dataesr/scientific-paragraphs-categorization huggingface dataset:
The northern lek site is the place used by these individuals to mate. The loggers were turned on in the evening following the capture event to avoid potential behavioral biases related to capture. Loggers collected GPS locations and 3D accelerometer data. Data were downloaded with a maximum transfer distance of 10 km from four permanent base stations connected to antennas. A total of 32 bats were equipped with loggers but three were discarded for this study because of the early loss of the collar and one because of insufficient data. The 28 loggers with two different settings collected GPS locations and accelerometer data (16. 7 Hz burst during 15 s every 2 min;  Grundy et al. , 2009)  from 10th to 30th January. A first group ("group 1") of 16 individuals was monitored from 17:45 to 06:00 (UTC + 01:00) during a mean of 9.
We fitted HMM with the "momentuHMM" R package  (McClintock & Michelot, 2018). Regarding group 1, the mean values of smoothed speed resulting from the modeling procedure were (i) 2. 4 m/min ± 1. 2 SD (range: 0. 1-6. 7) for stationary bouts, (ii) 9. 1 ± 5. 1 (range: 0. 6-33. 5) for short flights, and (iii) 134. 3 ± 141. 4 (range: 0. 4-796. 2) for commuting flights. These values were consistent with the current knowledge on the species  (Carpenter, 1986). Behavioral state identification was then generalized to locations from group 2. For this purpose, smoothed speed was computed for all these locations and the maximal value of smoothed speed obtained for short flights in group 1 (33. 5 m/min) was used as a threshold. The second location of each pair of consecutive locations, for which the speed calculated was above this threshold, was associated with commuting flights and discarded since the HMM approach could not be directly carried out for group 2.
This method maximized the sample size of the short flights and stationary bouts. If applied on group 1, the locations retained (short flights and stationary bouts) increased by only 3. 5% compared to the segmentation (HMM) method, indicating a good correlation between methods. For both groups, short flight and stationary bout locations within the lek site area were considered as related to the mating activity and related to the foraging activity otherwise. Finally, stationary locations recorded at the very beginning or ending of the night were associated with the bat's daily resting (an individual typically roosts alone or within a small group in a given place for a few days, before moving to another place located a few meters or kilometers away;  Bradbury, 1977)  and were consequently excluded from the datasets analyzed.
, 2020). The corresponding habitat type (i. e. , managed rainforest or agricultural lands) was attributed to each FA. We calculated the Geodesic distance (meters) between each FA and the lek site by averaging the distance of all constitutive locations to the centroid of the lek. Similarly, the closest distance to the river was calculated for each FA. The duration spent within each FA during a given night was calculated by cumulating the time elapsed between consecutive constitutive locations (if several visits of the same FA occurred during a night for a given bat, the duration was cumulated). Then, we determined FAs that were revisited on the following nights. Appendix S2 presents data related to the field prospection of randomly selected foraging patches to describe potential foodresource availability for bats.
However, so far, no method was developed for fluorescent images and confocal microscopy, which constitute most datasets in embryonic epithelia. Here, we devised DeXtrusion, a pipeline for automatic detection of cell extrusion/cell death events in larges movies of epithelia marked with cell contour and based on recurrent neural networks. The pipeline, initially trained on large movies of the Drosophila pupal notum marked with fluorescent E-cadherin, is easily trainable, provides fast and accurate extrusion/cell death predictions in a large range of imaging conditions, and can also detect other cellular events such as cell division or cell differentiation. It also performs well on other epithelial tissues with markers of cell junctions with reasonable retraining.
Such a challenging question requires a highly quantitative dataset on the total number of cell death as well as their precise spatiotemporal distribution and high throughput methods to compare these values in various perturbed backgrounds. The recent advances in long-term live imaging provide a wealth of data regarding tissue dynamics, especially for epithelia in 2D. However, retrieving cellular events quantitatively in a high throughput manner remains extremely challenging. For instance, there are more than 1000 extrusions, distributed all over the epithelium in the Drosophila pupal notum in less than 12h  (Guirao et al. , 2015; Valon et al. , 2021). So far, quantitative analyses of cell death/extrusion were performed using laborious manual detection of these events  (Moreno et al. , 2019; Valon et al.
