dataesr/scientific-paragraphs-categorization huggingface dataset:
IMPLEMENTATION The GATB library is composed of five main packages: system, tools, bank, kmer and de-Bruijn packages. The system package holds all the operations related to the operating system (OS): file management, memory management and thread management. Using such an abstraction allows client code to be independent from the OS, thus suppressing compilation directive inside the code or improving some OS accesses by hiding specific OS optimization. The supported operating systems are Linux, Mac and Windows. The tools package offers generic operations used throughout the user application but not specific to genomics area. For example, this package includes design pattern tools (such as iterators, observers, smart pointers, etc. ) and object collections (such as containers, bags, iterables, etc.
). It also optimizes the way GATB data structures are saved. The HDF5 file format is currently used (HDF5, 2012). This powerful technology is extremely well suited for large and complex data collection such as those handled in the GATB library. The bank package provides operations related to standard genomic sequence dataset management. All the main sequence file formats are supported, and high-level interfaces allow sequences to be easily iterated regardless of the input format. In other words, algorithms are written independently of the input formats. The kmer package is dedicated to fine-grained manipulation of k-mers. Optimized routines are provided to perform k-mer counting from large sequence datasets, to find k-mer neighborhood or to select k-mers based on different criteria. Finally, the de-Bruijn package provides high-level functions to manipulate a static de-Bruijn graph data structure: creation from a set of k-mers, iteration through different nature of nodes (simple k-mers, branching k-mers, etc.
), extraction of neighbor nodes, etc. Additional information (e. g. k-mer coverage, markers of visited nodes) is stored in the graph branching nodes. From this abstraction level, developing new tools based on de-Buijn graphs is fast, and does not require programmers to delve into low-level details. The GATB library takes benefit of the parallel nature of today's multicore architecture of microprocessors. When possible, time-consuming parts of the code are multithreaded to provide fast runtime execution. The GATB library is developed in C+ + under the A-GPL license and is available from the following Web site: http://gatb. inria. fr. An extensive documentation with tutorials is available to guide designers in the process of developing new NGS tools from the GATB building blocks: http://gatb-core.
RESULTS To demonstrate the efficiency of the GATB library, a few software implemented from GATB are briefly presented. The idea is to give a quick overview of the application spectrum of the GATB library and some performance numbers. Minia  (Chikhi and Rish, 2012 ) is a short-read de-Bruijn assembler capable of assembling large and complex genomes into contigs on a desktop computer. The assembler produces contigs of similar length and accuracy to other de-Bruijn assemblers-e. g. Velvet  (Zerbino and Birney, 2008). As an example, a Boa constrictor constrictor (1. 6 Gb) dataset (Illumina 2 Â 120 bp reads, 125 Â coverage) from Assemblathon 2  (Bradnam et al. , 2013)  can be processed in $45 h and 3 GB of memory on a standard computer (3. 4 GHz 8-core processor) using a single core, yielding a contig N50 of 3.
6 kb (prior to scaffolding and gap-filling). Bloocoo is a k-mer spectrum-based read error corrector, designed to correct large datasets with low memory footprints. It uses the disk streaming k-mer counting algorithm contained in the GATB library and inserts solid k-mers in a Bloom filter. The correction procedure is similar to the Musket multistage approach  (Liu et al. , 2013). Bloocoo yields similar results while requiring far less memory: for example, it can correct whole human genome re-sequencing reads at 70Â coverage with 54 GB of memory (see Supplementary file 1 for extra information on Bloocoo). DiscoSNP aims to discover Single Nucleotide Polymorphism from non-assembled reads and without a reference genome. From one or several datasets a global de-Bruijn graph is constructed, then scanned to locate specific SNP graph patterns  (Uricaru et al.
, 2014). A coverage analysis on these particular locations can finally be performed to validate and assign scores to detected biological elements. Applied on a mouse dataset (2. 88 Gb, 100 bp Illumina reads), DiscoSnp takes 34 h and requires 4. 5 GB RAM. In the same spirit, the TakeABreak software discovers inversion variants from non-assembled reads. It directly finds particular patterns in the de-Bruijn graph and provides execution performances similar to DiscoSNP  (Lemaitre et al. , 2014).
