dataesr/scientific-paragraphs-categorization huggingface dataset:
Filtering and taxonomic assignation of 454 pyrosequencing amplicons A three-step filtering method was adopted to eliminate ambiguous amplicons: 1) denoising was performed with Acacia v1. 52. b0 and AmpliconNoise v1. 29 as described in Materials S1  [37, 38] ; 2) amplicons not containing the exact distal primer sequence were removed; 3) chimeras were eliminated using UCHIME with default parameters after Acacia denoising  [39]. Finally, primer sequences were trimmed off and amplicons were assigned to their closest hit in the Protist Ribosomal Reference database (PR2, version August 13, 2012  [4] ) using ggsearch  [40]. Amplicons corresponding to symbiotic microalgae (e. g. Phaeocystis) or contaminants (e. g. fungi, metazoans or other distant radiolarians) were not included in subsequent analyses.
The filtered amplicons assigned to Acantharia or Nassellaria were aligned with Muscle, implemented in Seaview v. 4. 2. 6  [41] , and clustered at different identity thresholds, from 80% to 99%, using usearch (  [42] ; v. 6. 0. 203_i86linux32). The V4 and V9 alignments of each individual cell did not exhibit ambiguous sections since amplicons are short and highly similar. The secondary structure of the V4 and V9 amplicons was predicted using the RNAfold server available on the Vienna RNA web servers (http://rna. tbi. univie. ac. at). Another analytical approach, called the linkage method, was applied to infer dominant patterns and eliminate random noise  [43]. Details of the methodological procedures are given in Materials S1. The raw V4 and V9 sequences have been deposited in the Short Read Archive under the accession number PRJEB4199.
Note that the acantharian Pec 16-2 and the nassellarian Ses 60 had no valid V4 amplicons after filtering in both denoising programs, probably because of the low initial number of raw amplicons obtained from these samples (1,081 and 1,088, respectively). Some of these raw amplicons were partial (distal primers were missing), and the majority were assigned to fungi, stramenopiles or metazoans that could correspond to preys or contaminants. From a single cell, after denoising with Acacia and merging strictly identical amplicons, the number of unique amplicons was as high as 76 for the V4 and 18 for the V9. On average, 70% of these unique amplicons are singletons (amplicons occurring only once in the dataset). With AmpliconNoise, the number of unique amplicons was much lower (up to 4 V4 and 3 V9 amplicons from a single cell).
7 times more than OTUs with common amplicons (Figure  3 , Table  S2 ). This additional line of evidence demonstrates that many amplicons are artificially produced during the PCR and sequencing steps, and are divergent enough to lead to an overestimation of OTU richness. In addition to the more complex and computationally-intensive denoising algorithms  [37, 38, 44] , sample replication and crossvalidation (selecting amplicons shared by replicates) could be an efficient and biologically meaningful method to differentiate technical artifacts from real biological signal in metabarcoding surveys. The identification of common amplicons between replicates also has the advantage of circumventing the use of arbitrary criteria and thresholds for inclusion/rejection of amplicons (e. g. minimum copy abundance and identity cut-off values), therefore allowing comparison between different environmental metabarcoding datasets.
Considering the continually decreasing cost of sequencing and increasing size of new datasets, sample replication and cross-validation should be considered as an additional denoising step to ensure accurate estimates of environmental microbial diversity (''Replicate or lie'' as claimed in  [45] ), though it remains to be properly tested on complex microbial communities. To better understand the intracellular diversity of V4 and V9 regions, another approach, called the linkage method, was applied to the same single-cell datasets  [43]. By detecting SNP combination patterns in sliding windows along the sequence, this method found that many pyrosequenced amplicons contained numerous random errors (Table  S3 ). These amplicons that had unique patterns with no redundancy in each cell were therefore excluded for subsequent analyses (more details in File S1).