Moreover, DeXtrusion generalises well outof-the-box to other epithelia (e. g. : pupal abdomen, pupal wing), which can be further enhanced by retraining using small training sets. By resolving the bottleneck of extrusion/cell death detection, DeXtrusion will open the way for a more systematic and quantitative characterisation of cell death distribution in large datasets which will be essential to understand its multi-layered regulation.
Results DeXtrusion: a neural network to recognise extrusion in cropped image sequences. We first aimed at detecting cell extrusion in the Drosophila pupal notum: a singlelayer epithelium on the back of the developing Drosophila pupal thorax with a high rate of cell extrusion/apoptosis that follows stereotypical patterns (Fig. 1A )  (Guirao et al. , 2015; Levayer et al. , 2016; Marinari et al. , 2012; Valon et al. , 2021; Villars et al. , 2022). We used a dataset generated in the laboratory covering a large number of extrusion events (6700 in the training set and 2320 in the test set see Supplementary tables 1 and 2). This dataset was made of large-scale movies of the pupal notum obtained from two different imaging set-ups (see Methods), with different frame rates, signal-to-noise ratios, and different fluorescent proteins coupled to the adherens junction protein E-cadherin.
1C  purple). Eventually, the network provides a probability of containing an extrusion for each cropped image sequence. This probability was finally thresholded to a binary output (extrusion or control event). We trained the DeXNet on image sequences extracted from our dataset, split in a training and test set (25 versus 7 movies, see Methods). We took cropped image sequences in the reference scale centred spatially and temporally around the manually detected extrusions (time 0 being defined by the termination of apical constriction), while control sequences were made of similar cropped image sequences that do not contain any event (see Methods). The temporal size of the sequences was chosen so that it contains enough information to recognise extrusion while reducing the chance to capture two events (see Methods, Fig.
S1A-C ). The x-y size of the cropped input images, as well as other hyperparameters of the model (number of epochs -the number of times the dataset is processed by the network for training-, of features, data augmentation…), were tuned by testing several sets of hyperparameters (Fig. S1 , S2 and S3). Taken together, the selected hyperparameters allowed the model to converge properly under 50 epochs and took around 55 min of training with 1 GPU (Fig. 1D ). Compared to our manual annotation, the classification of the neural network on our test dataset resulted in a precision of 0. 97, with 2% False Positive and 1% False Negative (Fig. 1E-F ). As our neural network performed well at spotting cell extrusions in cropped image sequences, we then integrated it into the DeXtrusion pipeline to detect all the cell extrusions in a large-scale movie.
2C ), we also thresholded the size of the positive volume to fit the minimal volume associated with extrusion events (which last between 20 and 30 minutes  (Villars et al. , 2022) ). The probability threshold and volume threshold were optimised to maximise the precision and recall (Fig. S2 , Methods). We also used a watershed separation to separate close events. Eventually, the exact location of the extrusion is defined by the centroid of the high-probability volume. Detected extrusions are then exported as a list of point ROI (Region Of Interest) compatible with Fiji ROI manager (Schindelin et al. , 2012) (Fig. 2A ). To assess the performance of DeXtrusion, we compared the resulting ROIs with the manually annotated ROIs of our test dataset. We first measured systematically the xy Euclidean distance and time distance between manually annotated and automatically detected extrusions (Fig.
3C ). Thus, to enhance the precision of DeXtrusion (the proportion of correct extrusion detection), we decided to add these other cellular events in DeXNet to discriminate them from extrusion. We trained the network to detect cell extrusions, cell divisions and SOPs or the absence of events using manually annotated events in our dataset (6700 extrusions, 3021 divisions and 3054 SOPs, Fig. 3F ). DeXNet was able to classify the cropped image sequences for these 4 events with high accuracy on our test set (Fig. 3G, Movie1 ). By including this new DeXNet in our pipeline, we significantly increased the precision of DeXtrusion (from 0. 26 to 0. 41, Fig. 3E, E'' ), while this did not impact significantly the recall (Fig. 3E' ). This is also reflected by the F1 score (Fig. 3E ), a measurement of precision and exhaustivity of detections (see Methods, from 0.
