dataesr/scientific-paragraphs-categorization huggingface dataset:
Note that our set-up is different from the one corresponding to the observation of the full interaction flow, namely when data consists in the complete knowledge of edges appearance and disappearance along a continuous gradient. Indeed in our case data is still aggregated over some time intervals or corresponds to a sequence of networks which are specific to a set of discrete values of a one-dimensional factor. When considering continuous time interaction flow data, the object of interest (the flow) is called a temporal network  (Holme, 2015)  and this setup will not be explored in this article. Lastly, it is important to mention that the time frame selection may be an issue in cases where choosing the resolution for the time aggregation is not driven by the ecological question. Indeed, in many cases, the choice of the time frame is expert-based: for instance the dataset from  Reynolds et al.
This number may be selected either through a statistical model selection criterion called ICL or relying on heuristic procedures  (Matias and Miele, 2016). To summarise, the dynSBM approach allows for exploring the following questions: i) Is there any high-level structure in the network, i. e. does dynSBM find more than a single group of nodes? ii) Does this network structure vary with time, i. e. are the nodes group memberships evolving with time? iii) What are the group switches trends and frequencies, i. e. what are the values of the underlying Markov chain parameters? iv) Are there any stable or unstable individuals, i. e. are there peculiar group memberships trajectories? Datasets Ants interaction networks. Colonies of the ant Camponotus fellah were followed with a tracking system that monitored the individual positions over days of observations and dynamic social interactions were deduced from physical proximity  (Mersch et al.
, 2013). Broadstone Stream seasonal food webs. This dataset concerns the aquatic macroinvertebrate community of Broadstone Stream in south-east England  (Schmid-Araya et al. , 2002; Woodward et al. , 2005). Six seasonal connectance food webs were recorded, one every two months from May 1996 to April 1997. We restricted here to simple presence/absence Figure  3 : Adding constraints to avoid label switching between time steps. At time t = 2 there are two equivalent alternative scenarios. In scenario 1 group memberships stay constant in time and interaction parameters change with time; in scenario 2 group memberships change between the two time steps but the intra-group interactions stay constant. In this latter case, the green group is characterised by being a 'peripheral nodes' group, the yellow group is a community and the red group is a hub.
Results In a previous work, we proposed a statistical analysis of two animals contacts networks (sparrows and onagers respectively, see  Matias and Miele, 2016). Here, we aim at focusing at a more ecological perspective and specifically explore issues raised by dynSBM on two ecological datasets: a dynamic contact network of ants  (Mersch et al. , 2013)  and a seasonal food web  (Woodward et al. , 2005).
e. those that experience group switches at certain time points. Indeed, our dynSBM groups allow to pinpoint these q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 1 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 1 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 1 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 1 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 1 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 1 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 1 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 1 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 1 Figure  4 : Interaction properties between groups on the ants dataset. Interaction presence and intensity between nodes in any of the Q = 3 groups to the others are represented in a global Q × Q matrix; each cell contains T = 10 time points on the x-axis corresponding to the different time steps.
Broadstone Stream seasonal food webs The number of sampled species of this aquatic macro-invertebrate community varies seasonally (up to N = 26 in total including 10 predators) as well as the number of directed links. This dataset forms a dynamic trophic network with T = 6 snapshots (May, August, October, December 1996, February, April 1997) and we are interested in addressing the temporal variation in the web structure. Five species were not sampled each month but this situation where nodes are present/absent over time is supported by our model (see supplementary  Between two days (t = 1,. , 10 on the x-axis), each line is a flux that represents the switch of one or more ants from a group to another group (q = 1,. , Q represented on the y-axis). D1-1 D1-2 D1-3 D2-1 D2-2 D2-3 D3-1 D3-2 D3-3 D4-1 D4-2 D4-3 D5-1 D5-2 D5-3 D6-1 D6-2 D6-3 D7-1 D7-2 D7-3 D8-1 D8-2 D8-3 D9-1 D9-2 D9-3 D10-1 D10-2 D10-3 Here, Dt -q denotes group q from day t.
Species from group 1 are "hidden" species: they do not eat much, and are not much eaten either. This group is obtained by our statistical procedure due to fewer feeding links from/to other species compared to species of the other groups. It is not coherent from a taxonomic point of view as it gathers a mixture of predators with little activity and secondary preys that we call peripheral species. Lastly, our model can deal with the overall decrease in the number of links after October (see Figure  6 , blue area decreasing with time in boxes) which is partly due to the fact that tanypods become less predatory and more detritivorous after autumn  (Woodward et al. , 2005). q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 q q q q q q 0 1 Figure  6 : Interaction properties between groups on the food web dataset.
