dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors would like to thank  CC-IN2P3 ,  Pascal Calvat  and  Jean-Yves Nief  for support with data transfer, data storage, hosting and maintenance of the BioEmergences workflow,  Ingrid Colin ,  Audrey Colin  and  Shuhan Guo  for technical assistance,  Patrick Parra  and  Jean-Yves Tiercelin  for precision mechanics. Thanks to  Pedro Guerra  and  Giorgos Kontaxakis  for their critical comments on the manuscript.
This work was supported by the  Spanish Ministry of Science  under the  FPU fellowship program  to CCG and under projects  TEC2010-21619-C04-03 ,  TEC2011-28972-C02-02 ,  CDTI-CENIT (AMIT)  and  INNPACTO (PRECISION&XIORT) ,  Comunidad de Madrid (ARTEMIS) ,  CEIMoncloaUPM-UCM (PICATA program ),  Agence Nationale de la Recherche (FBI and TEFOR) ,  FP7 EC  funding to NP (zf-health project),  European Regional Development Funds (FEDER) ,  IBiSA ,  CNRS  and  Region I ˆle de France  financial support to the  BioEmergences platform through the DIM ISC-PIF. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In maize, 60% of the 20,000 Helitrons contain captured gene fragments  [21, 22]. Similarly, in rice, 2,809 Pack-Mutator-like elements (Pack-MULEs) containing host gene fragments were identified  [23]. Identification of transposable elements in large and complex genomes is a daunting task, even in high quality assembled genomes. As an example, since 2001, in the rice genome, the number of miniature inverted-repeat TEs (MITEs) discovered has steadily increased over time from 6,641 to 179,415  [24] [25] [26] [27] [28]. A high-quality TE prediction and annotation is essential to prevent mis-annotation of functional genes and to understand the biology of genomes  [29]. Different strategies have been developed, including similarity search against databanks of known TE sequences, de novo repeat detection, k-mer-based counting, and structural motif detection (reviewed in  [30] ).
Early, manually curated TE modeling was performed on selected bacterial artificial chromosome (BAC) sequences during map-based cloning projects or using plasmid or BAC end sequences  [32] [33] [34] [35] [36]. At a larger scale, analyses of 18 Mb of long BAC contig sequences spread along the 3B chromosome  [31]  led to precise delineation of 3,222 TEs. Together with the public Triticeae REPeat sequence database (TREP)  [37] , this provided a representative high quality reference library of wheat TEs. Beyond the identification of TEs, the reconstruction of the nested insertion pattern is a computational challenge that requires fine tuning of dedicated algorithms and knowledge of TE structure and evolution. Recently, a number of genome scale sequences were produced from different wheat genomes.
More than 80% of the TREP families comprise one to two TE copies only versus 57% for ClariTeRep, confirming that the similarity-based classification increases the number of families compared with the clustering-based approach  [44]. In the second step, we automated two things: (i) correction of the over-fragmentation of TE predictions, that is, the fact that a TE is not detected as a single feature but rather split into several neighboring ones; and (ii) the reconstruction of nested TE insertions. To this aim, we developed a program called CLARI-TE, which allows merging neighboring predictions that belong to the same family (see Materials and methods). Then, nested clusters were automatically reconstructed by joining remote predictions belonging to a single element. We estimated the accuracy of the automated annotation by analyzing a manually annotated sequence of approximately 1 Mb (scaffold v443_0137) containing 196 TEs and 47 nested insertions that was manually annotated for this purpose.
We compared the annotation produced by RepeatMasker (using ClariTeRep) with that from CLARI-TE and another annotation pipeline, TEannot (part of the REPET package  [45] ) (Table  1 ). At the nucleotide level, RepeatMasker correctly assigned approximately 90% of nucleotides that belong to TEs, showing that our TE library is comprehensive enough to detect the vast majority of TEs in a wheat genomic sequence. However, the 196 TEs were predicted as 590 separated features with RepeatMasker, illustrating the over-fragmentation problem. The improvement was significant when using both TEannot and CLARI-TE, which detected 345 and 286 features, respectively (Table  1 ). CLARI-TE was more accurate than TEannot in predicting the correct borders of the TEs (sensitivity of 66% versus 45%, respectively) and in limiting the number of false positives (specificity of 52% versus 27%, respectively).
