dataesr/scientific-paragraphs-categorization huggingface dataset:
By analyzing paired-end reads that mapped near each fosmid-predicted breakpoint, we could infer the genotypes: only 23 breakpoints could be confidently assigned to a homozygous genotype (i. e. with less than five read pairs spanning the breakpoint). The find module recovered 11 of them. Of the remaining 12 likely homozygous sites, the breakpoints of 8 of them were included in a large gap (!k) in the reference binary string. This suggests that these sites were close to other form of polymorphism, which would explain why MINDTHEGAP did not detect them. Among the 20 detected insertions by the find module, the fill module succeeded in reconstructing correctly two inserted sequences of sizes 4137 bp and 6729 bp, with respectively  Note. Simulated insertions of size 1000 (homozygous). The number of deletions simulated in the reference genome appears in the column 'N sim.
Time and memory performance Figure  4  shows the total runtime and maximal memory used by MINDTHEGAP and SOAPindel on the real C. elegans dataset. The machine used for all tests is a 12-core Intel E5-2640 @ 2. 50 GHz with 192 GB of memory. For MINDTHEGAP, the breakdown of the three steps index, find and fill shows that the major part of running time is spent on the index step. For SOAPindel, the time required to map the reads to the reference with bwa is included in the total time; however, SOAPindel alone remains slower than MINDTHEGAP as a whole. SOAPindel used eight threads and MINDTHEGAP only one. Importantly, even though MINDTHEGAP stores in memory the whole de Bruijn graph of the C. elegans read dataset, its memory peak (0. 7 GB) is six times lower than SOAPindel. On the NA12878 dataset with 2.
DISCUSSION MINDTHEGAP is the first integrated method to detect and assemble insertion variants of any size and any type, using modest computing resources. The find module of MINDTHEGAP differs from most other existing methods by not relying on read mapping. Instead, the de Bruijn graph of reads is compared against the reference sequence, which enables fast and low-memory analysis. However, one current limitation of the find module is that it fails to detect insertions when other polymorphism occurs near the insertion site. Improvements to waive this limitation are under development, based on a more detailed analysis of gaps longer than k. Furthermore, the method could also be used to output SNPs and other types of structural variants. Long insertion variants are challenging to detect and assemble; thus, there is a shortage of tools to compare MINDTHEGAP with.
We compared our results with SOAPindel, which is a popular indel detection software limited to short insertions. The NovelSeq software  (Hajirasouliha et al. , 2010)  is designed to find and assemble large insertions, and therefore would have been another candidate for comparison. However, despite several attempts and reaching out to the author, we were unable to run the software successfully on any of our datasets (the novelseq_cluster step ran indefinitely). NovelSeq relies on a complex pipeline, and we conjecture that it may be tailored to specific data types. While most other insertion detection methods require to run external software, MINDTHEGAP is stand-alone and is therefore easy to use. If needed, the modular organization of MINDTHEGAP allows users to replace the find module with the results of a classical insertion detection based on paired-end mapping.
The fill module could also be used as a de novo assembly finishing step, i. e. gap-filling between adjacent contigs in scaffolds, although we did not evaluate its performance for this task. One important design choice for the fill module is to perform assembly with all the k-mers in the read dataset. This enables to assemble not only novel insertions, but also duplicated insertions and transposition events. Classification of assembled insertions into the different event types is not done by MINDTHEGAP, but can be done by re-mapping insertions to the reference genome. One drawback of considering all reads during insertion assembly is that the de Bruijn graph becomes more complex to analyze. An important future work will be to improve the recall of the fill module by using paired-end reads information to guide traversal of contig graphs.
As repeated regions are notoriously difficult to assemble, we anticipate that our approach might not be effective  for mobile element insertions. However, there exist methods tailored to the assembly of MEI, based on local assembly with recruitment of mate reads. Our tests on the NA12878 dataset showed there is room for improvement: only two long homozygous insertions were successfully assembled out of 23 predicted ones. We postulate that (i) polymorphism or repetitions near the insertion sites hinder detection by the find module, and (ii) the complexity of the human genome makes de novo assembly of large contigs difficult. As no other tool was able to assemble long insertions, we could not assess whether our results were owing to weaknesses in our method, or to specificities of this particular dataset (complex insertion sequences or mispredicted insertions).
