dataesr/scientific-paragraphs-categorization huggingface dataset:
coli; thus, the numberOfPGDBInSpecies for E. coli is equal to 667. The fifth key is the number of pathways in a PGDB (numberOfpathway). The sixth key is the pathwayFrameID key that is the official unique MetaCyc identifier of each pathway. The seventh and eighth keys are PS and the pathway frequency score (PFS), as described in Figure  2. The last key is the ReasonToKeep key that explains why we decided to keep the pathway in the database. The reasons are either that the pathway is from MicroCyc, that the pathway has a high enough threshold quality score, or that the metabolic pathway is complete. In the hierarchy table, the pathwayHierarchy corresponds to the pathway functional hierarchy found in MetaCyc. For example, for the nitrogen fixation I (pathwayName) pathway, its hierarchy is Degradation/Utilization/Assimilation-Inorganic Nutrient Metabolism-Nitrogen Compound Metabolism-Nitrogen fixation, and its pathwayFrameID key is N2FIX-PWY.
Management of the MACADAM database update How: The MACADAM database is built from a pipeline of python scripts. The update process takes around 2 days, depending on the parallelization capacities. In terms of dependencies, MACADAM requires Python 3, the Pandas package and a valid license of Pathway Tools that have to be installed. All other dependencies are included by default in the python 3 setup. MACADAM can be updated at each RefSeq release (i. e. addition of new high-quality annotated genomes). MACADAM automatically downloads the new index summary from RefSeq. MACADAM then builds script downloads and processes all the genomes that matched our quality standards and launches Pathway Tools on each one of them. This is the crucial part in terms of computing power. In order to save time, the process is parallelized on the cluster of the GenoToul Bioinformatics Platform (http://bioinfo.
genotoul. fr/) that provides access to high-performance computing resources. To do this, we process genomes by batches of 50 genomes and the whole step takes ∼1 day. This step needs at least 8 GB of RAM for each batch. The generation of the unique ID and the calculation of the PS take around 6 h each due to file movements and extraction of archives. MACADAM can take up to 600 GB of disk space during the construction of the PGDBs, which is the most critical part. After compression, this file takes up 150 GB of disk space. Who: MACADAM database benefits from the GenoToul Bioinformatics Platform facilities, including a permanent staff and technical support. The database will be available for downloading and querying as long as it demonstrates its utility for the research community. Moreover, the python script pipeline is available on the GitHub repository (GitHub URL: https://github.
Querying the MACADAM database Input query MACADAM provides users with metabolic pathways and functional annotations about taxonomic names. Thus, the user has to query MACADAM with one or several species and/or one or several taxonomic names (if several, they have to be separated by a comma). For more precise queries on taxonomic names, the taxonomic rank can be specified thanks to a drop-down menu (phylum, order, class, family, genus or species). Otherwise, the user has to query on 'all ranks'. Thus, if the user specifies 'coli' and 'genus', E. coli will not be a match. One specificity of MACADAM is that if the queried organism/taxonomic name has no link to functional information, the user still obtains functional information about the upper rank of the target. To limit this, the 'Strict taxonomy' option allows the user to query only on target organism/taxonomic names and not beyond the specified taxonomic ranks.
The search can also be refined by specifying (i) the full name (or part of the name) of a metabolic pathway or a functional feature (e. g. a query with 'nitrate' limits output information to 11 MetaCyc pathway names, 4 functional annotations from FAPROTAX and 1 functional annotation from the IJSEM phenotypic database), (ii) a MetaCyc class hierarchy ID (e. g. a query with 'denitrification' limits the output to 3 MetaCyc metabolic pathways and 4 FAPROTAX functional annotations), (iii) a specific PS (min score = 1 means that MACADAM returns only complete metabolic pathways), (iv) metabolite names (n = 7921), (v) reaction names (n = 592), (vi) enzyme names (n = 7440) and/or (vi) EC numbers (n = 2134). All search fields can be filled with complete or incomplete strings.