Of note, the accuracy was much lower for the test movie of the colcemid injected pupae (Fig. 3E-E '', deep blue, movie ID18), which phenotype was poorly represented in the training dataset and corresponded to an extreme condition where the mode of extrusion and the architecture of the tissue are quite different  (Villars et al. , 2022). After all these optimisation steps, the final model (2nets) can still detect all cellular events on a movie covering the full pupal notum (1200 by 1200 pixel^2, 200 time points) in less than 40 min on a regular PC with 1 GPU. Taken together, these optimisations steps led to a drastic increase in precision while maintaining similar recall on test movies. The last remaining false positive (~20%) can be easily filtered out manually from the final ROIs list. We estimated that the full procedure (DeXtrusion computation time+manual correction) takes between 1 or 2 hours for a movie covering the full pupal notum over 200 times (representing roughly 1000 extrusions).
Generalisation of DeXtrusion to mutant context and other epithelia The performance of a neural network is usually highly limited to the training data range  (Mockl et al. , 2020) , which may also apply to DeXtrusion. To test the generalisation of our model, we therefore, challenged our pipeline by testing its performance on different data from closely similar biological and imaging conditions in very different contexts. We first trained two "full" networks, using all our annotated data (training+test dataset). To adapt DeXtrusion to other tissues (Fig. 4A ) where the cell size and duration of cellular events are not of the same scale, the reference scale at which movies are resized is calculated so that cell diameter is around 25 pixels and extrusion taking place over 4-5 frames. We first tested DeXtrusion on Drosophila pupal notum depleted for EGFR (UAS-EGFR-dsRNA driven by pnr-Gal4), a condition that modifies tissue shape and the spatiotemporal distribution of extrusion while not affecting so much the extrusion process per se  (Moreno et al.
Discussion In this study, we have designed a fast tool that allows the automatic detection of several cellular events and estimated its accuracy. While the pipeline was primarily optimised for the pupal notum with labelled E-cadherin, the same principle can easily be applied to any other epithelial tissue provided there is a minimal dataset to retrain the model. While our precision (~0. 8) still requires a manual correction phase, this leads to a considerable gain of time compared to the manual annotation of large movies (from 10 hours to 1 hour ) which could lead to rapid quantification of a large number of movies. This considerable gain of time opens new opportunities for systematic quantification of the spatiotemporal distribution of cell death in a large numberof movies. DeXtrusion offers the possibility for large screening of drugs/mutations on the spatiotemporal distribution of cell death that could lead to the identification of new biological factors modulating cell apoptosis and new spatiotemporal feedbacks.
Adding this new feature could not only increase the precision of our pipeline but also offer an additional feature for large-scale analysis of tissue dynamics. We distributed DeXtrusion as an open-source pipeline with a graphical interface to propose an efficient and user-friendly tool to the community. Our main aim was to reduce drastically the necessary manual input to allow large-scale analysis of cell extrusions in the Drosophila pupal notum. The final tool allowed to go further by offering the possibility to screen also for cell divisions and SOPs and moreover can be easily adapted to add other cellular events or to other kinds of cellular labelling. the figure and results. LV contributed to the dataset with his data. AV, GL and RL wrote the manuscript and designed the project.
Methodology Generation of the training dataset To create an annotated dataset, we used movies that had been previously manually annotated for extrusions in the laboratory for other studies  (Moreno et al. , 2019; Valon et al. , 2021; Villars et al. , 2022). Pupae were dissected and imaged on a (1 μm/slice). All the movies were built using a local z-projection plugin which follows tissue curvature  (Herbert et al. , 2021). We pooled together 32 movies, all from the Drosophila pupal notum, but with different acquisition setups, genetic backgrounds and junctions staining. The full list of movies and their characteristics is given in Supplementary tables 1 and 2. We split the annotated data into two datasets, one for training (Supplementary table  1 ) and a smaller one for testing (Supplementary table 2).
For this, we selected randomly one movie of each of our different conditions for the test set, and all remaining movies of the same condition were used for training. We obtained two datasets: the training set of 25 movies, with 6692 annotated extrusion events from 8 different conditions, and a test set of 7 movies, with 2320 annotated extrusions from 7 different conditions. These data with the manual annotation are freely available on a repository (https://doi. org/10. 5281/zenodo. 7586394)  (Villars et al. , 2023). For each network training, the training dataset was split in 75-25% between training and validation data. Note however that these two subsets were not fully independent as they are composed of windows extracted from the same movies (training movies).