Half of the large intact TEs (>5 kb) were initially predicted as several truncated fragments through similarity search, revealing that for the time being curation is absolutely required to precisely delineate TEs in complex genomes. Moreover, among the 21,165 insertions of LTR-RTs that were used to study the dynamics of insertion/deletion along chromosome 3B, 30% were nested and, thus, would have been missed without automated joining by CLARI-TE. The use of our program prevented biasing the data towards recent TE insertions as usually observed in signature-based approaches  [10, 44]. Finally, with a curated TE library, this approach can be applied to any complex genomes (>50% TEs) with nested TEs. The proportion of complete elements was lower than estimated previously in our pilot study on selected BACs from chromosome 3B  [31] : they represent 22% and 6%  for LTR-RTs and CACTAs, respectively, compared with 59% and 47% in the previous manually curated annotation.
Similarity search and automated curation using CLARI-TE We applied our procedure to the 2,808 scaffolds assembled for the 3B chromosome (HG670306 and CBUC010000001-CBUC010001450), and the T. urartu  [39]  and Ae. tauschii genomes  [40]. Each sequence was investigated for TE content using RepeatMasker (cross_match engine with default parameters)  [72]  with ClariTeRep. We developed CLARI-TE, a perl program  [73, 74] , to correct the raw similarity search results. It performs the three following steps. First, resolution of overlapping predictions. To solve the overlap between two predictions, priority was given to keep the prediction that covers an extremity of a TE. If none or both of the predictions cover a TE extremity, priority was given to keep the longest prediction and recalculate the positions of the other one.
Estimation of LTR-RT insertion date and phylogeny We used the program TRsearch from REPET  [45]  to find the positions of both 5′ and 3′ LTRs from a complete element. We discarded predicted LTRs that did not correspond to the extremity of an element (in a range of 50 bp). Pairs of LTRs were aligned using MUSCLE  [75]  and insertion dates of LTR-RTs were estimated considering a mutation rate of 1. 3 × 10 -8 substitutions/site/year  [76]. Finally, using R, the distribution of insertion dates was plotted for each family with 20 or more copies with an estimated date. For each distribution, a burst peak date was determined and a period of activity was calculated by considering the shortest period of time containing more than 80% of the dated insertions. Phylogeny was established by computing multiple alignments of all intact LTR-RTs belonging to families RLG_famc1 and RLC_famc2 using MAFFT  [70].
Hierarchical clustering of distributions In order to identify CACTA families with similar distributions along the chromosome, we performed a hierarchical clustering of the distributions calculated in a sliding window of 10 Mb (step of 1 Mb) by using the R package 'pvclust'  [79]. We considered only families representing at least 0. 01% in at least one 10 Mb window. The Pearson correlations were calculated between each pair of distribution and a clustering was applied by the agglomerative method 'average' (N = 10,000 bootstrap resampling).
Acknowledgements This project was supported by grants from the  French National Research Agency  ( ANR-09-GENM-025 3BSEQ ), a grant from  France Agrimer , and a grant (project  DL-BLE ) from the  INRA BAP division. JD is funded by a grant from the  French Ministry of Research. NG is funded by a grant from the  European Commission  research training program  Marie-Curie Actions  ( FP7-MC-IIF-NoncollinearGenes ). LP is funded by a grant from the  Region Auvergne.