Web interface: MACADAM explore We have created a web interface called MACADAM Explore (Figure  5 ) to facilitate the consultation of the MACADAM database. MACADAM Explore is built on an Apache Hypertext Transfer Protocol (HTTP) server. It provides commands to search and retrieve the data in the database. Python CGI is used for the front-end web interface. All common gateway interfaces and database interfacing scripts are written in the Python programming language. The database file (SQLite format) with a query script is downloadable at http://macadam. toulouse. inra. fr.
Utility and discussion MACADAM is designed to collect bacterial and archaeal genomes of the highest quality associated with the highest quality annotations Reliable data are needed to infer the functional potential of complex prokaryotic communities. Indeed, obsolete functional information can lead to inaccurate insights  (36). As far as possible, MACADAM avoids sequentially spurious annotations. In the MACADAM database, reliability is ensured by filters on genome quality, meaning that only complete genomes have been taken from RefSeq. Moreover, to ensure up-to-date annotations, we compute PGDBs with Pathway Tools at each release, based on RefSeq, NCBI taxonomy, MetaCyc, MicroCyc, FAPROTAX and the IJSEM phenotypic database. MACADAM PGDBs cover all phyla recognized by the List of Prokaryotic names with Standing in Nomenclature (LPSN; http://www.
bacterio. net/) and the 10 other newly proposed phyla from the NCBI taxonomy (Figure  7 ). Proteobacteria is the most prevalent phylum and accounts for >55% of the genomes collected in MACADAM, followed by the Firmicutes and Actinobacteria phyla that account for >22% and 11% of the collected genomes, respectively. This pre-eminence is probably explained by the research effort devoted to these phyla by biologists. Accordingly, Escherichia and Salmonella are the prevalent genera in the database (5. 1% and 4. 3% of the database, respectively). Interestingly,  according to Figure  7 , the Acidobacteria phylum is weakly represented in MACADAM (only 21 PGDBs, 19 of which are from MicroCyc). But these PGDBs include the highest number of metabolic pathways (mean = 230; min = 138; max = 298; Figure  8 ).
4. 3. 2 reaction is a key reaction in this pathway. Therefore, if the organism contains this reaction, the whole pathway will be identified for the organism. These PS and PFS data are useful to biologists for data mining. An important feature of MACADAM is its ability to infer functional annotations for taxa with no associated genomic sequences using the taxonomy. In the case of a taxon with no functional information in MACADAM, i. e. missing in MetaCyc, MicroCyc, FAPROTAX and the IJSEM phenotypic database, we provide information for the upper taxonomy rank. For example, if a species has no functional information, MACADAM automatically requests functional information at the genus level, just like FAPROTAX. However, MACADAM can do this at any taxonomic rank, while FAPROTAX is limited to the order rank.
Conclusions MACADAM was designed for the microbiology community as a functional annotation information database based on multiple sources of data on functional annotations and on metabolic pathways (MetaCyc, MicroCyc, FAPROTAX and the IJSEM phenotypic database). The database is also based on the complete and interoperable NCBI taxonomy. MACADAM covers all known bacterial and archaeal phyla, as of February 2019. A standardized score enables quick comparison and comprehension of the potential presence of a pathway. If there is no functional information on the taxonomy entered, MACADAM automatically checks the upper taxonomic rank in order to provide functional information associated with related organisms to users. MACADAM can be explored via metabolites, reactions, enzymes, EC numbers or specific pathways.
A user-friendly web interface makes querying easy. MACADAM will be useful to all biologists who need to determine the functional potential of a prokaryotic species or any other taxonomic rank. Since the source code to build MACADAM is available to everyone (GitHub URL: https://github. com/ maloleboulch/MACADAM-Database), MACADAM can be included in any functional inference tool able to integrate the abundance tables of complete microbial communities generated, among others, we plan to include MACADAM in the FROGS software  (39)  to analyze amplicon metagenomics data.
Fourth, MACADAM is open access, Bacteria kingdoms. The user can request data about several freely downloadable and the possibilities of consulta- taxonomies or organisms at the same time and can do it tion are unlimited. Altogether, MACADAM facilitates using partial names. Second, MACADAM relies on up- taxonomical functional inference studies by synthesizing to-date NCBI taxonomy (22-24). Third, it is based on high-quality and up-to-date functional annotations and high-quality genome functional annotations thanks to the taxonomical information in one source. Table 1 shows the aggregation of (i) up-to-date RefSeq genomes annotated comparison between MACADAM, BioCyc, PATRIC and with MetaCyc functional data, (ii) high-quality functional KEGG databases in the context of queries on functional and.