, 2001). The OI method was also used in a simplified radiative transfer model by  Huneeus and Boucher (2007)  to assimilate synthetic observations of MODIS (Moderate Resolution Imaging Spectroradiometer) and CALIPSO (Cloud-Aerosol Lidar with Orthogonal Polarization). Adhikary et al. (2008)  assimilated monthly mean AOD data from MODIS and AERONET using the OI method to produce three-dimensional distributions of AOD over Asia. Niu et al. (2008)  improved dust storm forecasts (dust concentrations) over China by assimilating satellite retrieval data and surface meteorological station data. Tombette et al. (2009)  used the OI method over western Europe for assimilating PM 10 (particulate matter with an aerodynamic diameter lower than 10 µm) mass concentration observations from the BDQA (Base de Données de la Qualité de l'Air) network.
Observations for validation We employ two independent data types for DA validation: surface mass concentration measurements (i. e. PM 2. 5 and PM 10 ) and AOD data. The surface mass concentration measurements are from the BDQA (Base de Données sur la Qualité de l'Air, the French national database for air quality which covers France) network, the Barcelona network (three stations), the EMEP-Spain/Portugal network, and the EMEP-Europe database (see Fig. 1 ). The French and Barcelona networks (triangles in Fig. 1 ) provide hourly averaged mass concentration measurements of PM 2. 5 and PM 10. The EMEP-Spain/Portugal and EMEP-Europe networks (squares in Fig. 1 ) provide daily averaged mass concentration measurements of PM 10. The number of used stations in the BDQA, Barcelona, EMEP-Europe and EMEP-Spain/Portugal networks, which provide PM 10 or PM 2.
5 measurements in July 2012, is shown in Table  3. The BDQA network provides the most measurements, with 240 stations for PM 10 and 70 stations for PM 2. 5. The hourly AOD data at 355 nm are derived by level 2. 0 (cloud-screened and quality-assured) AOD data at 340 and 380 nm retrieved from AERONET (AErosol RObotic NETwork, http://aeronet. gsfc. nasa. gov/) following the Ångström law  (Wang et al. , 2014). The locations of the AERONET stations considered (e. g. stations that are close to the lidar network and that provide the level 2. 0 AOD data in the pre-ChArMEx/TRAQA and ACTRIS/EARLINET campaign) are shown as orange diamonds in Fig. 1. Thirteen AERONET stations are used for validation in this paper (see Table  3 ).
Hence, in this section, we investigate the impact of the assimilation altitude range on aerosol forecasting. We perform three DA tests (0. 75-3. 5 km a. g. l. , 1. 5-3. 5 km a. g. l. and a reference case 1. 0-3. 5 km a. g. l. in Fig. 10 ). As shown in Fig. 10 , assimilating lidar signals from 0. 75 to 3. 5 km a. g. l. (magenta lines) leads to similar scores (with respect to hourly data of BDQA) as assimilating from 1. 0 to 3. 5 km a. g. l. (a reference case, red lines). A first explanation is that the observation variance (sum of instrumental and representativeness variances, from the χ 2 diagnosis) of the model level of 0. 75-1. 0 km a. g. l. is set higher than those of the model levels from 1. 0 to 3. 5 km a. g. l. A second ex-planation is that the scores in Fig. 10  are computed using the observations from the BDQA network, where most improvements are from assimilation of lidar signals in Spain or Portugal (see Figs.
4  and 6 ). However, of the lidar stations in Spain, only Madrid and Granada provided data between 0. 75 and 1. 0 km a. g. l. (see Figs. 2  and 3 ). In addition, assimilating lidar signals from 1. 0 to 3. 5 km a. g. l. (magenta lines) leads to slightly better scores than from 1. 5 to 3. 5 km a. g. l. (black lines). ) and the correlation of PM 10 (or PM 2. 5 ) averaged for each of the six different experiments. The scores are computed for the BDQA network (hourly data). The vertical black lines denote the separation between the 12 h assimilation period (to the left of the black lines) and the 60 h forecast period (to the right of the black lines). The "DA L h = 50 km", "DA L h = 100 km" and "DA L h = 200 km" simulations correspond to an assimilation altitude range from 1. 0 to 3. 5 km. The "DA 0.