Conclusion and Perspectives Because of their ubiquitous distribution in marine environments and recurrent molecular detection from distinct environmental surveys, the radiolarian taxa Acantharia and Nassellaria will undoubtedly represent a significant part of sequence data in forthcoming environmental metabarcoding studies. The main goal of our study was to assess the intracellular variability of two genetic barcodes in these radiolarians by deep single-cell sequencing, and improve our ability to interpret metabarcoding datasets. Although several amplicon sequences and OTUs were found in a single cell, we assert that intra-individual polymorphism (defined as the divergence and relative abundance of the distinct copies) is limited in Acantharia and Nassellaria as cells contained a dominant ribotype with lowabundant variants.
The ribosomal array seems therefore to evolve in concert despite its multi-copy and heterogeneous nature, and more particularly the presence of multiple nuclei in Acantharia. Based on the combination of alignment visualization, technical replicates, denoising algorithms, secondary structure prediction and a patternbased method, we provided good evidence that many of the minor variants are artificially produced during the PCR and sequencing steps. More particularly, as also highlighted in other studies  [46] , we showed that amplicon sequencing approaches are not reproducible and require replicates for rigorous interpretation. Consequently, we recommend a conservative approach to discard artifacts from metabarcoding datasets: 1) two or three technical replicates (parallel PCR and sequencing steps from the same environmental sample), 2) a denoising procedure including cross-validation of amplicons between replicates, and 3) if working on specific taxon like Acantharia, alignment visualization with reference sequences to further remove ambiguous amplicons.
The remaining amplicons that correspond to the dominant ribotypes of each cell should better reflect the natural diversity and richness of radiolarians in the environment. This conservative approach is critical to properly infer and compare diversity estimates of a particular taxon or a whole community across different samples, and so irrespective of the high-throughput sequencing technique. In addition, low error-rate polymerases, low cycle numbers and ideally PCR-free methods should be favored to alleviate technical biases in future metabarcoding studies. Moreover, our approach allowed improving the 18S rRNA reference database of radiolarians by adding information about the number of ribotypes found in each species. For instance, we detected two ribotypes in the acantharian species Amphilonche elongata (Ei 44 and Ei 45).
Although one ribotype is numerically more abundant than the other, both can be detected in environmental metabarcoding datasets. Similar approach should be adopted on more eukaryotic taxa to fine-tune the assignation of environmental barcodes and avoid inflating diversity estimates in metabarcoding surveys. An obvious next step for radiolarians is to assess the rRNA copy number in single cells in order to estimate the abundance of these protists from metabarcoding datasets. For instance, a recent study using qPCR assays estimated the rRNA copy number per cell in benthic Foraminifera (i. e. 10,000-30,000)  [47]. This allowed the establishment of normalization factors that were used to correctly determine abundance of species by removing ''excess'' of amplicons. Similar normalization has been also applied in bacteria based on the known copy number in reference genomes  [48].
Alternatively, a single-copy gene could be selected as a barcode to assess the abundance of radiolarians in the environment, but the lack of genomic data remains the main barrier to select and validate such barcode among radiolarians. Extending the approach conducted here on radiolarian single cells to other microbial taxa will help to define taxonomically meaningful and relevant genetic entities, but also to contribute to a better understanding of the potential and limitations of the 18S rRNA gene marker for environmental metabarcoding studies. A careful representation of the diversity and relative abundances of microbial organisms is critical for the establishment of biodiversity monitoring projects and the assessment of the impact of anthropogenic changes. Table  S2  Number of common and non-common radiolarian amplicons (without Acacia and AmpliconNoise denoising) between single-celled technical replicates (PCR and sequencing on the same DNA extract).
Acknowledgments We thank the marine stations which hosted us for field sampling: LOV Villefranche-sur-Mer, the  Interuniversity Institute for Marine Sciences in Eilat  ( EU   FP7 ASSEMBLE program  funded this sampling mission), and the  Sesoko Marine Station in Okinawa. We are also grateful to the  Genoscope  for sequencing, and  C. Berney  and  John Dolan  for valuable comments on the manuscript.