Most previously published approaches indeed either focused on small subsets of noncoding   (6, 19) , or neglected to investigate intra-population trends in conserved regions while assessing potential functions of CNEs  (2, 12). Transposable elements (TEs) are mobile DNA elements capable of translocating randomly across an host genome. They have been shown to contribute to the genetic diversity and genome evolution of D. melanogaster through interference with regulatory regions  (20) (21) (22). While most existing studies focus on insertion polymorphism to asses functional roles of TEs  (20) , comparative genomics offer a way of assessing the effect of past events of TE domestication on genome evolution  (23). In the present study, we used a multiple-species alignment retrieved from the UCSC Genome Browser database, combined with polymorphism data obtained from 48 populations of D.
Genomic Datasets De novo search for CNEs in D. melanogaster was carried using a whole-genome MULTIZ alignment of 27 insect species and an associated neutral phylogenetic tree, retrieved from the nineteen populations from Ukraine, four populations from Germany and the United Kingdom, three populations from Austria, Finland, and France, two populations from Denmark, Spain, Switzerland, and Turkey and one population from Cyprus, Portugal, Russia, and Sweden. Paired-end Illumina Pool-seq analysis was performed on thirty-three to forty male diploid genomes for each sample. The output reads were subsequently trimmed, aligned on the reference sequence of melanogaster, and filtered in order to retain robust polymorphic sites  (Kapun et al. 2018).
Identification of conserved elements We made use of the R-friendly version of PHAST (PHylogenetic Analysis with Space-Time models, release 1. 6. 5  (26) ) to retrieve discrete conserved elements in D. melanogaster's genome. PHAST takes a multiple-species alignment combined with a phylogenetic tree for building a phylogenetic-Hidden Markov Model of sequence evolution (PhyloFit). This model is then used to assign basewise conservation scores along a region of interest. Finally, discrete conserved blocks can be identified, based on expected values of coverage and length for conserved elements (PhastCons). The software was run along the 2L, 2R, 3L, 3R, and X chromosomes of D. melanogaster (due to poor sequence conservation outside of the melanogaster subgenus, chromosomes 4 and Y were not searched for CNEs).
Measuring the proportion of adaptive substitutions in CNEs Keightley & Eyre-Walker  (15)  have developed the software DFE-alpha for measuring the proportion of adaptive substitutions (α) in a putatively selected region using allele frequency spectra. We made use of two of their programs (release 2. 15): est_dfe for measuring the strength of deleterious fitness effects and est_alpha_omega for estimating the value of α in CNEs. Runs of est_dfe and est_alpha_omega were performed on the different classes of CNEs using the combined DAFS for all populations, and assuming a constant population size for melanogaster samples. In addition to allele frequency spectra, estimation of alpha requires counts of divergent sites in selected regions, which were recovered using D. sechellia and D. erecta as outgroups (polymorphic sites contributing to the allele frequency spectra were not considered when estimating divergence).
Effects of phylogenetic distance on PhastCons detection power Assessing the effect of phylogenetic distance on PhastCons detection power revealed that species past a divergence threshold (for this case, outside of the Drosophila genus) did not improve the detection of conserved elements, due to poor genomic resemblance with the target species. (D. melanogaster) When only considering blocks shared by a minimum number of species (in order to remove regions containing indels and gaps), we found PhastCons performances to be nearly identical when the program was run on all species (27 insects) or on a subset of 15 species from the melanogaster group. However, recovery of conserved regions dramatically decreased below a certain divergence threshold (PhastCons run within the melanogaster subgroup, 5 species).