Discussion The inclusion of time in network analysis has been a recent challenge that requires ad-hoc modelling approaches. The success of these approaches has to be measured by their ability to extract substantial additional information that would not be caught by a traditional static network analysis. To this aim, we propose the use of our new dynamic stochastic block model to decipher temporal trends or temporary patterns in dynamic ecological networks. On the ants interaction network dataset, while the overall group behaviour trends are captured by our model, different individual behaviours are also highlighted. This way, our results can be interpreted at different scales. On the food web dataset, our model underlines a clear trophic organization but also seasonal differences in the prey assemblage.
These results require further investigation by experts, but it is interesting to note that our approach can play a key role in extracting unexpected patterns. Our model is grounded on a rigorous statistical method  (Matias and Miele, 2016)  and is implemented in an efficient R/C++ package that can handle hundreds to thousands of nodes. It is henceforth one of the very first tools for ecologists facing the recent availability of time-ordered datasets or that would like to explore the evolution of ecological networks with respect to a one-dimensional factor.
Manual corrections are also exploited for automatic parameter learning, which relieves the user of understanding the trickiest parameters of the algorithms. Furthermore, we compare CellStar with other segmentation and tracking tools. We developed a manually curated set of yeast microscopy images to be used as a benchmark. Indeed, no consensus has emerged yet on the best-performing tool, and no systematic analysis of their performance has been proposed for long-term videomicroscopy data. We thus selected images that reflect a diversity of situations encountered in typical experiments. We compared CellStar with six software solutions dedicated to yeast cell segmentation and tracking in brightfield microscopy, namely CellID  [1] , CellTracer  [6] , CellSerpent  [7] , CellX  [12] , Tracker  [24]  and the intensity-based segmentation-overlapbased tracking (IBSOBT) pipeline for CellProfiler  [25]  (see electronic supplementary material, table  S2 ).
Graphical user interface and automated tool calibration No S&T algorithm can give perfect results when applied to real datasets, errors will likely propagate through S&T iterations. The need for GUIs that easily enable the detection and manual correction of errors is now well recognized  [28]. A unique feature of CellStar is the ability to manually curate segmentation and tracking results using a GUI and use this information as ground truth for parameter learning to further improve batch image processing. Segmentation ground truth consists of segments drawn, validated or corrected by hand. Thanks to the iterative nature of the segmentation algorithm, manual corrections may propagate and solve other segmentation and tracking issues (figure  4a ). These contours can be then used for automatic learning of the weights appearing in the definition of the surface energy E surface.
Benchmarks and evaluation platform The choice of an image-processing tool is often difficult, because many tools have been proposed so far and because it is impractical to evaluate the effectiveness of each of them by visual inspection, in particular in the case of  rsif. royalsocietypublishing. org J. R. Soc. Interface 14: 20160705 long-term observations where a considerable amount of data should be inspected by eye. To help with this issue, we created a tool called Evaluation Platform. The tool enables convenient evaluation and comparison of the results of algorithms for the segmentation, tracking and long-term tracking of cells (figure  5 ). The evaluation is based on benchmark datasets, consisting of manually annotated images. Given analysis results, the tool computes the percentage of correct elements in the results ( precision) and in the ground truth (recall), and computes the F-measure, combining precision and recall.
We created an annotated benchmark composed of seven test sets that cover common situations for budding yeast micro-colonies growing as a mono layer, in particular images with sparse cells, dense cells, dividing cells and cells motion (for details, see Materials and methods). It contains more than 12 000 cells to segment and cell pairs to identify and spans up to 3 h of observations (60 frames). Using the Evaluation Platform, we compared the performance of seven software tools: Cell Tracer  [6] , CellID  [1] , IBSOBT via CellProfiler  [25] , Tracker  [24] , CellSerpent  [7] , CellX  [12]  and CellStar. The tools are listed in electronic supplementary material, table  S1. For each algorithm, we dedicated 6 h to tune the tool parameters to the benchmark. During this time, parameters were changed to improve the results, the quality of which was assessed by visual inspection.
This approach mimics the typical parameter tuning made by users of imaging software tools. In the case of CellStar, we used a combination of manual and automated parameter searches. For the other tools, because automated parameter search strategies were not available, parameter tuning was necessarily manual. The results of the tools on the benchmark are summarized in table  1. In all tests, CellStar obtained the best results for segmentation accuracy, tracking quality and long-term tracking quality. The other software performed well, but none of them was able to obtain consistent results on all test sets for segmentation and tracking. It is worth remarking, however, that the results of the comparison should be interpreted with care, because the results of the tested tools may be different with other parametrizations of the tools, and because not all tracking tools have been included in our test.