This work was supported by the PhD fellowship DIPHOPE from the  Region Bretagne  (J. D. ), the  BioMarKs  project ( 2008-6530 ,  ERA-net Biodiversa, EU ), and a  JST-CNRS exchange program  (F. N). F. M. was supported by the  Deutsche Forschungsgemeinschaft  (grant # DU1319/1-1 ). This study was also supported by the project  OCEANOMICS , that has received funding from the  French government , managed by the  Agence Nationale de la Recherche , under the grant agreement '' Investissement d9Avenir ''  ANR-11-BTBR-0008. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
One of the biggest challenges in hardware and software design is to ensure that a system is error-free. Small errors in reactive embedded systems can have disastrous and costly consequences for a project. Preventing such errors by identifying the most probable cases of erratic system behavior is quite challenging. In this paper, we introduce an automatic test set generator called AUTSEG. Its input is a generic model of the target system, generated using the synchronous approach. Our tool finds the optimal preconditions for restricting the state space of the model. It only works locally on significant subspaces. Our approach exhibits a simpler and efficient quasi-flattening algorithm than existing techniques and a useful compiled form to check security properties and reduce the combinatorial explosion problem of state space.
Thus, we strive for highly-automated testing techniques. We aim as well to describe with symbolic means the reachable state space model of the card specification for a particular security property. Expertise in symbolic verification and synchronous languages are required to automatically generate exhaustive test sets that represent critical situations. We hence focus on a set of techniques known as formal methods, based on the Binary Decision Diagram BDD  [1]  that are used in computer science to ensure correct system behavior. We propose in this paper, an automatic test set generator called AUTSEG. Generating automatic test sets and covering all transitions is not a new research area. However, we notably address in this paper the state space explosion problem. We first generate a powerful quasi-flattening algorithm which performs a simple and deterministic model, thus facilitating code generation.
Related Work Lutess V2  [2]  is a test environment, written in Lustre, for synchronous reactive systems. It automatically generates tests that dynamically feed the program under test from the formal description of the program environment and properties. This version of Lutess deals with numeric inputs and outputs unlike the first version  [3]. Lutess V2 is based on Constraint Logic Programming (CLP) and allows the introduction of hypotheses to the program under test. Due to CLP solvers' capabilities, it is possible to associate occurrence probabilities to any Boolean expression. However, this tool requires the conversion of tested models to the Lustre format, which may cause a few issues in our tests. B. Blanc presents in  [4]  a structural testing tool called GATeL, also based on CLP. GATeL aims to find a sequence that satisfies both the invariant and the test purpose by solving the constraints problem on program variables.
Contrary to Lutess, GATeL interprets the Lustre code and starts from the final state and ends with the first one. This technique relies on human intervention, which is stringently averted in our paper. C. Jard and T. Jeron, present TGV (Test Generation with Verification technology) in  [5] , a powerful tool for test generation from various specifications of reactive systems. It takes as inputs a specification and a test purpose in IOLTS (Input Output Labeled Transition System) format and generates test cases in IOLTS format as well. TGV allows three basic types of operations: 1. It identifies sequences of the specification accepted by a test purpose, based on the synchronous product; 2. It then computes visible actions from abstraction and determinization; 3. Finally, it selects test cases by computation of reachable states from initial states and co-reachable states from accepting states.
Quasi-flattening process The straightforward way to analyze a hierarchical machine is to flatten it (recursively substitute in a hierarchical FSM each super state with its associated FSM), then apply as an example a model-checking tool on the resulting FSM. Let's consider the model shown in Fig. 2 , which shows automata interacting and communicating between each other. Most of them are sequential, hierarchical automata (e. g. automata 1 and 2), while others are parallel automata (e. g. automata 6 and 8). We note in this architecture 13122 (3 × 6 × 3 × 3 × 3 × 3 × 3 × 3) possible states derived from parallel executions (graphs product) while there are many fewer active states at once. Indeed, this model is designed by the graphical formalism SyncChart  [11]. A classical analysis is to transform this hierarchical structure to the synchronous language Esterel  [12].