Adaptive substitutions in CNEs Estimates of α, the fraction of nucleotide divergence driven by positive selection, were computed in the different classes of CNEs using the software DFE-alpha (see Material & Methods) on newly-built DAFS. When all classes of CNEs were pooled, α was estimated at 0. 29, i. e. 29% of nucleotide divergence in CNEs is expected to be driven by positive selection. α was found highest in UTRs (α 5' UTRs = 0. 49, α 3' UTRs = 0. 40) and ncRNAs (α ncRNAs = 0. 42), intermediate in proximal regions (α proximal = 0. 33), and lowest in distal regions and introns (α distal = 0. 28, α introns = 0. 24).
Limits of statistical significance in very large datasets Genome-wide datasets (alignments, annotation files, polymorphism data…) are generally characterized by a very large number of entries, leading most statistical measurements to exhibit extreme significance  (30). This phenomenon is linked to large samples drastically increasing the power of statistical tests, even if the practical significance of the considered effects (i. e. the interpretation that can be drawn from the observed trend) is nearly nonexistent. Thus, many of the tests performed on CNEs, such as the estimations of differences in size and GC% between annotation types, produced extremely low p-values, even though the observed differences seemed far too weak to be interpreted as consequences of a biological effect. Both standard errors and bootstraps are strongly affected by this "large-sample size issue"  (30).
Influence of phylogenetic distance on PhastCons detection power Since its original publication (2), the PhastCons program (now implemented in the PHAST software (  26 )) has been widely used for retrieving discrete conserved elements in many metazoans and plants. Most species identified in the UCSC Genome Browser database have an associated PhastCons track of predicted conserved regions. Because of the questionable handling of indels and gaps by PhastCons (read as missing data), we chose not to use the existing set of predicted conserved elements available in the UCSC database for D. melanogaster (http://hgdownload. soe. ucsc. edu/goldenPath/dm6/phastCons27way/), identified using a multiple-species alignment of 27 insects. When examining sequence conservation within elements from the UCSC dataset, we determined that the most distant species have very little influence on the identification of conserved blocks.
Polymorphism estimation European populations The European consortium DrosEU recently generated Pool-Seq data from wild-caught flies from diverse sampling sites (Kapun et al. 2018). We used SNP variants detected in 48 samples from more than 30 localities all across Europe for our polymorphism analyses. Recovery of low-frequency SNP variants in the DrosEU populations was strongly affected by sequencing bias. Read coverage among samples fluctuated widely depending on the sequencing run (5 in total), resulting in an uneven recovery of rare variants across populations, as the quality thresholds used by the DrosEU Consortium discarded lowfrequency alleles more easily when sequencing depth was small. While nucleotide diversity (Tajima's π) is only marginally affected by rare variants, Watterson's estimator of genetic diversity (θ) is heavily influenced by their presence/absence.
Acknowledgements The author thanks  M. Hubisz  for help in exploring the subtleties of the rphast package for R,  M. Kapun  for advice and information on DrosEU polymorphism data and  C. Vieira-Heddi  for helpful discussions. Rphast is a R package developed by  M. Hubisz  & al. for analyses in comparative and evolutionary genomics. DFE-alpha is a software developed by  A. Eyre-Walker  and  P. Keightley  for estimating deleterious fitness effects and characterizing adaptive substitutions using site frequency spectra.
castaneum). Divergence between Drosophila species was estimated to range from 25 to 40 My (24) (vs >300 My for the whole alignment (25)). Polymorphism data in 48 European populations of D. melanogaster was obtained from the European Drosophila Population Genomics Consortium (DrosEU). Samples consisted in: UCSC Genome Browser database (http://hgdownload. soe. ucsc. edu/goldenPath/dm6/multiz27way/). Besides the reference assembly of D. melanogaster (BDGP6), the alignment comprised four flies from the melanogaster subgroup (D. simulans, D. sechellia, D. erecta, D. yakuba), ten flies from the.
First stage of the game: coalition formation Cooperative game theory is related primarily with coalitions, that is, groups of agents, who coordinate with each other to increase their payoffs. The clustering algorithms are one of the famous methods to set optimal coalitions. Clustering is the unsupervised classification of patterns into groups (clusters). Clustering algorithms divide a dataset N into several disjoint subsets (clusters), so that the characteristics in each subset shares some common feature -which is often measured by the proximity level according to some defined distance measure. Examples of distance measures include: Hamming distance, Manhattan distance, and Euclidean distance. The last will be utilised in the paper. The general aim of the most commonly used clustering strategies is to obtain a partition which minimizes the square error.