We compared CellStar with six other software solutions: CellID  [1] , IBSOBT via CellProfiler  [25] , CellTracer  [6] , Tracker  [24] , CellX  [12]  and CellSerpent  [7]. In all tests, Cell-Star obtained the best results for segmentation, tracking and long-term tracking accuracy. These result should be treated with caution, because every algorithm that we evaluated required parametrization and we cannot guarantee that optimal parameters were found in all cases. In order to automate the comparison of different tools and have an objective measure of image-processing quality for segmentation, tracking and long-term tracking, we built Evaluation Platform, which allows the automatic evaluation and comparison of any segmentation and tracking tool, thanks to benchmarks based on human-supplied ground truth data.
Benchmark construction We imaged a pSTL1-yECitrine-HIS5, Hog1-mCherry-hph S. cerevisiae strain derived from the S288C background  [24]. Yeast cells were placed in a microfluidic chamber limiting the growth of the colony to monolayers. We used an automated inverted microscope (IX81; Olympus) equipped with a 100Â oil immersion objective (PlanApo 1. 4 NA; Olympus) and a QuantEM 512 SC camera (Roper Scientific). The resolution of the camera is 512 Â 512. Brightfield images were taken every 3 min (50 ms exposure time). Raw images are analysed by an algorithm under investigation, and the outputs of analysis, stored in similar format as ground truth annotations. EP computes precision, recall and F scores for all datasets on three problems: segmentation, tracking and long-term tracking. EP outputs contain numerical values summarizing the performance over all datasets, plots providing insights into performance over series of frames as well as annotated images allowing investigation of the performance within the frame (more details in electronic supplementary material, figure  S4 ).
Performance evaluation We computed and compared the quality of segmentation, tracking and long-term tracking of seven different tools. Each tool required some manual parametrization. Because an exhaustive search was not possible, we fixed a maximal amount of time (6 h) to be spent on manual, or in the case of CellStar, manual and automated parameter search for each software. From one to three images per test set have been used to tune parameters. Although the resulting parameters depend on the user and its knowledge of the parametrizations used in the image-processing algorithms, this approach mimics the typical usage of the tools and results should reflect both the quality of the underlying algorithms as well as their usability  [33]. The quality of the analysis is evaluated using standard criteria.
For each ground truth contour, the contour with the highest rank is selected. The 'cost' of the parameter set is then defined as the root mean square of the match measures of all the selected contours. In both cases, the costs can be minimized using simulated annealing or global search (simulannealbnd and globalsearch in Matlab). Computation times may vary considerably from minutes to days, depending on the number of ground truth cells, the shape of the cells and the features of the images (contrast, lighting, noise, etc). In our experience on the test set images, good results are obtained within a few hours starting from a random parameter set, and a few minutes are enough for incrementally improving a parameter set after the addition of some new contours to the ground truth.
Funding. This work was supported by the  Agence Nationale de la Recherche  ( ICEBERG ANR-10-BINF-06-01 ,  COGEX ANR-16-CE12-0025 ,  MEMIP ANR-16-CE33-0018 ) and the  Who am I? Laboratory of Excellence  ( ANR-11-LABX-0071  and  ANR-11-IDEX-0005-01 ).
This research was partially supported by Labex Digi-Cosme (project ANR-11-LABEX-0045-DIGICOSME) operated by ANR as part of the program "Investissement d'Avenir" Idex Paris-Saclay (ANR-11-IDEX-0003-02) and by the ANR INS Project CAFEIN (ANR-12-INSE-0007).
⋆ This research was partially supported by Labex Digi-Cosme (project ANR-11-LABEX-0045-DIGICOSME) operated by ANR as part of the program "Investissement d'Avenir" Idex Paris-Saclay (ANR-11-IDEX-0003-02) and by the ANR INS Project CAFEIN (ANR-12-INSE-0007).
characterizes the spatio-temporal variability in geophysical data over a wide range of scales with only a limited number of scale-invariant parameters. This work aims to clarify the link between multifractals (MFs) and more conventional weather descriptors and to show how they can be used to perform a multi-scale evaluation of model data. The first part of this work focuses on a MF analysis of the climatology of precipitation intensities simulated by the COSMO numerical weather prediction model. Analysis of the spatial structure of the MF parameters, and their correlations with external meteorological and topographical descriptors, reveals that simulated precipitation tends to be smoother at higher altitudes, and that the mean intermittency is mostly influenced by the latitude. A hierarchical clustering was performed on the external descriptors, yielding three different clusters, which correspond roughly to Alpine/continental, Mediterranean and temperate regions.
To this end, a large-scale analysis of 5 years of simulated precipitation intensities from the COSMO (Consortium for Small-scale Modeling) numerical weather prediction model is conducted. The MF properties of the corresponding climatology of precipitation intensities are then studied and related to several regional and meteorological descriptors. The second part of this paper extends the work of  Gires et al. (2011)  over Switzerland for three different meteorological situations (snowstorm, convective summer precipitation and stratiform rainfall) by using simulated precipitation intensities from the COSMO model run with different microphysical parameterizations. This article is structured as follows: in Sect. 2, the COSMO model and the Swiss radar composite are briefly described. The studied events, radar datasets and model variables are then described in detail.
