dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, the PVN and the left and right SON constitute three distant points in the acquisition volume that are perfectly adapted to obtain an accurate alignment of the whole acquisition volume in the reference space. After alignment, there is an average 11% shrinkage in volume compared to the reference atlas. This contraction rate is then taken into account for all analyses. Developmental atlas. To compare datasets obtained from different mice, onto a common 3D coordinate space, all acquisitions were re-referenced in an adult space based on the Allen brain mouse atlas. Acquisitions on younger animals were registered in the same space using the same procedure but using the aligned, combined, and symmetrized PVN and SON volumes of the older animals as the new landmarks. Thus, adult's animals (RP56) were re-referenced according to the Allen-Brain Atlas, juveniles (P14) according to adults, infants (P7) according to juveniles, neonates (P3) according to infants, and early post-natal acquisitions (P0) according to neonates.
As we focus on the ontogeny of OXT cell expression in the PVN, changes in cell density due to global size growth were controlled by using data re-referenced in the adult dimension space (Figure  S4D ). Then, to quantitatively measure the evolution of the density during the development, a measurement space constituted by the coordinates of all the cells detected in the adult subjects was defined. Then for each coordinate within this measurement space, the local density was estimated for each age, by calculating the average distance between the measurement point and the K nearest referenced cells (K = 100 -for a measurement space consisting of the OXT cells detected for 3 animals to which we add the same data in mirror symmetry; the average distance D of the K nearest neighbors can be converted in density, considering that in a sphere of radius D, the expected number of cells is K/2).
For each identified cell, the fluorescence intensity value was estimated, based on the peak intensity voxel, since this method does not assume a constant volume via Imaris Software (Bitplane, Oxford Instrument). Imaging parameters were set to minimize photobleaching image acquisition  (Coffman and Wu, 2012; Verdaasdonk et al. , 2014). As the fluorescence values are not directly comparable between two acquisitions and as the objective of this analysis is to identify fluorescence variations within the PVN, the fluorescence values were systematically Z-transformed for each subject (Figure  S4G ). Finally, using the two PVN subdivisions obtained by cell density analyses, for each animal the average difference in fluorescence between the two regions was estimated and an analysis of variance with post-hoc test corrected by the Tukey-Kramer's post-hoc correction for multiple comparisons was used to identify periods in development when a difference in fluorescence between the two clusters can/could be observed.
Statistical analysis The statistical analysis and graphical representations were performed using GraphPad Prism 8 software (GraphPad Software, Inc). Comparisons within groups (for OXT or AVP) were determined by either one-way ANOVA (age effect) or two-way ANOVA (age x region). Tukey-Kramer's multiple comparisons test was used to find post hoc differences among groups and calculate 95% confidence intervals to report effect size. When 95% confidence intervals were not calculated, then the Holm-Sidak's multiple comparisons test was used. In the text and figures, values are provided as mean G SEM, and p <0. 05 was set as the criterion for statistical significance.
ACKNOWLEDGMENTS This research is funded by  Centre National de la Recherche Scientifique , le  Vinatier Hospital  and  ANR  ( 16-CE37-0017-01 ,  17-CE37-0015-01 ), and a  Labex Cortex  ( ANR-11-LABEX-0042 ) grant from the  University of Lyon I in the program ''Investissement d'Avenir. ''We thank the genotyping facility ( SFR Biosciences-ENS Lyon ) and the animal facility ( iXplora-CarMen Laboratory U1060 ) for allowing access to the animal facilities. A special thanks to  Olivier Raineteau  for helpful discussion during data acquisition and to  Bruno Pillot  for help during animals' care.
Data and code availability d Microscopy data reported in this paper will be shared by the lead contact upon request. d All original Matlab codes written to process and analyze the immunolabeled cells detected with the Imaris software have been posted on the Open Science Framework website as part of the Oxytocin and Vasopressin Neurodevelopment project, and are publicly available as of the date of publication. DOIs are listed in the key resources table. d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
This study presents a deep learning model devoted to the analysis of swimming using a single Inertial Measurement Unit (IMU) attached to the sacrum. Gyroscope and accelerometer data were collected from 35 swimmers with various expertise levels during a protocol including the four swimming techniques. The proposed methodology took high inter-and intra-swimmer variability into account and was set up for the purpose of predicting eight swimming classes (the four swimming techniques, rest, wallpush, underwater, and turns) at four swimming velocities ranging from low to maximal. The overall F1-score of classification reached 0. 96 with a temporal precision of 0. 02 s. Lap times were directly computed from the classifier thanks to a high temporal precision and validated against a video gold standard.