Conclusions In this paper, a data assimilation (DA) algorithm based on the optimal interpolation (OI) method is used to assimilate lidar signals (normalised PR 2 ) for aerosol forecasts over Europe. The lidar data were retrieved from a 72 h period of intensive and continuous measurements in July 2012. The measurements were performed by 12 ground-based lidar stations of ACTRIS/EARLINET in the Mediterranean basin and one station in Corsica which was set up in the framework of the pre-ChArMEx/TRAQA campaign. First, we studied the impact of the length of the lidar DA period on aerosol forecasts. We found that 24 h DA leads to slightly better forecasts than 12 h DA. However, the differences between 24 h DA and 12 h DA are small after 6 h of forecast. Furthermore, because the impact of lidar DA lasts longer than 36 h in the forecast period, we have used 12 h as the assimilation period length in this paper.
Acknowledgements. This work was supported by  CEA  ( Commissariat à l'Énergie Atomique ) and  CEREA joint laboratory École des Ponts ParisTech -EDF R & D. We thank all participants of the  EARLINET network and MISTRALS/ChArMEx (Chemistry-Aerosol Mediterranean Experiment , http://charmex. lsce. ipsl. fr/)for the 72 h continuous measurements. Forecasts and near-real-time quick looks for the lidar measurement alert have been provided by the  MISTRALS/ChArMEx Operating Center  (http://choc. sedoo. fr/)set-up at  OMP (L'Observatoire Midi-Pyrénées ), Toulouse. Lidar measurements are supported by the 7th  Framework Programme project Aerosols, Clouds , and  Trace Gases Research InfraStructure (ACTRIS) network  (grant agreement no. 262254 ). The Barcelona EARLINET lidar team thanks the  Spanish Ministry of Economy and Competitivity and European Regional Development (FEDER)  funds through project  TEC2012-34575  and  Scientific and Technological Infrastructure  project  UNPC10-4E-442 , as well as the  Spanish Ministry of Science and Innovation  and  FEDER  funds under projects  CGL2011-13580-E/CLI  and  CGL2011-16124-E/CLI.
Based on a visual inspection, the internal network architecture of an Indonesian Stylaster was described and its growth model was proposed  [23]. Thresholding was used, combined with mathematical morphology techniques (watersheds, distance transform, skeletonization) provided by a software tool called Pore3D  [26] , in order to quantify the porosity and the average diameters of the canals within two small subvolumes. Another study has used a seeded-region-growing segmentation technique and focused on the skeletonization of coral branches, so as to evaluate the tree-like structure of a whole Madracis mirabilis colony  [18]. However, this approach is rather macroscopic, as it extracts one axis (skeleton) per branch, while ignoring the internal structure. The morphological skeletonization used to quantify the canals  [23]  is also unsuited for our study.
The remaining images were contaminated by various artifacts mainly due to an imperfect immobility of the samples. While the total extent of the specimens exceeded 20 cm, the FOV of the images acquired only included small portions ranging from 5 to 32 mm. The image referred to as BASE (Fig. 6 ), as well as the long straight isolated branch in S3 dataset (on the left in Fig. 5 ) were used to train the image-processing algorithms, whereas the remaining branches in S3, as well as the images EX, M1 and M5, were used to evaluate these algorithms. The design of image processing algorithms was based on several characteristics determined by visual inspection of the images. The reader can particularly refer to Figure  3 , where examples of anatomical structures are annotated. Firstly, the gorgonin axis appears as a relatively dark and homogeneous (albeit noisy) region in the center of each branch.
All the imageprocessing algorithms and the graphical user interface have been implemented using the CreaTools software development platform (http://www. creatis. insa-lyon. fr/site/en/CreaTools). Pathway extraction in a canal. We adopted a tracking strategy inspired by an algorithm that extracts blood-vessel centerlines in magnetic resonance angiography (MRA) images  [31]. In that work, the tracking was performed independently in two opposite directions from a starting point. The starting point was interactively selected and next points were added in a prediction/correction scheme. The prediction used the local vessel orientation inferred from the eigenvectors of the inertia matrix. The correction (repositioning) of the new point used the centroid information and was restricted by continuity (elasticity) and smoothness (flexibility) constraints.