Therefore, developing predictive tools to describe such networks at the scale of the cell has become a challenging goal. Methods and tools to construct sRNAmediated regulatory networks Two steps are necessary for the construction of a predicted sRNA-mediated regulatory network. First, it is necessary to acquire the two types of nodes corresponding to mRNAs and sRNAs. Both can be obtained by RNA-seq experimental approaches. Without such experimental data at the genome scale, CDS can be obtained from existing annotated genes, whereas putative sRNAs can be extracted from the RFAM database  [23]. Second, it is necessary to predict interactions between sRNAs and their putative mRNA targets, which will be represented as edges between the two types of nodes in the graphs.
Their performance is dependent on the quality of the input alignments, and the main drawbacks include a weaker prediction efficiency and the need for a high-capacity CPU. In this review, we focus on three software programs, RNAup, IntaRNA and ssearch, to illustrate their sensitivity and specificity. The benchmark used is composed of a set of 43 validated sRNAs and 52 mRNAs, with 62 true interactions and 41 noninteractions (see supplementary data) collected from sRNAtarBASE  [41]  (mainly composed of E. coli and Salmonella enterica sRNAs). The results of the predictions are represented graphically with receiver operating characteristic (ROC) curves in Figure  2. Based on these results, it is important to mention that sophisticated methods and naı ¨ve approaches give comparable results for this data set.
The first two software programs exploit sequence information together with structural information, whereas the last one only addresses sequence complementarity. RNAup and IntaRNA, which rely on RNA folding, show a slightly better compromise between sensitivity and specificity. To further our investigation, we used randomly shuffled sequences to evaluate the statistical significance of the RNA composition. We focused on gcvB sRNA, for which (1) more than 10 targets have been experimentally validated and (2) involvement in the pervasive regulation of more that 1% of Salmonella genes was previously suggested  [10]. As shown in Figure  3 , the distribution of the scores given by the three software programs was not higher than that with a real sequence. These observations suggest that use of only the sRNA alphabet with the short length of the interaction regions and the lack of biological information for the interacting rules does not enable discrimination of the results given by the real gcvB sequence.
Functional enrichment analysis In enrichment methods, the underlying idea is to compare groups of genes that are put together according to biological criteria (for instance, groups of mRNAs that share a predicted interaction with one unique sRNA) with a second catalog of gene groups derived from a biological database and sharing a biological annotation  [42, 43]. The two most widely used biological databases are the Gene Ontology (GO) and KEGG databases, which describe the hierarchical relationships between semantic annotations describing the functional activity of proteins and the metabolic pathway assignation of enzymes, respectively. Other databases, such as MetaCyc  [44] , can be used to complete KEGG information because they attempt to provide more exact, true biological pathways, for example, by separately recording the different pathway variants observed in different organisms.
The enrichment strategy provides overrepresented mRNA annotations according to a molecular database of interest. A statistical score is computed according to the presence of enriched annotated terms (e. g. GO or enzymatic activity) in a reference group, called the background (e. g. the total mRNAs of the genome of interest). Following and extending this strategy, more than 68 software programs have been proposed and reviewed  [42]. The choice of one of these software programs is not an easy task, and some of the following ranking key points have to be prioritized. First, the use of standard formats for the mRNA identification is crucial. Moreover, typically, only a few gene model organisms are indexed, regardless of the biological database that is used. A second key point to consider is the list of available databases, as each database is specialized in different biological features.
The GO database is the most widely used and partitions genes into three main groups corresponding to their associated biological processes, cellular localization and molecular functions. It is nevertheless meaningful to note that the GO hierarchy is based on DAGs (Directed Acyclic graph, a graph where a child annotation can be linked to several parent annotations, and vice versa). In consequence, one gene can contain several annotations on multiple levels of precision. This redundant information has been described in several works  [45, 46]  to increase the difficulty of interpreting results (for instance, relying on various genes with different levels of precision in their annotation, according to the GO database). Recently, Dutkowski et al. [47]  investigated this problem and proposed a new level of compact ontologies to filter out the GO node annotation.