More specifically, in Swinmming Activity Recognition (SAR), CNN-based methods, which can automatically extract discriminative features with convolutional kernels, have demonstrated better and more generalizable performances than conventional methods using predesigned features such as descriptive statistics  [16]. An interesting DL approach for human swimming style recognition and lap counting has been developed in which a convolutional neural network (CNN) with high performance was successfully used in swimming style recognition. Furthermore, studies based on recurrent neural networks (RNN) such as LSTM have demonstrated interesting results for SAR  [36]  with a Bi-LSTM (Bidirectional Long Short-Term Memory) network. Data records were collected from 40 swimmers and were labeled into eight classes: Unknown, Null, Freestyle, Breaststroke, Backstroke, Butterfly, Turn, and Kick.
The Bi-LSTM method was able to perform activity classification with an average F1 score of 91. 39%. To attain such performance, the network used pre-extracted statistical features as inputs instead of direct IMU signals. However, LSTM and Bi-LSTM are designed to process and make predictions from available sequences of data. In contrast, CNN is designed to exploit "spatial correlations" in data, making them perform well when identifying shapes from images  [37]. Therefore, it would be interesting to train a bi-LSTMbased model on swimming without pre-extracted features, as in  [36]  except with raw IMU data, in order to input data to be temporal series. All of the above-mentioned studies relied on publicly available  [16]  databases with a non-video-based labelling of activity into five classes: the four swimming techniques and a transition phase including the rest of the swimmer's activity (turn and break).
Despite valuable contributions, this approach requires the development of new classes to provide more widely applicable insights for monitoring in real training conditions. From this perspective, it is be necessary to integrate other essential variables of training control, such as LT and a classification separating the underwater, turn, and rest phases. To the best of our knowledge, ML and DL have never been used to compute LT automatically. Finally, while most of these models are well suited to considering homogeneous data, they show large performance drops in ecological conditions or when applied on high level populations, for example. Indeed, many papers developed their models with a homogeneous population  [25]  and/or at homogeneous paces  [15]. Therefore, it may be important to gather a database in which the participants have heterogeneous levels of swimming (inducing inter-subject variability) and swim at heterogeneous paces during their training (inducing intra-subject variability) in order to ensure that the model can be widely generalized.
This study is primarily aimed at developing a deep learning model devoted to the analysis of swimming using a single IMU attached to the sacrum. It should be able to classify swimming activities at the different velocities that may occur during a full training session. Moreover, the proposed methodology should be generalizable to a wide panel of swimmers through the use of a database with high inter-and intra-swimmer variability. Secondary purposes are to compute LT directly from the classifier with high temporal precision and to validate LT values using a video gold standard.
Experimental Set-Up The experiment took place in an indoor 25 m pool. The participants were instrumented with one waterproofed IMU (Cometa WaveTrack, Milano, Italy) composed of a 3D accelerometer and 3D gyroscope. Accelerometer and gyroscope data were sampled at the same frequency of 280 Hz using a full scale set at ±8 g and ±1000 deg • s -1 , respectively. The sensor was placed on the sacrum at the middle point between the two posterior superior iliac spines, then fixed with double-sided tape and secured with waterproof medical adhesive (Tegaderm, 3M, Cergy-Pontoise, France). The IMU described a coordinate system defined with x-axis pointing cranially, y-axis pointing laterally, and z-axis pointing posteriorly. Three cameras sampled at 30 Hz were used to identify lap events and swimming activities, and served as a reference for validation purposes.
Ground Truth Activity Video Labelling and Lap Time Assessment From the entire dataset, eight phases defining swimming activities were defined. These phases and their defined starting and ending occurrences are summarised in Table  1  and were used to extract timecodes from the video gold standard. Then, the IMU signal was labelled with this subsequent activity. Based on this digitization of swim activity, videobased lap times (LT CAM) were computed according to specific definitions depending on the swimming technique and the lap type, namely, the beginning, middle, and last lap of an interval, respectively denoted as LT START, LT MIDDLE, and LT END (see Table  3 ). Table  1. Phase identification used in video labelling and then for feeding into the algorithms.
IMU Data Processing and Deep Learning Model 2. 4. 1. IMU Data Preprocessing Let X be the raw data that correspond to the sensor's output time series: X = x 1 , x 2 ,. , x t where x i denotes the accelerometer and gyroscope values at time t. Raw data from the IMU sensor were filtered using a second order Butterworth low-pass filter with a 10 Hz cut-off frequency and downsampled to 50 Hz in order to lower the computational cost. An example is shown in Figure  2. Standardization was applied to the input data. The mean ( Xij ) and the standard deviation (σ) were computed for each IMU channel. Then standardization was applied to each time series following Equation (1): X norm ij = X ij -Xij σ (1) where X ij is a given IMU channel, Xij is the mean of the corresponding IMU channel accross all subjects, and σ is the corresponding standard deviation.