Smart Card Model The generic model of the studied smart card is designed from a given transport standard called Calypso. This standard defines 33 commands. The succession of these commands (e. g. Open Session, SV Debit, Get Data, Change Pin) presents possible scenarios of the card operation. All commands have been modeled with the Galaxy tool  [16]. Galaxy is an automata editor of finite state machines, able to generate four types of automata: simple (basic automata), parallel automata, hierarchical (light Esterel  [18] ) and syncChart. We chose to use light Esterel (a light version of SyncChart), a synchronous graphical model that integrates high-level concepts of synchronous languages in an expressive graphical formalism. The resulting model presents 52 interconnected automata including 765 states.
Girvan and Newman (2002)  propose to maximize a modularity score based on the observed values of the internal densities of the classes, compared with their expected values in a random model. The choice of the optimal number of classes is done by splitting current classes as long as a modularity gain can be achieved  (Newman, 2006). However, algorithms based on modularity are asymptotically biased and may lead to incorrect community structures as shown by  Bickel and Chen (2009). Handcock et al. (2007)  propose to map the vertices in a continuous latent space and to cluster them according to their positions. A maximum likelihood approach as well as a Bayesian procedure, coupled with a BIC criterion to estimate the number of classes, are implemented in the R package latentnet  (Krivitsky and Handcock, 2009).
The community structure assumption is however not relevant in several types of networks. Transcription factors may for example regulate common operons without regulating each other directly. Other examples like actors or citation networks even exhibit a bipartite structure. Estrada and Rodriguez-Velazquez (2005)  therefore look for disassortative mixing, in which most edges link vertices of different classes. Hofman and Wiggins (2008)  define a mixture model by an intra-group connectivity λ and an inter-group connectivity ǫ, which allows to deal with both assortative and disassortative mixing. Moreover, they develop a variational approximation of the marginal log-likelihood and use it to derive a non asymptotic Bayesian criterion to estimate the number of classes. It is implemented in the software VBMOD.
To get round this difficulty,  Nowicki and Snijders (2001)  use a Bayesian approach based on a Gibbs sampling estimation of the posterior distributions. This method is implemented in the software BLOCKS, available in the package StoCNET  (Boer et al. , 2006). However, no model based criterion is given to determine the number of classes. Daudin et al. (2008)  and  Mariadassou et al. (2010)  propose to tackle that issue in a frequentist framework through an asymptotic approximation of the integrated complete-data log-likelihood. In a Bayesian framework,  Latouche et al. (2009)  introduce a non asymptotic approximation of the marginal log-likelihood as a criterion to estimate the number of classes. All techniques previously cited determine a partition of the vertices into classes. In other words, every vertex is assumed to belong to a unique class.
This property may not correspond to real applications, in which objects often belong to several groups. Proteins can for instance have more than one function  (Jeffery, 1999)  or scientists belong to several scientific communities  (Palla et al. , 2005). It is therefore relevant to develop methods in order to uncover overlapping structures in networks. To our knowledge, the first clustering approach capable of retrieving such overlapping clusters was the algorithm of  Palla et al. (2005)  implemented in the software CFinder  (Palla et al. , 2006). For a given integer k, it computes all the k-cliques (complete subgraphs on k vertices) and all the pairs of adjacent k-cliques (k-cliques sharing k -1 vertices). A community is then defined as the vertex set of k-cliques which can be reached from each other through a sequence of adjacent k-cliques.
In other words, to each vertex i corresponds a {0 -1} vector Z i describing the classes it belongs to, and Z i may contain one, several, or no coordinates equal to 1. The connection probabilities are then determined by using a connectivity matrix like for SBM. The model parameters are estimated in a frequentist framework by using two successive approximations of the log-likelihood. Simulations show a better behaviour of this model for retrieving structures on a fixed number of classes in comparison with CFinder and MMSB. However, it suffers from a lack of criterion to choose the right number of classes. Our main concern in this paper is to derive a criterion to estimate the number of classes in OSBM. To do so, we rely on the Bayesian framework and take advantage of the marginal likelihood p(X), which provides a consistent estimation of the distribution of the data  (Biernacki et al.
