first non-wake epoch) or directly after the final non-wake epoch. This allowed us to focus on the intra-sleep periods of wakefulness. The five sleep stages did not occur in equal proportions throughout the night. Table  S1  gives the proportion of each AASM stage, which is similar to the known proportion of sleep states (e. g. , N1 comprises only 5% of sleep whereas N2 accounts for approximately 50% (  31 )). To deal with the imbalance, we subsampled the datasets to equalize the number of epochs for each stage as explained in Figure  1b. We took all of the epochs (N) of the least frequent stage for each dataset, then randomly selected N epochs from each of the other five stages (total 5N epochs).
k-means clustering We used k-means clustering (MATLAB function kmeans with Replicates = 50, Max Iterations = 500 from Statistics and Machine Learning Toolbox version 11. 2). This algorithm groups data based on its Euclidean distance to the center location for each of k clusters  (70). We used it due to its simplicity, efficiency and wide presence in the literature  (71). To enable comparison with the 5-way classification of the AASM guidelines, we set k to be 5. However, by no means does our framework impose this number.
Visual scoring task During standard visual scoring, human scorers have access to a full night of temporally sequenced data. This allows them to rely on temporal context to score the epoch, especially when it is ambiguous (e. g. , is it preceded/followed by N2, N3 or REM?). To compare the epochby-epoch discrimination in our approach with traditional scoring, we created a task where the surrounding context was eliminated for human scorers. We recruited 4 scorers ('No-context scorers'), who had substantial experience in sleep scoring according to the AASM guidelines (all from the sleep laboratory at Monash University including one of the authors, TA). They scored isolated 30-second epochs one at a time in a 5-way forced choice manner using our custommade sleep scoring experimental software (MATLAB, Psychtoolbox).
In each trial, the software displayed an isolated 30-second signal consisting of 1 EEG (C3-A2), EOG (LOC-A2) and EMG (EMG1-EMG2). Participants were asked to score these epochs in accordance with the AASM sleep scoring guidelines and to the best of their ability. The number of epochs for each sleep stage was equalized. Two participants scored a subset of randomly selected epochs (n=37 trials per stage, in total n=185) from a balanced dataset (ID: 1800001) and the two others scored epochs (n=41 trials per stage, in total n=205) from a different balanced dataset (ID: 1800005).
Single-feature analysis In Part 3, we assessed the ability of single hctsa features to individually discriminate between N1/REM, Wake/N1, N2/N3 and N1/N2. In this analysis, we focused on the feature values generated by the EEG derivation. Given that some hctsa features produced special values in some datasets and not others, we filtered out such features in any of the 12 datasets (2146 nonvalid features) to enable the combination of results across datasets. A total of 5603 valid features that were common to all datasets were considered for single-feature analysis.
Computation of p-values To determine which features were significantly informative of sleep stages, we performed permutation testing by randomly shuffling the AASM labels of EEG time series. We computed balanced classification accuracy (following the procedure described in Computation of classification accuracy) over 100 iterations based on permuted labels, producing for each given feature and dataset a null distribution of 100 samples. Each sample was averaged across datasets. We estimated p-values as the proportion of null samples whose mean accuracy was as high or higher than the mean accuracy generated from un-permuted labels. We corrected pvalues using the False Discovery Rate (FDR) method  (73).
We named the 5 clusters as CW, C1, C2, C3 and CR, which were matched with W, N1, N2, N3 and REM respectively. f) After completion of training, each test epoch was assigned to the nearest cluster (i. e. , cross-validation). To assess the overlap between cluster decisions and the AASM labels, we computed the percentage of test epochs whose AASM label matches the cluster it was assigned to (e. g. , N2 epoch assigned to C2). The entire procedure was iterated 100 times per dataset. See Methods (Feature-based clustering) for details on each step of the procedure. To help interpretation, we order features in EEG by their correlation-based similarity using average linkage clustering  (18, 19). We order features in EOG and EMG using the same ordering as in EEG. Each pixel encodes a normalized feature value from 0-1 (see colorbar below the matrix).
Acknowledgements We thank  Tom Drummond  for co-supervising PS for her thesis,  Dror Cohen  for his pilot analysis using the hctsa method on another set of sleep data,  Jeroen van Boxtel  for discussion on the visual scoring task,  Jay Kim  for constructing a preliminary version of the visual scoring task,  Romain Holzmann  and  Ursula Voss  for providing the data, whose results will be published elsewhere.