The time series data were then transformed in a preprocessed time series, X : X = x 1 , x 2 ,. , x n where n is the number of total dimensions. This preprocessing is needed in order to preserve the signal characteristics, including relevant information about the activity. Following data recording and preprocessing steps, a two step methodology was developed. The first step was performed using a deep learning model trained from the database. The second step filtered the raw predictions of the DL model in order to eliminate prediction mistakes through a previously engineered procedure  [21].
The architecture is summarized in Table  2. The network was implemented using Tensorflow  [39]  and Keras  [40]  and relied on LSTM cells  [41]  used bidirectionnaly  [42]. Learning lasted ten epochs, an epoch being the number of passes through the entire training dataset the DL algorithm has completed. The initial learning rate was fixed to 0. 001 after performing a grid search optimisation on a range of learning rates from 0. 1 to 0. 00001, as this value led to the smallest loss for ten epochs. This selected learning rate was scheduled with an epoch-dependant decline during training, as follows: α = 0. 001 800 epochs 100 (2) Finally, based on testing set raw prediction sequence Ŷ, an algorithm developed with Matlab (The Mathworks™, R2020b, Natick, MA, USA) was applied in order to remove artefacts and prediction skips by filtering the activity predictions  [21].
One subject was randomly chosen to form these sets, and all other subjects accounted for the training set. While this validation procedure relies on the same principle as holdout cross-validation, it illustrates how the model works when tested on a subject that is not part of the database, making the results more generalizable  [21]. We recorded 990 laps during the experiments, along with calculations of LT CAM during each lap. However, due to material bugs and human mistakes during the execution of the protocol, only 952 laps and 870 laps were retained to compute LT IMU and LT MAN, respectively. The method used to describe LT CAM and LT MAN is sumarized in Table  3. LT IMUs were computed using Ŷ and based on class transitions, as shown in Table  4. Table  3. Definition of phase and events to compute LT CAM and to monitor swimmer activity with stopwatch (LT MAN).
Performance of Swim Activity Recognition The results presented here are based on the predictions made by the model for each iteration of the sliding window. Based on the video labeling, the model is supposed to predict the performed activity at the median frame of the sliding window, which in this work is the 45th frame of each window. They are presented in Table  5. Prior to the filtering step, the average overall precision on the testing set was 0. 77 and the weighted average precision was 0. 92. As the dataset was unbalanced, assigning weights to the classes as a function of their number of samples tended to improve the precision, as the model trained better on those classes. Short classes such as wallpush, turns, and underwater had lower precision, respectively, at 0. 17, 0. 58, and 0.
71. However, activities that were more well represented in the dataset (strokes and rest) were predicted more precisely, as 0. 98, 0. 96, 0. 83, 0. 96, and 0. 99, respectively, for the butterfly, backstroke, breaststroke, front crawl, and rest. This tendency remains the same for recall, with lower values for short classes and higher values for stroke styles and rest. Indeed, recall was 0. 08, 0. 71, and 0. 71 for the wallpush, turns, and, underwater phase, respectively, and 0. 83, 0. 94, 0. 95, 0. 96, and 0. 99, respectively, for the butterfly, backstroke, breaststroke, front crawl, and rest. The combination of those last two metrics, that is, the F1-score, was 0. 91 on the testing set. F1 scores per phase were between 0. 11 and 0. 99 for wallpush and rest, respectively. For the four swimming techniques, F1 scores were between 0.
Moreover, a recent study has shown that the highest performance in both lap detection and swimming technique identification were achieved with a sensor placed on the sacrum  [15]. Most previous papers collected data from relatively homogeneous groups of swimmers. Indeed, the data used for the classification algorithm settings independently considered elite level  [20] , college level  [18] , national second league level  [16] , and national level  [15]  swimmers. Thus, each of these studies were likely to rely on similar swimming techniques. This homogeneity in terms of the swimming level may affect the process of model training, as the resulting model may fail to generalize the algorithms to swimmers with different skills and levels. In the present study, a strong restriction on swimming level was imposed in the inclusion of participants.
As the present database included elite and non-elite swimmers from the regional to the second league level to participation in the national championship, the dataset used here was representative of a large variety of stroke techniques and levels. This suggests that the proposed model can be used reliably with a wide range of swimming proficiency levels. Moreover, many previous studies involved small numbers of participants, i. e. , N = 3  [51] , N = 11  [52] , N = 13  [25] , and N = 17  [15]. Apart from  [16, 18] , who collected data from a large number of swimmers, i. e. , 45 and 40, respectively, to the best of out knowledge, the data collection presented in this manuscript represents one of the largest databases collected in the literature to date regarding IMU-based swimming activity classification.