Evaluation Methodology To evaluate the methods proposed, the pathways automatically extracted were compared to reference pathways. As ground truth was not available, the reference pathways were interactively drawn by a trained observer. In order to assess the accuracy of the pathway extraction algorithm itself, while avoiding possible errors induced by incorrect locations of automatic seed points, the algorithm was initialized with manually selected points. Separately, we evaluated the accuracy of the automatic detection of seed points within canals, in the cases where this option was exploitable. Four datasets (EX, M1, M5 and S3) were used in the evaluation. The dataset BASE, as well as the long straight isolated branch in S3 (on the left in Fig. 5 ) were left out, since they had previously been used to train the algorithms during their development.
Pathway extraction accuracy A total of 132 seed-points located in different branches of the corals were used to extract the canal pathways. As several initialization planes were used in the datasets M5 and S3, the final number of extracted pathways was reduced to 99 after the fusion of curves following the same canal. The details are provided in Table  2. Figure  11  displays the remaining pathways in the datasets evaluated. Most of the extracted pathways (red) closely follow the reference ones (green). This visual impression is confirmed by the quantitative results. Among the 99 extracted pathways 85 were correct (paired with reference pathways) and 14 were erratic, so that the precision was PREC~85=(85z14)~0:859, while 10 reference pathways were not paired with any of the extracted pathways, which results in a recall equal to REC~85 =(85z10)~0:895.
The details per dataset are displayed in Table  3. These details demonstrate that the average overlap between the paired pathways, measured by the Dice similarity index, ranged between 79:2% (S3) and 95:7% (M5) of the pathway length, with an average of 86:9%. As for the mean distance between the paired pathways, it ranged from 34 to 66 mm. The ratio between this distance and the mean estimated canal radius in the given dataset (R canal ) was equal to 0. 31 on average.
One small canal was missed (Fig. 12(d) ). In the remaining 2 planes, in datasets EX (Fig. 2(c )) and M1 (Fig. 4(c) ), the algorithm failed to delineate the canal region, so a quick user interaction was required to grossly draw the inner boundary of this region and provide its approximate thickness by clicking a point within the outer boundary. After this manual aid, the algorithm automatically found the seed-points in all canals (Fig. 13 ). Considering only the fully automatic localization, the algorithm achieved the following values of precision and recall: PREC~108=(108z3)~0:973, REC~108=(108z24)~0:818, as 1z23 seeds were missing at that stage. Including the semiautomatic localization in EX and M1 datasets, these results respectively increased as follows: PREC~131=(131z3)~0:978, REC~131=(131z1)~0:992.
On image acquisition After several attempts, we converged towards an acquisition protocol giving satisfactory results (see settings in Table  1 ),  although the number of tested configurations was not large enough to claim that these settings actually were optimal. To the best of our knowledge, thus acquired images are the first 3D representation of the octocoral internal structure. Despite the small number of exploitable datasets acquired to date, our images were quite diversified: four different spatial resolutions ranging from 4. 5 to 25 mm, topology ranging from simple segments to several branches with bifurcations, apical segments, a ''bud'' of a new branch. Additionally, these datasets contained more than hundred different canals. The range of spatial resolutions explored was selected considering the size of the structures of interest.
Acknowledgments The authors would like to thank  Jerome Adrien  from the  MATEIS laboratory  for the image acquisitions and  Eduardo Enrique Davila Serrano  from  CREATIS  for his help in image visualization and analysis with CreaTools. Assistance and comments from BIOMMAR members were greatly appreciated, particularly from  Nestor Ardila ,  Nini Johanna Cadena  and  Carlos Edwin Go  ´mez. We thank  Natural National Parks (Colombia)  for the collecting permit #  DTC-CR-T-036-03/09.
