dataesr/scientific-paragraphs-categorization huggingface dataset:
We performed statistical tests to assess if TinGa's mean score was significantly higher than the mean scores of the four other methods on the different trajectory types. The P-values associated with these one- (f) The time every method took on datasets of differing numbers of cells (from less than 500 cells to 10 000 cells), in seconds. We represented the time on the y-axis on a log scale, so that differences between methods that were fast on large datasets such as TinGa and Monocle 3 could be seen Fig. 7. Methods on the x-axis are ordered by the number of datasets on which they outperformed the others. The y-axis represents the number of datasets on which each method had the best mean score across all methods. For each method, bars represent the different trajectory types for which the method performed best.
These bars are ordered and coloured from most simple (in light yellow) to most complex trajectory type (in dark red) sided t-tests can be seen in Table  3 , which contains P-values associated with paired t-tests computed on the 178 synthetic datasets. TinGa consistently performed significantly better than RaceID/ StemID across all trajectory types. It also significantly outperformed Monocle 3 and PAGA on simpler trajectories such as linear, bifurcating, converging and cycles. On the other hand, the mean scores of TinGa were significantly higher than the mean scores of Slingshot on more complex trajectories such as trees and acyclic graphs (with a P-value of 0. 05). For each of the 178 synthetic datasets, we determined which of the five tested methods performed the best. The results are presented in Figure  7.
TinGa had the best score on 68 out of the 178 datasets. We also observed that TinGa was the method that performed best on the greater diversity of synthetic trajectory types. Monocle 3, the second-best method that outperformed the other methods on 42 synthetic datasets, mainly showed its best performance in two types of trajectories: trees and graphs. Slingshot, the third-best method that outperformed the others on 28 synthetic datasets, mainly outperformed the other methods on simpler trajectories, from linear to cycles, while PAGA and RaceID/StemID performed best on trees. On the other hand, TinGa outperformed the other methods on linear, bifurcating, cyclic, tree, acyclic and graph trajectories.
Real datasets Figure  8  shows violin plots of the scores of the five TI methods we tested on real datasets. These results were split between datasets with a silver and a gold standard. Datasets with a gold standard are datasets for which external information such as cell sorting or cell mixing were used for validation of the trajectory. In datasets with a silver standard, the ground truth trajectory was extracted directly from the expression data, typically by clustering and validation by experts. In datasets with a silver standard, we observed results that were comparable to the results previously shown on synthetic datasets. TinGa, Slingshot, Monocle 3 and PAGA were the methods that had the best correlation and F1 branches scores, as can be seen in Figure  8a  and b, respectively. As observed previously, Monocle 3 outperformed Slingshot on the feature importance score (Fig.
8c ). In the case of datasets with a silver standard, it not only performed better than Slingshot but also TinGa and PAGA on the topology HIM score (Fig. 8d ). Overall, the mean scores of TinGa and Slingshot were relatively spread from mediocre (0. 25) to very good scores (>0. 8) compared to Monocle 3 and PAGA, which returned more consistently mean scores around 0. 55 on the real datasets with a silver standard. As observed on synthetic datasets, the scores of RaceID/StemID were greatly affected by the fact that it failed to return results on the large datasets, due to memory issues. We compared the time necessary for each method to run (Fig. 8f ). TinGa was the fastest of the five TI tools. It took 11 s on average to run on small datasets and only 21 s on average on datasets containing more than 10 000 cells.
Monocle 3 had very similar results on small datasets, but it took twice longer than TinGa on our largest datasets. Moreover, the method crashed on nine datasets. PAGA took slightly more time to run on large datasets, needing more than 3 min on average to run on datasets containing more than 10 000 cells. This method did not work on all datasets either: it crashed on 17 of them. RaceID/StemID was the second slowest method and already needed a few minutes to run on medium datasets. This method systematically crashed on datasets of more than 5000 cells, which represents 69 datasets. Slingshot and TinGa were the only methods that returned a result for all the 250 real and synthetic datasets on which they were tested. However, Slingshot was the least scalable of the five methods that we tested, and ran for more than 2 h when applied to the largest dataset of the study that contained 19 647 cells.