The definition of pedophilia we use in this article thus encompasses both the medical definition of pedophilia (sexual interest in prepubescent children) and hebephilia (sexual interest in pubescent children not sexually mature). We discuss related work in Section 2, to give an overview of the state-of-the-art on online pedophile activity detection and analysis. Section 3 presents a short introduction to P2P systems, before our description of our datasets and how we collected them (Section 4). We then present the details of our comparison of the amount of pedophile queries in KAD and eDonkey in Section 5. Section 6 focuses on an important feature of pedophile activity: ages entered in queries. Finally, in Section 7, we introduce a methodology to estimate the fraction of pedophile queries in KAD from the one in eDonkey.
Their study focused on "onset", the first deliberate viewing of child pornography. They gathered the Top 300 queries submitted to the popular Isohunt tracker (part of the BitTorrent network) and published on the website Isohunt. com. Their study lasts for 3 months, a scale similar to ours, but they resort to manual classification of the queries. Their dataset is particular, as it only gives a relative popularity order for some queries, and may not provide any indication on the extent of child pornography in the network. Plus, with only 300 queries collected daily, they get very few pedophile queries (only 3), which leads to results with a limited statistical significance. However, their discussion is truly interesting, including comments on whether "regular pedophile users" are likely to submit several times the same query (to "build a collection"), while first-time users may not (they do not progress to downloading material once they have discovered the meaning of intriguing pedophile sequences such as pthc).
This bias may lead query-based studies like ours to slightly overestimating the demand for child pornography, and would impact estimations on the number of pedophile users, but additional filtering based on the IP-address or the client ID could limit this issue. In  [4] , the authors developed and assessed a dedicated tool for search engine query classification, and collected large-scale datasets on eDonkey (up to 28 weeks of uninterrupted experiment). We use here their tool and one of their datasets. Part of their work was later reused by another team to study another P2P network, BitTorrent  [13]. The European Commission has set up a "Safer Internet" program  [1] , which funded some large research projects such as MAPAP  [14]  and iCOP  [15]. In parallel, authors of  [16, 17]  provided an extensive study (one-year long) on child pornography on Gnutella and eMule, partnering with law enforcement to develop software platforms and collect data on child pornography trafficking.
Experimental Setup and Datasets In order to compare pedophile activity in two different P2P systems, we first need appropriate datasets, the collection of which is a challenge in itself. In KAD and eDonkey, different kinds of measurements are possible, depending on the details of the network's architecture. In eDonkey, servers index files and providers for these files, and users submit keyword-based queries to servers to seek files of interest to them  [19]. By monitoring such a server, one may collect all those queries  [20]. Here, we record all queries received by two of the largest eDonkey servers during a three-month period in 2010. The servers are located in different countries (France and Ukraine) and have different filtering policies: the French server indexes only non-copyrighted material, while the Ukrainian server openly indexes all submitted files.
The sets of keywords were established using the work on pedophile query detection presented in  [4]. Notice that our set of keywords contains mainly common English words (love, early, flowers), but some are in other languages (saison, pedofilia), and some are also brand names (pokemon, nokia). Because of the differences in architectures of the two networks and of the measurement methodologies, we obtained very different datasets, which are not directly comparable: in eDonkey, we observe all queries from a subset of users whereas in KAD we only observe queries related to a given keyword, but from all users. In addition, based on various versions of KAD clients, the measurement tool only records the queries containing a monitored keyword placed in first position or being the longest in the query.
As a consequence, with a short keyword such as avi, a name extension for video files, we almost only record queries in which it is the unique keyword, because otherwise it most likely is neither the longest nor the first word in any query. In order to obtain comparable datasets, we therefore limit our study to a subset of our datasets: the queries composed of exactly one word among the 72 keywords we monitor. As a result of this construction process, we obtain three datasets, which we call eDonkeyFR, eDonkeyUA and KAD. They contain 241,152, 166,154 and 250,000 queries respectively, all consisting of a unique keyword from our list of 72 monitored keywords, which ensures that they are comparable. The server corresponding to the eDonkeyFR dataset is located in France, while the one corresponding to eDonkeyUA is in Ukraine.