This work was done in the framework of the  French ANR Labex PRIMES  ( ANR-11-LABX-0063 ). Alfredo Morales was supported by a  Colombian COLCIENCIAS doctoral grant. Support from ''Proyecto interfacultades'' (Vicerrectoria de Investigaciones, Universidad de los Andes)  and  COLCIENCIAS  (Grant No. 1204-521-29002 ) is greatly acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Bioinformatics analysis Rat, mouse and human LIMLE amino-acid (AA) sequences were analyzed with NCBI blast (National Center for Biotechnology Information database). To study cellular localization, the rat LIMLE AA sequence was analyzed using the AA sequence-based predictors PSORTII  [18] , TargetP1. 1  [19]  and NetNES  [20]. The probability of LIMLE to be associated with the membrane was checked by the presence of alpha-helix with TMHMM 2. 0  [21]  and NMT tools  [22]. The presence of specific signals or links with known family proteins according to the predictive secondary structure or predictive 3D structure, were analyzed by Inter-ProScan portal  [23] , Gene3D  [24] , ''Protein Homology/analogY Recognition Engine'' (PHYRE)  [21]   [25, 26]  and the ''Iterative Threading ASSEmbly Refinement'' (I-TASSER)  [27, 28]  web servers.
LIMLE protein localization and bioinformatic analysis To determine the cellular localization of LIMLE, we analyzed its amino-acid (AA) sequence by using a variety of predictive software programs including PSORTII  [18] , TMHMM2. 0  [21] , NMT tools  [22] , TargetP1. 1  [19]  and NetNES  [20]. Results revealed no transmembrane domain and a high probability for LIMLE to be located in the cytoplasm (data not shown). In addition, we detected a Nuclear Export Signal domain (NES) from AA 100 to 110 (I-K-A-Q-L-N-D-D-L-E-I), suggesting that LIMLE can circulate between the nucleus and cytoplasm. To determine its exact cellular localization, without access to specific antibodies, we transfected eukaryote cells (COS) by lipofection with a plasmid encoding the rat LIMLE full-length sequence and containing the V5 tag.
NCBI blast of the rat LIMLE sequence (gene name RGD1310371, gene ID 308794) revealed 71% and 88% identity with the human (gene name C15orf26, gene ID 161502) and mouse (gene name 1700026D08Rik, gene ID 75556) sequences respectively, demonstrating that LIMLE is highly conserved between species. Any specific signal characteristic of a known family protein or specific pattern was revealed with InterProScan portal  [23]. Moreover, sequence alignment algorithms such as ''Basic Local Alignment Search Tool'' (BLAST)  [33]  and PSI-BLAST  [34]  could not detect a homolog of LIMLE in the protein databank. Sometimes, structural information can help to predict biological function of a molecule (3D shape dictates how the protein interacts with a ligand or other proteins). We used the homology modeling web servers ''gene3D''  [24] , the I-TASSER  [27, 28]  and PHYRE for protein structure prediction  [25, 26, 35].
The traces are stored in database, this operation enriches collective past experiences. The traces left by the learner during the learning session evolve dynamically over time; the case-based reasoning must take into account this evolution in an incremental way. In other words, we do not consider each evolution of the traces as a new target, so the use of classical cycle Case Based reasoning in this case is insufficient and inadequate. In order to solve this problem, we propose a dynamic retrieving method based on a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). Through monitoring, comparing and analyzing these traces, the system keeps a constant intelligent watch on the platform, and therefore it detects the difficulties hindering progress, and it avoids possible dropping out.
This system is based, firstly on the traces to feed the system and secondly on the reconciliation between the course of the learner (traces in progress) and past courses (or past traces). The past traces are stored in a database. Our system is able to represent, follow and analyze the evolution of a learning situation through the exploitation and the treatment of the traces left by the learner during his/her learning on the platform. The analysis of the course must be executed continuously and in real time which leads us to choose a Multi-Agents architecture allowing the implementation of a dynamic case-based reasoning. Recently, several research works have been focused on the dynamic case based reasoning in order to push the limits of case based reasoning system static, reactive and responsive to users.
The first step consists of modeling the raw data contained in the log file. It is necessary to be able to collect the traces files containing at least, the following elements: time for the start date of the action, codes action which consists in codifying the learner's actions and learner concerned. In our system IDCBR-MAS a target case or target trace is represented by a trace learner in progress when interacting with the Moodle platform. The cases sources are previous traces learners that are stored in database. The cases sources are traces left by the learners which followed the same training on the Moodle platform. The following figure shows the target trace structure: We developed a module in Moodle platform that can be the interface between the Moodle server and our IDCBR-MAS system.