This platform, which also provides a web server and web services for a huge list of bacteria, integrates a large list of databases, ranging from generalized databases, such as Uniprot, to specific databases that exploit protein domain information or sequence conservation. In addition, the web server David-WS enables the simultaneous exploitation of the enriched semantic terms given by all databases. After the computation of an enrichment score for each database, an additional clustering step is performed to group genes that share similar annotations, regardless of the database. This clustering step is helpful when dealing with the redundancy problem created by the GO annotation because it adds supplementary information to the biological knowledge.
Information visualization produces visual representation of input data to help the expert to build hypotheses and test them through bioinformatics algorithms. To increase the user's level of confidence, bioinformatics algorithms can either reduce the scope of the study or integrate additional biological knowledge. Such an analytical loop produces new data that can potentially be used to motivate experimental studies. During the past decade, the community has produced a large number of software pieces for biological network analysis and visualization (for a general review see  [53, 54] ). Two categories of visualization tools can be distinguished: general network visualization tools and network-dedicated tools. Among the general tools, one of the most famous is Cytoscape  [55] , and others include Tulip  [56] , Pajek  [57]  and Gephi  [58].
These tools support many features and are generally suitable options for the visualization, analysis and exploration of large networks. Additionally, some of these tools integrate a plug-in management system or a script interpreter  [56, 58, 59] , allowing experts to increase the number of supported features (for instance, by adding a dedicated clustering algorithm). Although these tools have proven their efficiency in general cases, because of their generic nature, they may not fulfill the requirements of all users, such as in cases of welldefined and network-specific tasks. This leads to the second category of visualization tools, which are dedicated to particular types of networks. For instance, some of these tools are designed for the analysis of protein-protein interaction networks  [60] , metabolic networks  [61, 62] , signaling networks  [63, 64] , gene regulatory networks  [65]  and sRNA-mediated regulatory networks  [66]  (for detailed reviews, the reader can refer to  [67] ).
Another interesting method is based on the computation of graph metrics  [56, 57, 61] , which has been described in several reviews  [70, 71]. In general, when analyzing biological networks, inferring topological metrics helps in generating hypotheses on the robustness and/ or dynamics of the network  [72, 73]. Finally, use of expert knowledge is of utmost importance to reduce the scope of the analysis by focusing on subparts according to biological information. The integration of biological databases is currently one of the main challenges in the data mining community, and many studies have focused on enrichment analyses for driving the integration of multipurpose 'omics' or annotated data. Although such database integration enables the user to perform more realistic analysis, few tools integrate visualization approaches  [74] , and if so, they are dedicated to specific biological networks  [75, 76 ].
An illustration with E. COLI The input of the visualization environment is a list of sRNAs and mRNAs extracted from databases and complete genome annotations. When building the network, the two types of genes are automatically assigned to nodes in a bipartite graph where different colors and shapes can distinguish between them, and two nodes are linked by an edge if an interaction between the corresponding RNAs has been predicted. The resulting biological networks can have a prohibitive size due to the large amount of predicted interactions, which hinders their exploration and analysis. Additional information may enrich that first network in an integrated way. For example, the enriched functional annotations of mRNAs allow highlighting a group of genes that belong to the same functional class.
To do so, we used rNAV  [66] , a visualization software program that encapsulates the process of building, enriching and exploring sRNAmediated networks. Figure  5  shows the analysis process we followed in this study. One of the key advantages of the visualization strategy is to support an iterative process where the network is successively pruned until a given hypothesis is confirmed. The first step is to build an E. coli network using the 15 sRNAs involved in biofilm regulation using INTARNA. We obtained a network composed of 2913 nodes as 5 0 UTR mRNAs and 6705 edges modeling the predictive interactions. Such a prohibitive number of putative interactions make the interactive exploration of the data extremely difficult. The second step of the analysis was to perform a functional enrichment.