Amount of Pedophile Queries in eDonkey versus KAD The most straightforward way to compare the pedophile activity in different systems certainly is to compare the fraction of pedophile queries in each system. Figure  1  presents the fraction of queries for each category of keywords. This plot clearly shows that there are very distinct search behaviors in the two networks, since values obtained for the paedophile and not paedophile categories significantly differ between KAD and the two eDonkey datasets. More surprisingly, the fraction of pedophile queries is significantly lower in KAD than in eDonkey which is in sharp contradiction with previous intuition, as KAD is assumed to provide a higher level of anonymity. The plot also shows that values obtained for the two eDonkey servers are similar, which indicates that very different filtering policies have no significant influence on the amount of pedophile queries.
In order to gain a more detailed insight on this phenomenon, we study the frequencies of each keyword separately in the three datasets. As we want to explore possible correlations between the pedophile nature of a keyword and its frequency, we need a way to quantify the pedophile nature of a keyword. To do so, we use the 28-week dataset and the pedophile query detection tool from  [4] , which divides a dataset between paedophile and not paedophile queries (with a precision above 98% and a recall above 75%). We denote by Q the whole dataset of queries, and by Q(k) the set of queries containing a given keyword k. For each keyword k, we obtain Q(k) = N (k) + P (k), where N (k) and P (k) are the subset of queries containing keyword k and tagged as not paedophile or paedophile, respectively. We then define the paedophile coefficient π(k) of keyword k as: π(k) = |P (k)| |Q(k)|.
If all the queries with keyword k are pedophile queries, π(k) = 1, and if none of them are, π(k) = 0. All keywords in the not paedophile category have a paedophile coefficient below 0. 006. For keywords in the mixed category, the paedophile coefficient is above 0. 01 and below 0. 4. All paedophile keywords have a paedophile coefficient above 0. 885. Finally, we plot in Figure  2  the ratios f eDonkeyF R (k) f kad (k) and f eDonkeyU A (k) f kad (k) , where f s (k) denotes the frequency of queries composed of keyword k in the dataset s, for each of our 72 keywords. We rank keywords on the horizontal axis in increasing order of paedophile coefficient. The horizontal line represents y = 1, which enables a visual comparison of the values: if the point is below the line, then the keyword is more frequent in KAD, otherwise it is more frequent in the eDonkey dataset.
This plot gives a clear evidence for a correlation between the pedophile nature of a keyword and its higher presence in eDonkey than in KAD. In addition, the frequencies in both eDonkey datasets are very similar for the vast majority of keywords. We therefore conclude that anonymity is not the prevailing factor when pedophile users choose a network, since neither the decentralized architecture of KAD nor the different filtering policies increase the frequency of pedophile queries. Instead, the frequency of pedophile queries is even higher in eDonkey than in KAD. Finding an explanation for this unexpected phenomenon is still an open question. The higher technical skills required to use KAD may be part of the explanation. Users may also search content on eDonkey while protecting their privacy with other tools, such as Virtual Private Networks or TOR  [22].
Ages Indicators in Queries A way to gain more insight on observed pedophile activity is to study the distribution of age indicators in queries  [11]. Notice that age indicators are sometimes used in other contexts than pedophile activity, especially when parents seek content suitable for children of a certain age. However, one can observe on Figure  2  that ages indicators have similar behavior to those obtained for the pedophile group, and are therefore closely related to the topic. We plot the distribution of age indicators on Figure  3 : for each integer n lower than 17, we plot the number of queries of the form nyo in each dataset (yo stands for years old). The three plots have similar shape, with mostly increasing values from 1 to 10, a little drop at 11, a peak at 12 and a fall from 13 to 16.