In comparison, TinGa took 23 s on the same dataset. All methods performed significantly worse on datasets with a gold standard compared to silver-standard datasets. Since the validation of these trajectories does not rely on the data itself but on external measures, it might not reflect the processes in the data exactly and be more complex to infer. Even though Slingshot and Monocle 3 returned significantly lower correlation and featureimp_wcor scores than on the real datasets with a silver standard (Fig  8a  and c ), these two methods had the highest mean scores on datasets with a gold standard (Fig. 8e ). The mean score of TinGa on these datasets  was slightly lower than its results on silver and synthetic datasets, and the mean score of PAGA completely dropped on these datasets, never reaching a value higher than 0.
5. This might in part be explained by the fact that datasets with a gold standard consisted mainly of linear and bifurcating trajectories, two trajectory types on which Slingshot tends to excel, while PAGA can over-estimate these datasets complexity (see Fig. 9 ). The number of real datasets on which each method performed best are presented in Figure  9 , where results on datasets with a silver and a gold standard are shown separately. TinGa outperformed the other methods on 18 silver-standard datasets, which ranged from simple linear to most complex disconnected trajectories. As observed previously in synthetic datasets, PAGA performed best on a majority of tree trajectories. Slingshot and Monocle 3 returned the best results on 16 and 14 real silver-standard datasets, respectively. These two methods also performed best on a majority of the simple real datasets with a gold standard, while TinGa performed best on the only real complex dataset with a gold standard.
We then performed statistical tests on the real datasets to assess whether TinGa's mean score was significantly higher than the mean scores of the four other methods on any trajectory types. Table  4  shows the results of the one-sided paired t-tests that we performed, and contains the P-values computed among the real datasets. Since there was only one real dataset containing an acyclic graph, we could not compute any statistics on this trajectory type. As observed previously in synthetic datasets, TinGa consistently performed significantly better than RaceID/StemID across all trajectory types, except for real cyclic datasets. Moreover, the mean scores of TinGa were significantly higher than the mean scores of PAGA on both cyclic and linear datasets, and higher than the scores of Slingshot on the more complex trees and disconnected graphs (with a P-value of 0.
05). Figure  10  is shown as an example of the trajectories returned by the different methods on a real linear dataset. On this dataset, TinGa and Slingshot accurately retrieved a linear trajectory that was similar to the real trajectory (at the top left of the figure). The cell ordering was therefore optimally retrieved by these two methods, while PAGA for instance found a trajectory that diverged greatly from the ground truth, and reordered the cells in a very different way. The trajectory identified by Monocle 3 consists of many nodes, and even though it globally resembles the ground truth, it identified two noisy micro-structures: a branch and a cycle. In this case, the mean score of Monocle 3 was therefore impacted by the fact that the topology it returned was more complex than expected, which resulted in a low HIM score.
Topology bias In order to further investigate the type of trajectory topology that TinGa would return compared to other methods, we then focused on the bias in topology. Saelens et al. (2019)  had already highlighted the fact that some TI methods such as PAGA, tended to overestimate the complexity of a trajectory, while other methods, amongst which Slingshot, typically under-estimated the complexity  of a dataset. We assessed the difference in topology between the trajectories returned by the five TI methods tested in this article and the real data topologies (Fig. 11 ). Our results confirmed that PAGA, and also RaceID/StemID returned too complex trajectories when facing linear or cyclic datasets. We observed the same trend in Monocle 3, which also tends to reconstruct too complex topologies on linear or cyclic datasets.