Hence, the known interactions are of particular interest for identifying further conserved features regarding the experimentally validated pairs of sRNA-mRNA (e. g. the conservation of the position within one sRNA targeting several mRNAs, the RBS interaction position onto the 5 0 UTR of mRNAs or the presence of an enriched annotation term extracted from the biological databases). We thus extracted the subnetwork corresponding to these validated interactions (see Figure  6 ). By visually comparing the predicted interactions in these four subnetworks of sRNA of interest (Figure  6  shows the predicted interactions of RybB and GcvB) and taking into account the conservation of interacting sRNA regions, we reduced the number of putative interactions. Following such an analysis process, we highlighted the experimentally validated interactions (7 for RybB and 12 for GcvB) and suggested new interaction candidates (30 for RybB and 28 for GcvB), which could all be potential areas for future investigation.
Discussion and conclusion Because of the lack of biological information regarding these RNA-RNA interaction rules, prediction tools often yield a prohibitive number of candidates. To circumvent this problem, filtering of the predictions to focus on the most promising ones is crucial. The strategy relies on providing a posteriori multipurpose information, according to the heterogeneous data extracted from databases (using enrichment methods) and the visual exploration/analysis of the biological network (using visualization approaches). We reviewed the different methods in both domains in the context of biological networks. To address the large amounts of data generated in network prediction, the integration of additional biological information is often used to filter the results. To do so, enrichment methods allow the exploitation of 'omics' data (derived from multipurpose methods) to identify statistically significant subgroups of sRNA targets sharing an annotation feature.
Key Points • Because of the lack of biological information regarding these RNA-RNA interaction rules, prediction tools often yield a prohibitive number of candidates. • To address the large amounts of data generated in regulatory network prediction, the integration of additional biological information is often used to filter the results. • One strategy relies on providing a posteriori multipurpose information, according to the heterogeneous data extracted from databases (using enrichment methods) and the visual exploration/analysis of the biological network (using visualization approaches).
Spectrometers Recently, amateur astronomers have starting to use spectrometers. This has become possible for three reasons: i) the availability of low readout noise CCD cameras at a reasonable price is a fundamental point, since the light of the spectrum is dispersed; ii) commercial spectrometers for astronomy are now available for amateur astronomers; iii) and the number of experiments published with professionals has increased, indicating that the methods used by amateurs was adequate for professional standards. For example, a PRO-AM collaboration in spectroscopy started in stellar physics in the end of the 20th century  [33]. In another example, the Be star database of the Observatoire de Meudon 4  is fed by hundreds of spectra per year provided by amateur astronomers. The resolving power is defined by R = λ /∆ λ = c/∆ v, where λ is the observed wavelength and ∆ λ is the resolved element corresponding to the spectral sharpness delivered by the instrument, c is the speed of light, and ∆ v is the resolved element expressed in velocity (km/s).
How can amateurs contribute to the data? Visual observations have to be sent to the International Meteor Organization in order to be of use for the scientific community. An online form is available in several languages  15. An automated preliminary data analysis allows one to directly follow the evolution of a meteor shower with time. In the case of a major event, a full analysis follows such preliminary reduction. Video data can be automatically shared thanks to online databases such as the IMO VMDB (Visual Meteor Datebase) or the French BOAM (Base des Observateurs Amateurs de Météore)  16. Care must be taken when setting up such databases: in order to be useful for the scientific community, recommendations regarding the data to be saved were described in  [116]. The goal is to have enough elements in order to judge the quality of an observation, and to draw conclusions based on complete confidence in the data set used.
Data-mining of asteroids There are several international initatives for data-mining of asteroids in archives that were initially devoted to scientific programs oriented to cosmology, star structure and evolution. Two of these initiatives are cited here as representative of collaborations between professional and amateurs astronomers: Euronear  26  and the Spanish Virtual Observatory initiative for NEAs  27. Astronomical databases produced by professional observatories can be accessed via Internet for Solar System objects searches. Precoveries and recoveries of MB asteroids and NEAs are activities adapted to data-mining. Serendipitous encounters of asteroids in the archives can be retrieved by comparing their ephemeris with the epoch when the images were obtained  [201]. The presence of objects in an archive is a function of their limiting magnitude and for this reason systematic inspection of candidate images must be done  [200].