Experimental Results We implemented algorithm MAW a programme to compute all minimal absent words of a given sequence. The programme was implemented in the C programming language and developed under GNU/Linux operating system. It takes as input arguments a file in (Multi)FASTA format and the minimal and maximal length of minimal absent words to be outputted; and then produces a file with all minimal absent words of length within this range as output. The implementation is distributed under the GNU General Public License (GPL), and it is available at http://github. com/solonas13/maw, which is set up for maintaining the source code and the man-page documentation. The experiments were conducted on a Desktop PC using one core of Intel Xeon E5540 CPU at 2. 5 GHz and 32GB of main memory under 64-bit GNU/Linux.
We considered the genomes of thirteen bacteria and four case-study eukaryotes (Table  4 ), all obtained from the NCBI database (ftp://ftp. ncbi. nih. gov/genomes/). To test the correctness of our implementation, we compared it against the implementation of Pinho et al. [15] , which we denote here by PFG. In particular, we counted the number of minimal absent words, for lengths  11, 14, 17, and    Table  4 : Species selected for this work with reference to the respective abbreviation and identification of genome sequence data by accession number for bacteria or genome assembly project for eukaryotes in the genomes of the thirteen bacteria listed in Table  4. We considered only the 5 ′ → 3 ′ DNA strand. Table  5  depicts the number of minimal absent words in these sequences. We denote by M 11 , M 14 , M 17 , and M 24 the size of the resulting sets of minimal absent words for lengths 11, 14, 17, and 24 respectively.
MAW also reduces the memory requirements by a factor of 5 compared to PFG. The maximum allocated memory (per task) was 6GB for MAW and 30GB for PFG. To further evaluate the efficiency of our implementation, we compared it against the corresponding performance of PFG using synthetic data. As basic dataset we used chromosome 1 of Hs. We created five instances S 1 , S 2 , S 3 , S 4 , and S 5 of this sequence by randomly choosing 10%, 20%, 30%, 40%, and 50% of the positions, respectively, and randomly replacing the corresponding letters to one of the four letters of the DNA alphabet. We computed all minimal absent Table  7 : Elapsed-time comparison of MAW and PFG for computing all minimal absent words in the genome of Homo Sapiens and Mus musculus words for each instance. We considered both the 5 ′ → 3 ′ and the 3 ′ → 5 ′ DNA strands.
Phalaenopsis species are popular ornamental plants worldwide because of their elegant appearance and extended longevity, and they are of great economic importance for the floral industry. P. equestris is an important breeding parent because of its many colorful flowers in a single inflorescence. It has a karyotype of 2N = 2X = 38 with uniform small-size chromosomes of 1-2. 5 µm in length  5. Its genome size is estimated to be 1. 6 × 10 6 bp per haploid genome, which is relatively small in comparison to the genomes of other species in the same genus  5  or even other genera  6. Some transcriptome sequence data have previously been generated for this species, and a transcriptome database, OrchidBase  7, 8  , has been established. Here we present the first whole-genome sequence and analysis of P.
Methods and any associated references are available in the online version of the paper. Accession codes. Sequencing data, annotations and analyses results have all been uploaded to the FTP site ftp://ftp. genomics. org. cn/from_ BGISZ/20130120/ for evaluation. The data have also been submitted to the NCBI database under BioProject PRJNA192198. Note: Any Supplementary Information and Source Data files are available in the online version of the paper. each singleton, for example, a gene with no homologs, was considered to represent a separate gene family (as shown in Fig. 1a ). Functional annotation. Two methods were used for functional annotation. First, InterProScan 14 (using default settings) was run to map known protein domains to all genes. Using InterPro to GO mapping, GO labels were obtained for the InterPro domains.