Movie augmentation The movies acquired at higher spatial and temporal resolution are much smaller compared to the majority of movies once rescaled in the reference scale. As such, this kind of data was under-represented in the training data, with only a few events by movies. To reduce this bias, we doubled these movies by downsampling temporally each original movie two times and introducing a shift in the frames extracted between the two repetitions. These movies are indicated with "_aug" at the end of the names in the available dataset.
Data rescaling To homogenise the training dataset and have events of similar duration/size, we rescaled all our dataset to the same temporal and spatial resolution of 5 min/frame and 0. 275 µm/pixel. Rescaling of the movies and ROIs was done with a Fiji macro. DeXNet networks were trained with input windows of this reference scale. A division event was visible on 2 to 3 time-frames, cell extrusion on 4 to 5 frames, and a cell had a typical diameter of 25 pixels. Therefore, the DeXtrusion pipeline must rescale all the movies to this reference scale before applying the classification. Since characteristic event duration and cell sizes can vary between tissues/organisms, we used these "cellular" features for rescaling rather than absolute time or distances.
Data to test generalisation We tested the robustness of DeXtrusion to different unseen datasets. The first dataset was composed of 5 movies of Drosophila pupal notum depleted for EGFR (UAS-EGFR-dsRNA)  (Valon et al. , 2021). We do not have manual annotations on these movies, so they were not used in the training data. However, 3 movies in the original dataset were acquired with the same imaging and genetic conditions. This dataset was thus considered very similar to the training data. The second dataset was a large movie of the Drosophila pupal wing from  (Etournay et al. , 2015 ) (3879x1947 pixels, 200 time-frames). This tissue was not represented in the training data, but the organisation of the epithelium and cell shape is very similar to the pupal notum. This dataset was considered similar to training data.
We manually annotated a few extrusions (59) spanning the movie to evaluate the recall of DeXtrusion in this sample. We extracted a small part of the movie (495x444 pixels and 200 frames) and annotated this cropped movie to use as retraining data with 27 extrusions, 29 divisions and 19 additional controls for reinforcement. Note that the field of view of the movie contains the whole wing but also external tissue on the top and bottom parts. We focused the quantification only on the ROIs that were fully inside the wing. The third dataset was composed of four movies of larval epithelial cells (LECs) of the Drosophila pupal abdominal  (Davis et al. , 2022). The annotation of cell extrusions in the larval epidermal cells was obtained using the segmentation mask provided in the original dataset  (Tapon and Salbreux, 2022)  through tissue miner  (Etournay et al.
Acknowledgements We would like to acknowledge the  Image Analysis Hub  of the  Institut Pasteur  for discussions and advice for this work. We would also like to thank  Raphaël Etournay  for sharing the pupal wing movie and  Nic Tapon  for providing the original and segmentation of the pupal abdomen movies. We acknowledge the help of the  HPC Core Facility  of the  Institut Pasteur  for this work. AV was supported by a  PhD grant  from the doctoral school " Complexité du Vivant" Sorbonne Université  and from an extension grant of  La Ligue contre le Cancer , work in RL lab is supported by the  Institut Pasteur  ( G5 starting package ), the  ERC  starting grant  CoSpaDD (Competition for Space in Development and Disease , grant number  758457 ), the  ANR CoECECa , and the  CNRS  ( UMR 3738 ).
1 (uniform) weighting from  Beuther et al. (2018). (b) Associated features (a. f. ): cm: cm continuum; w: H 2 O maser; m: CH 3 OH maser. (c) References for distances and luminosities: d1:   2006 ),  Xu et al. (2006) , dl1:  Molinari et al. (1996) , dl2:  Molinari et al. (1998) ; l1: rms survey database (http://rms. leeds. ac. uk/cgi-bin/public/RMS_DATABASE. cgi) using SED fitting from  Mottram et al. (2011)  including Herschel fluxes and the latest distance determination; l2: rms survey database, using SED fitting from  Mottram et al. (2011)  updated to the latest distance determination; l3: rms survey database, calculated from the MSX 21 µm flux using the scaling relation derived by  Mottram et al. (2011)  and updated to the latest distance determination. ( * ) Distances in agreement with  Reid et al.