These values for KAD are below the values for the eDonkey servers, which is due to the fact that this dataset is a bit smaller than others and that pedophile queries are rarer in it. The key point here is that the distributions are very similar in all three datasets. This indicates that, although the amount of pedophile activity varies between systems, its nature is similar, at least regarding ages. In  [4] , the authors establish a method to quantify the fraction of pedophile queries in eDonkey. It relies on a tool able to accurately tag queries as pedophile or not, and on an estimate of the error rate of this tool. Such an approach cannot directly be applied to KAD though, as only a small (and biased) fraction of all queries may be observed in this system. We however show in this section how to derive the fraction of pedophile queries in KAD from the one in eDonkey.
In a given system, eDonkey or KAD here, we consider different sets of queries and we denote by Q the set of all queries, P the subset of pedophile queries in Q, Q the subset of queries composed of one word among the 72 monitored keywords, P the subset of pedophile queries with one word, i. e. consisting of one of the 19 monitored pedophile keywords (and so: P = Q ∩ P ). Figure  4  illustrates our notations. In both our eDonkey measurements, |P | and |Q| may be directly estimated, as shown in  [4] , and one can then obtain the fraction |P | |Q| of pedophile queries in the dataset. We give the results for our two measurements in Table  1. On the contrary, in KAD, one may only estimate |P | and |Q|. , which capture the probability for a non pedophile query, respectively pedophile, to make a query of one word among one of our monitored keywords.
Given the definition of α and β, there is no a priori reason to assume that they have significantly different values between eDonkey and KAD. From the definitions of α and β, we have: α = |Q| -|P | |Q| -|P | =⇒ |Q| = α|P | + |Q| -|P | α β = |P | |P | =⇒ |P | = |P | β Then, the following expression holds: |P | |Q| = |P | β × α α|P | + |Q| -|P | = α|P | β|Q| + (α -β)|P | (1) We now use expression (1) to infer the fraction of paedophile queries that were submitted in the KAD P2P network during our experiment. Using the values from Table  1  and the average values of α and β between our eDonkey datasets, we obtain: |P | |Q| ≈ 0. 087% ± 0. 008 This value is of similar magnitude to the one of eDonkey (approx. 0. 25%) but close to three times lower. This estimation of |P | |Q| relies on the value of α.
Conclusions We performed a comparative study of two large-scale peer-to-peer networks, KAD and eDonkey, with regards to the queries related to child pornography. We designed a methodology to collect and process datasets allowing to compare them in a relevant manner. We obtained the counter-intuitive result that pedophile keywords are significantly more present in eDonkey than in KAD, despite the higher anonymity level it provides. On the contrary, our study of age indicators in queries showed that the nature of pedophile queries is similar in these systems. We finally established the first estimate of the fraction of pedophile queries in KAD. We obtained a value close to 0. 09%, which is of the same magnitude but significantly lower than in eDonkey (0. 25%). Our approach here is similar to the one used in  [4] : we focus on search queries, which help to grasp the demand for pedophile material.
Identification of evolutionarily conserved and novel genes as developmental regulators was confirmed by demonstrating characteristic expression profiles for a sub-set of genes, and by gene knockdown studies. Conserved genes coded for members of many known signaling pathway and transcription factor families, as well as previously unstudied proteins. Nearly 30% of the identified genes were restricted to cnidarians (the jellyfish-sea anemone-coral group), supporting the idea that the appearance of new genes during evolution contributed significantly to generating animal diversity. analysis (DGE) was performed using an Illumina HiSeq sequencing platform. The number of mapped reads onto a reference transcriptome data set was taken as a measure of transcript level, and the statistical significance of differences in these levels between samples assessed using the DEGseq package (Figure  1 ; see Materials and Methods for technical details).
3) As yet uncharacterized genes that have homologs in bilaterians. This third category includes proteins containing conserved domains identified in the PFAM database such as the Domains of Unknown Function DUF4323 and DUF3504. Cnidarian restricted genes. A considerable proportion of the sequences with complete predicted ORFs (37/126 = 29%) did not have identifiable orthologs among known bilaterian genes or any other non-cnidarian sequences in the NCBI databases (see Methods for details), as defined by a lack of significant similarity by reciprocal BLAST along the length of the sequence. These 'cnidarian restricted' sequences included the transcript most strongly under-expressed (highest z-score) in Wnt3-MO embryos, WegO1. In some cases, despite the absence of any identifiable ortholog, recognizable conserved motifs such as SAM or PH domains could be detected within the sequence of these transcripts using domain prediction software, suggesting involvement in mediating protein-protein or protein-membrane interactions.