We also observe that RaceID/StemID tends to return extremely complex trajectories compared to ground truth, irrespective of the real topology in the data. On the other hand, slingshot and TinGa accurately returned linear topologies when facing simple datasets. We report however that Slingshot tends to model cyclic trajectories as linear, an error that TinGa typically circumvents. If we then focused on more complex datasets, such as converging, bifurcating or multi-furcating trajectories, we noticed that TinGa, PAGA and Slingshot were relatively unbiased towards the topology complexity. Monocle 3 and RaceID/StemID, on the contrary, tended to return overly complex trajectories for these topologies. Finally, if we focused on the most complex datasets on which we performed our comparison, we noticed that methods that tended to find too complex topologies in simple datasets performed more accurately on complex datasets.
PAGA showed no bias in topology on disconnected graphs and showed only a slight bias in the direction of more simple topologies when applied to connected or acyclic graphs. Slingshot, on the other hand, under-estimated the complexity of disconnected, connected, acyclic and tree graphs. We observed the same trend in TinGa for the two last-mentioned topologies, but the bias was much less pronounced that the bias observed for Slingshot. All methods seemed to struggle with finding the right topology for tree datasets.
Discussion So far, every new TI method that was published compared its results to a maximum of 10 other methods (which were not necessarily selected among the best ones), on a maximum of 10 datasets. In this work, we presented an extensive comparison of TinGa to four of the best existing TI methods to our knowledge on 250 datasets. This allowed us to clearly establish the relative performance of each method in a minimally biased setting, since adding more datasets automatically reduces the possibility that we would over-estimate the performance of our method. The datasets on which we tested TI methods were either generated by one of four different simulators or real single-cell RNA-seq datasets. This allowed us to test different aspects of the methods. In synthetic datasets, we have the advantage of having a refined gold standard, with information on every cell's state of progression in the trajectory we simulated.
Testing the methods on real datasets is of course essential, but in these datasets, a gold standard is more difficult to extract, and is usually based on a grouping of cells into time points or clusters, which is less refined than the single-cell information obtained in synthetic datasets. The TinGa method showed a very good performance on average on all types of trajectories, while we observed that Slingshot performed best on simple trajectory types, and PAGA and Monocle 3 were more prone to reconstructing complex trajectories types. Slingshot relies on two steps of first clustering the lowdimensional data and then fitting principal curves through these clusters. This results in the Slingshot trajectory typically being very well correlated with the gold-standard trajectory, since it follows the principal density structures in the data.
PAGA also starts with a clustering step, but the method then significantly differs from Slingshot since one small graph is then built per cluster. Several steps of refinement then allow linking the subgraphs that need to be linked while keeping separate the components that should not be merged, which allows the method to recover disconnected trajectories. This approach typically leads to more convoluted trajectories. Monocle 3 has a similar approach to PAGA, since it also performs clustering followed by a step where a principal graph is built for each cluster. Several refinement steps are then applied in order to produce a clean final graph, among which merging the subgraphs that should be linked. From what we observed in Figure  11 , the similarities between PAGA and Monocle 3's methodologies are reflected in the way they model simple trajectories, since they both tend to return more complex trajectories than needed when applied on linear or cyclic datasets.
In this setting, we observed that TinGa was a promising TI method. Its performance is comparable to Slingshot on simple datasets, but also accurate on complex trajectories where it performed equally well and sometimes outperformed the PAGA and Monocle 3 methods. In a field as complex as is TI, we believe that more than one TI tool should be used at the same time, to increase understanding of the data. We provide TinGa, a method that is applicable to a wide range of trajectory types, and can play a role in the inference of complex disconnected trajectories, a problem that very few methods are able to tackle for now, while still being accurate on simple trajectories.