The existence of a functional relationship between speech perception and production systems is now widely accepted, but the exact nature and role of this relationship remains quite unclear. The existence of idiosyncrasies in production and in perception sheds interesting light on the nature of the link. Indeed, a number of studies explore inter-individual variability in auditory and motor prototypes within a given language, and provide evidence for a link between both sets. In this paper, we attempt to simulate one study on coupled idiosyncrasies in the perception and production of French oral vowels, within COSMO, a Bayesian computational model of speech communication. First, we show that if the learning process in COSMO includes a communicative mechanism between a Learning Agent and a Master Agent, vowel production does display idiosyncrasies.
COSMO ("Communicating Objects using Sensory-Motor Operations") is a probabilistic model implementing the relationships between all these variables, and exploiting these relationships for performing speech perception and production tasks. Written in the Bayesian Programming framework  [59, 60] , COSMO encodes in a probabilistic manner the internal representations of the agent. It has already been defined, implemented and evaluated in previous work: to account for the emergence of sound systems in human languages  [58] , to demonstrate the indistinguishability between auditory and motor systems under "perfect learning conditions"  [57, 61]  and to analyze the complementary role of the auditory and motor systems  [62, 63] , in relation to neurocognitive data  [64]. It has also provided a framework for the development of speech production models, analyzing the way they could deal with variability due to coarticulation  [46]  and adaptation to sensory perturbations  [65].
This computational model does fit naturally inside the definition of the "General Auditory and Learning Approaches to speech perception" introduced by  [2] , that they call the "General Approach" (GA) and define in the following way (p. 154): "GA does not invoke special mechanisms or modules to explain speech perception. Rather, it assumes, as a working hypothesis, that speech sounds are perceived using the same mechanisms of audition and perceptual learning that have evolved in humans or human ancestors to handle other classes of environmental sounds". Let us now consider how COSMO models a motor theory of speech perception. Motor decoding is characterized by the distribution P(O S | S). This means that the COSMO model would search for a phonetic unit most likely to have produced a gesture resulting in the sound S.
This is literally quite close to the "revised" version of the motor theory, in the classical paper by  [5]. Quoting the abstract of this paper, phonetic perception would consist in detecting "the intended gestures of the speaker that are the basis for phonetic categories. Built into the structure of this module is the unique but lawful relationship between the gestures and the acoustic patterns in which they are variously overlapped". Hence the theory states that the relationship between sounds and gestures enables the recovery of gestures from sounds, and gestures provide the basis for categorization. Technically, in COSMO, from the use of Eq (1) and principles of Bayesian inference, it can be shown that: PðO S j SÞ / X M PðM j O S ÞPðS j MÞ : ð3Þ In the second member of this equation, the second factor P(S | M) conveys the "lawful relationship between the gestures and the acoustic patterns", while the first factor P(M | O S ) conveys the relationship between gestures and phonemes, since "the intended gestures of the speaker [.
Specification of sensory and motor variables in the VLAM articulatory model. In the following, linguistic objects O S and O L in COSMO can be one of seven discrete elements corresponding to seven vowels among the ten analyzed by M&S: /i u e o ε ɔ a/, which happen to constitute the preferred 7-vowel system in human languages  [77]. We removed the front rounded vowels /y ø oe/ from the analysis to simplify the current study, but we will see that the final results are quite clear and do not seem to depend on the studied vowel. Motor and sensory representations are based on a realistic model of the vocal tract, VLAM (for "Variable Linear Articulatory Model"  [78] ). This model features seven articulatory parameters derived from a guided Principal Component Analysis of cineradiographic images of the vocal tract.
Finally, the correlation coefficients between data in the perception and production tasks (see Table  3 ) are lower (around 0. 60) in the M&S study than in the perceptuo-motor and motor simulations (where they are close to 1). This is of course due to the fact that our simulations are intrinsically less noisy than real data. Indeed, behavioral data are likely to include a large number of factors of interpersonal variation and inter-trial variability, which we did not aim to account for in this study, and were thus not included in the model.