Climatological study In the first part of this work, a MF characterization of all precipitation intensities simulated by COSMO during 5 years is performed. The computed MF parameters are then com-pared with various descriptors. A total of 43 115 hourly time steps of COSMO simulations in analysis mode covering a period of 5 years (2011 to 2016) were retrieved from the Me-teoSwiss archives. These data are available at a 2 km resolution (0. 02 • angular resolution) using a setup known operationally as "COSMO-2" (COSMO, 2015), which was used for forecast and analysis until the beginning of 2016. The first and last 100 km of the COSMO-2 domain along both longitudinal and latitudinal directions were discarded from the analysis in order to avoid border effects. The remaining area is shown in Fig.
QPE data In Sect. 5, precipitation intensities at the ground simulated by the COSMO are compared with the QPE product from the Swiss operational radar composite. The Swiss radar composite consists of plane position indicator measurements of × 83 m clutter-free radar bins are corrected for partial beam-blocking and averaged to derive polar 1 • × 1 • × 500 m radar bins. Reflectivity measurements are then converted to equivalent precipitation intensity with a  Marshall and Palmer (1948)  Z-R relationship. The precipitation estimation at the ground is extrapolated from multi-radar observations aloft using a weighting function that depends on the altitude above the ground and the radar visibility. The correction for the vertical profile of reflectivity (VPR) is done with an average profile based on aggregation over a few hours and over the visible part of the area located less than 70 km from the radar.
A proper correction using fractional integration would allow for a much smoother correction, since it is proportional to H , but is computationally intractable because of the very large dataset that is used. In order to test the effect of zeros on the overall analysis, the MF parameters were also estimated in space 2 by using only the fields where there is precipitation over at least 50 % of the surface. This did, however, not impact the main conclusions in terms of correlations and spatial structure of the MF parameters. Hence the subsequent study was performed on the raw precipitation fields without any kind of filtering.
Acknowledgements. The authors would like to thank  MeteoSwiss  for access to the Swiss operational radar composite as well as the initial and boundary conditions used in the COSMO simulations. The authors thank the  Partenariat Hubert Curien -Germaine de Staël  (Projet  32709UK ) for financial support that made this collaboration possible. The authors also thank  Tim Raupach  for proofreading of the final version of the manuscript. Edited by: Graham Feingold Reviewed by: two anonymous referees.
The Spliced Transcripts Alignment to a Reference software (STAR), specially designed for aligning long reads  20  , was then used to identify DMD canonical splice junctions as well as potential new ones from junction-spanning reads. Compared to TopHat2, a popular mapper suitable for processing relatively short reads, STAR performed better both in the total mRNA-Seq and our DMD-targeted mRNA-Seq datasets (Table  1 ) for the positioning of the 78 canonical junctions and the discovery of alternative splice junctions as illustrated with pseudoexon 1a, an already known case of pseudoexon insertion in the DMD transcript  21. Splicing pattern of DMD transcript in skeletal muscle tissue. Four independent skeletal muscle tissue samples from normal young men were used to establish the splicing pattern of the muscular DMD transcript.
While some events have already been reported (del9, del71, del78, PE1a), the other ones are here described for the first time (Supplementary Table  S2 ). The very low level of detected ASEs precluded their experimental validation by RT-PCR in most cases, but we were able to conduct these analyses for three of them (del71, del78, and PE1a). We performed either standard or fluorescent RT-PCR using primers that amplify both the included and the skipped isoforms, and analysed PCR products by gel electrophoresis or fragment analysis on a capillary sequencer, respectively. The three ASEs were successfully validated (Fig. 3 ), with a greater concordance between RNA-Seq and fragment analysis data (Table  3 ). Table  1. Performance of TopHat2 and STAR spliced aligners on Total mRNA-seq and DMD mRNA-seq data for the detection of exon junctions in the DMD transcript.
"Exon junctions: all" refers to the number of detected junctions in the RNA-seq datasets including both canonical (n = 78 in the DMD gene) and new junctions due to alternative splicing events. Mean coverages given by TopHat2 and STAR for canonical junctions and for pseudoexon 1a (PE1a), a well-documented example of pseudoexon insertion in the mature muscular DMD transcript, are indicated. In addition to exon skipping events, the RNA-Seq analysis has revealed the usage of alternative splice sites located either at proximity of the natural ones or deep in the introns (Table  2 ). The reliability of the bioinformatics analysis for these new sites was systematically verified by inspection of the raw sequencing reads. In particular for pseudoexons, we verified that the whole sequence of the pseudoexon could be retrieved from a single read.