VENµS SATELLITE Launched in 2017, dedicated to vegetation and land monitoring, VENµS scientific mission is a unique combination of specific features: high spatial resolution (5m), high revisit (2-day orbital cycle), and high spectral resolution (12 spectral bands from 420 to 910 nm)  (Dedieu, 2006). 123 sites on Earth, representative of different ecosystems and climatic zones, are currently acquired. Freely available on THEIA land data portal (https://www. theialand. fr/en/product/venus), VENµS data provides opportunities to validate image processing algorithms and study the land surface evolution for a wide range of surface types (ice, vegetation, soils. ).
The resulting image stacks are first processed with a custom program running under an open-source image analysis package (Icy) to measure the surface of each leaf disk over time. We further developed statistical procedures running under the R software, to analyze the time course of the feeding activities of the larvae and to compare them between treatments. As a test case, we compared how European corn borer larvae respond to quinine, considered as a bitter alkaloid for many organisms, and to NeemAzal containing.
These approaches are well suited to laboratory investigations on small scale series, but handle a limited number of repetitions and often make use of specialized and expensive hardware. Here, we describe a fast and reliable testing protocol that can be performed using a minimum of hardware components  [20] , based upon measuring the consumption of leaf disks by larvae. Our approach combines three elements (i) a novel and flexible feeding bioassay whereby the consumption and movements of 150 larvae can be followed across several days with a webcam, (ii) programs to analyze the stacks of images (RoitoRoiArray and AreaTrack), written as plugins running within the free bio-image software ICY  [21] , and (iii) a new cutting-edge statistical approach to compare the kinetics of larval feeding  [22] , taking into account the diversity of behavioural responses in the insect population.
The bottom of each plate is made of a transparent glass plate. Each cage is first filled with a 2-3 mm layer of agar on which a freshly cut leaf-disk is deposited. Then one larva is placed in each cage and a second glass plate is placed on the top to close cages. The cost of one system (including the webcam, the LED panel and the plates) is less than 400 $ US. The camera can record images at regular intervals (1 per minute) using a surveillance camera security software (VisionGS). This allowed us to register the feeding behaviours of 150 larvae, distributed into three plates, during several days. Each plate received a different treatment corresponding to different concentrations of antifeedant product. One standard computer could easily control 4 webcams that is to say it is possible to record feeding bioassay tests with 600 larvae at a time on one computer.
The limiting factor is the time required to introduce insect larvae into the cages. For this reason, it is difficult for one operator alone to start more that two experiments including 300 larvae at a time, as was done here. In this paper, a single bioassay or batch corresponded to two cameras, each monitoring three plates. ii) An open-source software for image analysis: the analysis of the image stacks (1920 x 1080 pixels; RGB 24 bits) is performed with 2 plugins written in Java, and running under the opensource bio-imaging software, Icy  [21]. The main steps of image analysis are described in Fig 2. A first plugin, RoitoRoiArray is used to help users creating areas defining the contour of each cage, and attributing a unique ID number to each one. A second plugin, AreaTrack is used to measure the leaf area at each time point.
iii) Freely-available R-scripts for data-processing and model-based data analysis: We propose a statistical method to compare treatments considering the variability of individual behaviours. One experiment may consist in several bioassays/batches. The statistical unit is a curve corresponding to the consumption of a single leaf disk measured over time  (Fig 3. 1). In this paper, we ran a total of four batches to compare the larvae responses to different concentrations of NeemAzal and quinine. Each batch consisted in one bioassay with two cameras moninoring a total of six plates (Table  1 ). Five concentrations of each of the two antifeedant product were tested. Controls consisted in a water treatment without any antifeedant. For each treatment, two plates were done and distributed among the batches (see Methods for details).
Overall, we obtained 1200 consumption curves. Data analysis includes two steps. First, we define a typology of possible behaviours taking into account the whole dataset. Second, we compare the distribution of behavioural types between treatments of the same product. The different steps of the analysis, including data processing and data analysis were coded into R scripts that are freely available, along with the user manual  [22]. As an example, distribution of behavioural types in three treatments, NeemAzal 10mM, Water (control), and Quinine 100mM are represented as pie charts. Each line sums to one.