Moreover, with strong inter-and intra-subject variability, it can be hypothetised that the present database covers most of the swimming skills and techniques needed to train an in-field generalisable classifier. Furthermore, the literature highlights a clear imbalance in terms of the different swimming actions analyzed. Indeed, most previous studies were restricted to specific swimming techniques or phases. While most classification algorithms have focused on the front crawl and backstroke, studies investigating the butterfly and/or breaststroke are more scarce. Moreover, apart from  [15] , the identification of swimming microphases, including turns and underwater phases, have not previously been tackled. However, the identification of those phases is decisive in analysis over a full training session, or at least during a swimming set.
e, every 0. 02 s. Recent studies have developed novel approaches to increase the temporal precision of such predictions, such as the one in  [20]. More specifically, these authors used a synthetic minority oversampling technique (SMOTE)  [57]  relevant for microphase analysis. The only paper considering numerous microphases is that of  [15] , who performed SAR in the same eight classes we investigated, namely, wallpush, underwater, turn, rest, and the four main swimming techniques. However, with a single intensity at 80% of maximal speed, their database suffered from a lack of generalisation; this would require various intensities to be encountered during training. The present manuscript, in contrast, tried to attain high temporal precision with numerous types of activities predicted at very different speeds and intensities.
28 and 0. 82, respectively. This poor identification may be due to the duration of phases that are very short, leading to a lower representation in the database of those phases and consequently to fewer opportunities for the model to train on such temporal configurations. However, identifying a wall push is not systematically necessary to identify lap times, because the identification of pre-and post-phases may be sufficient. Moreover, the analysis of the classification report showed that wallpush is most of the time confused with either the underwater, turn, or rest phases, which are the adjacent classes. This analysis suggests that there is not a misclassification of wallpush with other classes; rather, the model has difficulty identifying boundaries between turns, underwater and rest phases, and wall pushes.
Therefore, another contribution of the present manuscript is the development of a model suited for any level of swimmer and intensity of swimming, regarding the variability embodied in the database. Such precision has, to the best of our knowledge, never been reached in the previous literature aiming to classify swimming activity with machine learning models. Our results on lap times highlights a minimal loss compared to stopwatch measurement (3. 84% vs. 12. 12%), demonstrating the relevance for coaches of such an automated method. This may help coaches to automatically monitor swimmers at the same time. In comparison with previous works based on thresholds, the present results based on a large data base show lower differences between IMU-based and video-based lap times. Indeed, a difference of 0.
Limits and Perspectives The present manuscript is among the first to reach such good performance in the classification of swimming activity with this level of temporal precision and data heterogeneity. However, this work suffers from limitations that may engender interesting perspectives for future studies. The database we used was imbalanced, and certain classes were underrepresented compared to other swimming activities (i. e. , wallpush and turns), leading to lower performance in predicting these short and under-represented activites. Therefore, it would be interesting to use methodologies dealing with imbalanced datasets, such as SMOTE, in order to improve performance on those classes. Moreover, the present model did not take the diving start and leg kicking phases into account. The inclusion of such phases in the algorithm would be an interesting improvement in view of its usability in daily training routines and monitoring.
Finally, the network architecture presented here relies mainly on bi-LSTM. It would be interesting to compare different neural network architectures and their respective performance, as has been carried out in several previous papers. A final interesting point involves the inputs of the neural network. We made the choice to use raw IMU data as the input of the model in order to retain the maximum amount of information and not discretize the data. A hybrid approach using raw IMU data and discretized data in the temporal and frequency domains, respectively, as in traditional machine learning, might be an interesting approach and allow the model to attain better performance.
| PCA of clinical parameters and patient clustering in the 12-18 years children cohort PCA analysis determined six PCs, PC1 to PC6. PCA was robust to data reduction, as shown by PCA calculation after repeated random data removal (Supporting Information: Figure  1B ). The amount of variance explained by each component is shown in Supporting Information: Table  2. We focused on the first three PCs in the following analysis (PC1, PC2, and PC3), as they accounted for more than 70% of the whole variance of the sample (respectively 38%, 19%, and 17%; Supporting Information: Table  2 ). Evolution of the three PFT parameters (ΔppFEV 1 , ΔppFVC, ΔppFEF 25-75 ) were the strongest contributors to PC1, as shown by their correlation with this PC (Figure  1  andSupporting Information: Table  2 ) and their value in the loading matrix (Supporting Information: Table  3 ).
In order to better understand the adoption of domesticated livestock in the Altai, we conducted proteomic analysis of dental calculus from 21 Bronze Age (Afanasievo, Khemceg [Chemurchek], Sagsai) and Iron Age (Xiongnu) individuals (c. 2900 BCE to 300 CE) from the region. Results of this analysis confirm that the Altai Mountains acted as a navigable barrier across which new adaptations were transmitted to the eastern steppe (Fig  1 ). Correlated with previous archaeological and biomolecular datasets, our results shed new light on how societies expanded and transformed after the adoption of domesticates.