In this way, the final sample size was narrowed down to 18 regions along with two additional sources (NGC 7538 IRS1 and NGC 7538 S) 2 http://rms. leeds. ac. uk/cgi-bin/public/RMS_DATABASE. cgi 3 When the flux at 21 µm is greater than at 8 µm. which were observed previously with almost the same setup; we subsequently refer to these two sources as the CORE pilot regions  (Beuther et al. 2012; 2013; Feng et al. 2016). Table  1  lists the names and positions of the sources with their properties, including features associated with the regions such as the presence of various masers and/or centimetre continuum emission as proxy for the star formation activity. The threshold between IR-bright and IR-dark is defined according to  Motte et al. (2007)  based on cores in the Cygnus X star-forming region at a distance d = 1.
Calibration of the NOEMA observations was performed with the CLIC programme of the Grenoble Image and Line Data Analysis Software (GILDAS 4  ) package developed by IRAM and Observatoire de Grenoble. In most cases, two phase and amplitude calibrators were observed. Strong quasars (e. g. 3C84, 3C273, or 3C454. 3) were used for the bandpass calibration. For most of the target observations, the source MWC349 was used for absolute flux calibration, assuming a model flux of 1. 86 Jy at 220 GHz. For a few tracks, the source LKHα101 was used for the flux calibration. The absolute flux scale is correct to within 20%, according to the observatory standard. The imaging of the NOEMA observations was performed with the MAPPING programme of GILDAS. The continuum was extracted from line-free regions of the WideX band as described in  Beuther et al.
Temperature distribution We make use of the XCLASS software  (Möller et al. 2017)  within CASA to fit pixel-by-pixel the spectra of CH 3 CN (12 K -11 K ) K = 0-6, including CH 3 13 CN (12 K -11 K ) K = 0-3, under the assumption of LTE, which is typically the case in such dense environments. In summary, the user provides initial guesses and sets allowed ranges for a set of parameters (column density, rotational temperature, line velocity, linewidth, and source size); the software solves the radiation transfer equation and through a minimisation routine modifies these parameters until the best fit is found. The software then creates maps for each of the fitted parameters. The details of the XCLASS fitting routine are outlined in Appendix B of  Ahmadi et al. (2018)  where we had fixed the source size to yield a beam filling factor of 1 and only fitted for the optically thin CH 3 CN (12 K -11 K ) K = 4-6 lines, including CH 3 13 CN (12 K -11 K ) K = 0-3 isotopologues.
The PV plot of Cep A HW2 is particularly interesting, as it has a very extended nature, with a significant amount of emission present in the top-left quadrant associated with infall motions. In fact, using the 3D velocity field of 6. 7 GHz CH 3 OH masers,  Sanna et al. (2017)  show that the velocity field in Cep A HW2 is dominated A171, page 12 of 46 A171, page 13 of 46  Ahmadi, A. , et al. : A&A, 677, A171 (2023)  by an infall component from a radius of 900 au (∼1. 3 ′′ ) down to 300 au (∼0. 4 ′′ ), where a rotational component associated with an accretion disk becomes dominant. To determine protostellar masses, we fitted Keplerian curves to the 6σ edges of emission in the PV diagrams shown in Fig. 4  (the outermost contours) using the KeplerFit 6  package  (Bosco et al. 2019)  in Python.
2018). In fact, the emission in the bottom-left quadrant of the PV plot for W3(H 2 O) W corresponds to W3(H 2 O) E, therefore the expected Keplerian curve corresponding to a mass of 10 M ⊙ , shown as a yellow line, properly fits the contribution from W3(H 2 O) W in the top-right quadrant. Similarly, NGC 7538 S likely harbours multiple protostars as the continuum emission has an elongated shape and the CH 3 CN (12 3 -11 3 ) emission has multiple peaks along this elongation (see  Fig. A. 1). Therefore, for this source, only one side of the PV plot was fitted. Furthermore, the emission of CH 3 CN lines in IRAS 23385 lacks the high velocity components expected in Keplerian rotation, and can be better traced by HC 3 N(24-23) and OCS (18-17). In fact, thorough analysis of the CORE data by  Cesaroni et al.