Names were assigned to the analyzed transcripts on the basis of orthology and/or membership of known gene families (all phylogenetic analyses in File S2). Multiple members of known gene families were distinguished by suffixes designating the 4 main expression profile types: O, A, IE or D. Cnidarian-specific transcripts lacking any recognizable orthologs from non-cnidarian species in NCBI databases, and those with non-cnidarian orthologs that had not previously been characterized, were assigned novel names using the same suffixes, prefixed by ''Weg'' to denote differential expression in Wnt3-MO early gastrulae, or given names based on recognizable repeats when present. Wnt3-MO embryo under-expressed transcripts show oral and endodermal profiles. Consistent with the aboralized phenotype of the Wnt3-MO-embryos, the twenty top underexpressed transcripts were all strongly localized during normal development to cells at the future oral pole (site of gastrulation) at the early gastrula stage (Figure  2 ).
DGE analysis For each experimental condition, total RNA was extracted from batches of 900-1400 early gastrula stage embryos using RNAqueous kit (Life Technologies/Ambion, CA). RNA integrity was confirmed by formaldehyde gel electrophoresis. Two independent biological replicates were performed for the uninjected and Wnt3-MO conditions, and single samples for the other morpholino conditions. Estimated final embryo numbers in each sample, after removal of any showing arrested cleavage or irregular development, were as follows: Uninjected: each 1900; Wnt3-MO: each 2300, Fz1-MO: 900, Fz3-MO: 1600 and Stbm: 1400. Library construction and Illumina short-read (51 bp) sequencing was performed by GATC (Konstanz, Germany). To quantify gene expression, the number of mapped reads onto a reference transcriptome data set was taken as a measure of transcript level.
The reference transcriptome, comprising 24893 distinct (non-overlapping) assembled sequences, was built by combining, using CAP3 software, previous EST data  [15, 32]  and Illumina sequences from one of the untreated early gastrula samples generated in this study. Redundant sequence entries were eliminated by USEARCH (ver. 5. 2. 32_i86linux32). The longest predicted ORF from each sequence was used as the reference for read mapping. To reduce polymorphism, adaptator sequences and probable 59 UTR sequences upstream of the first ATG in each cDNA contig were removed, For each experimental condition approximately 80 million of 51pb Illumina reads were mapped on the reference transcriptome using the Bowtie command, with tolerance of two mismatches. Reads that matched to more than one reference sequence were not taken into account.
Around 35% of the reads obtained for each condition could be mapped using this method. Statistical analysis was performed using the DEGseq R package  [50]  to determine for each transcript whether the observed ratio of transcript levels (M) between two samples is significant given the global average expression (A). The Random Sampling Model employed assumes a normal distribution for log 2 (C), where C is the number of counts, as confirmed for our data by a Q-Q plot (Figure  1B ). M = log 2 (C sample1)-log 2 (C sample2) estimates the difference of expression between the conditions; A = (log 2 (C sample1)+log 2 (C sample2))/2 measures the average expression in the two conditions. A p-value was generated for each gene to determine whether the expression difference between samples was significant.
Gene sequence analysis A R-script was devised to analyze automatically the six possible reading frames of each unique assembled transcript sequence and to predict the best ORF (''find. ORF'' script downloadable at http://octopus. obs-vlfr. fr/R_scripts). Sequence comparisons were performed with both BLASTx with the whole sequence and BLASTp with predicted translated ORF against the ''nonredundant''' (nr) NCBI database. Domain analyses (Files S1 and S3) were performed using Interproscan, SignalP for the detection of secreted peptide signals and TMHMM for the prediction of transmembrane domains. Gene identities (column 3 of File S1) were based on BLAST and domain analyses. Gene accession numbers are provided in File S1 and File S3.