Bioassays confirm NeemAzal as an antifeedant for ECB larvae After using the whole dataset to attribute a behavioural type to each individual (Fig  3 ), the effect of each treatment (i. e. antifeedant concentration) was analyzed separately for each antifeedant product by a multinomial regression (see Methods). Each analysis included the data of each dose of antifeedant and the controls, and resulted in the estimations of the probability distribution among behavioural types for each concentration of each product that are represented on Fig 4. A Wald test was used to test the significance of the comparisons between treatments (SI-Table  1  and SI-Table  2 ).
The plates and the stand were placed on top of an A3 white LED light panel (white 4000K; Display Concept, Brussels, Belgium), lying flat on a table. In this paper, one bioassay consisted in setting-up two experimental systems at a time, with simultaneously monitoring six plates with two webcams. Instructions for the making-up of the experimental system are deposited on a dataverse  [24]  and are freely available. Plates were designed with AutoCAD software with a 3D printer (Ultimaker 2+) using white PLA (polylactic acid) filament (Ref. RS Stock No. 134-8192), at a resolution of 0,6 mm  [24].
Image acquisition and analysis Image acquisition: Webcams (Logitech Pro C970) were connected to the USB port of a PC computer, with sufficient disk memory to store the images. Notice that one standard computer could easily control the 4 webcams, although we only used two webcams here. Time-lapse images were taken every minute by a video surveillance software (VisionGS BE 3. 1. 0. 4; http://www. visiongs. com) as 1920 x 1080 pixels jpg files, named with a time stamp. Image analysis: Image analysis of the stacks was performed semi-automatically, with the help of custom programs working within the bio-imaging open-source program ICY  [21]. In order to follow the consumption of each leaf-disk, regions of interest (ROIs) must first be defined to locate each cage or disk. This is done semi-automatically with a plugin called RoitoRoiArray (http://icy.
Data analysis Series of R scripts were written for data processing and statistical analysis. The scripts, along with the raw data and a documentation were deposited in a dataverse  [22]  and are freely available. Metadata: raw files were stored using lab storage facilities on specific directories. Directories names contain information about the date of the experiment and the camera number. Csv filenames contained all the information about the experiment: date, camera ID, plate ID, insect population, plant genotype, plant environment, plant coordinates, substance name, substance concentration. After image analysis, all csv files to be analysed together were copied under a single sub-directory Standardization: first, pdf images of leaf consumption through time are produced. Each pdf file corresponds to one plate and contains 50 plots, one for each cage.
Cages that produced abnormal plots, identified by their row and column coordinates, were manually removed from the analysis. Then, we standardized the data by dividing the leaf area by the total leaf area at time t0. Hence, the basic measure became the fraction of intact leaf disk from each cage at each time-point. We defined tmax=2500mn (40 hours), and only retained the data taken before tmax. Data clustering: the whole dataset, comprising the experiments performed with NeemAzal quinine and the control (four batches, 24 plates, 1200 cages corresponding to 1200 consumption curves), was used to run unsupervised clustering algorithm SOTA  [23]  on the individual curves in order to obtain 14 clusters. The total number of clusters was empirically chosen to avoid having clusters containing only a couple of curves and proved to be robust over the different experiments.
Introduction : One of the main tasks of bioinformatics is to associate biological roles to proteins using the always increasing biological data (1,2). To predict the function of a protein based on its sequence, computational methods look for sequence patterns in biological databases of known and already annotated proteins. Homology search (3,4), motif search  (5)  and functional PickPocket: Pocket binding prediction for specific ligand families using neural networks. domain search  (6, 7)  are the most common methods, among the many available tools. Other strategies exploit different data types, such as gene expression  (8)  or even a combinatorial approach  (9, 10). Most of the protein biological functions occur through interactions with other proteins or ligands  (11). The residues making the protein contact surface and cavity shape are often located far away within the protein sequence.