dataesr/scientific-paragraphs-categorization huggingface dataset:
Concerning the other methods that could have been included, we chose to discard the standard 3D Marching Cubes  (Lorensen and Cline, 1987)  as well as its implementation in MorphoGraphX (Barbier de  Reuille et al. , 2015)  because the produced meshes do not contain adjacency connections between more that two cells, and fail at representing accurately the topology of cell junctions. The Generalized Marching Cubes  (Hege et al. , 1997)  would correct this specific problem, and so could probably the adaptive tessellation method  (Chakraborty et al. , 2013)  , but they are not available to our knowledge in any freely available package, and could therefore not be tested. The results of the evaluation of the retained methods are presented in Table  1  summing up the average value and standard deviation obtained for all the different quality estimators on the tested dataset.
ACKNOWLEDGMENTS Imaging: The authors thank  Pradeep Das ,  Vincent Mirabet ,  Jan Traas ,  Henrik Larsson ,  Yassin Refahi ,  Carlos Galvan-Ampudia ,  Géraldine Brunoud , and  Jonathan Legrand  for providing confocal microscopy images of living meristems and for the segmentation of 3D tissue images. Packaging: The DRACO-STEM package provided on GitHub was built thanks to  Jérome Chopard , using the long-term support tool pkglts.
In the era of WGS, PCR-based typing approaches targeting small genomic regions keep their relevance by being extremely fast, cheap and with a great potential to be implemented with high-throughput equipment. However, assess appropriate genomic targets, both sensitive and specific, is an important issue. To meet this challenge, empiric approaches based on a limited quantity of genomic information and validated on a limited number of isolates are usually used. The tremendous increase of bacterial genomic information theoretically offers the opportunity to realize in silico analyses to evaluate the performance of the genomic targets selected  [2]. Here, we have developed a bioinformatics tool called 'Genome Target Evaluator' (GTEvaluator), which makes it possible to rank the most suitable markers among a list, by calculating their sensitivity (S e ) and specificity (S p ) toward a genome dataset.
A distance value is then computed by these two parameters (S e and S p ) providing a dataset-dependent estimation of the quality of the markers. A Bayesian stochastic approach was also set up to take into account the limited amount of information on which the calculations were performed (i. e. number of genomes). GTEvaluator provides a simple way to take rational decision on the choice of genetic markers by taking advantage of the large WGS resources now available. In this study we have tested GTEvaluator on two well-known and widely distributed foodborne pathogens: Bacillus and Salmonella. The first test with Bacillus is used as a negative casecontrol. We wanted to differentiate, at the species level B. cereus and B. thuringiensis. We included in the input of the workflow, genomes of the two Bacillus species and genomic markers known to be common to both (B.
cereus and B. thuringiensis)  [3] [4] [5] [6] [7] [8] [9] [10]. The second test with Salmonella is used as a case study. We wanted to differentiate several serotypes of Salmonella enterica. We established a list of genetic markers selected from several studies focusing on molecular serotyping of S. enterica serogroups and serotypes  [11] [12] [13] [14] [15] [16]. The performance of molecular markers was measured on a data set of 134 genomes corresponding to 14 serotypes. To design experimental situations which can be faced by biologists, the 14 serotypes were not equally represented in the genome dataset. GTEvaluator analysis provides an objective evaluation of the value of markers which takes into account the representativeness of the genomic dataset.
Description of the workflow The 'GTEvaluator' workflow is composed of three Python scripts (Fig  1 ). The first script is called 'GTEvaluator_matrixMaker' and requires a list of genomes and a list of targets as inputs. This script uses 'fuzznuc' (version 6. 6. 0) to match all genetic markers on all genomes  [17]. At this step, the user has the possibility to set two parameters: first, the number of 5'-nucleotides to be trimmed on primers if necessary and second, the maximal distance between forward and reverse primers. 'GTEvaluator_matrixMaker' produces a matrix file in tabular format reporting the 'presence' (1) or 'absence' (0) of the targets in the genomes. Presence is defined as a perfect match between the marker and the genomic target (100% of sequence homology). The 'GTEvaluator_matrixMaker' matrix is used as input for the second script 'GTEvaluator_statistic' which computes specificity, sensitivity, statistical distances (see below) and confidence intervals for each genetic marker and each pre-defined subgroup (i.
Whole-genome sequences The GTEvaluator workflow was tested using 44 and 134 genomes of Bacillus spp. , and Salmonella enterica serotypes respectively. For the two applications, no plasmid sequences were used but only chromosomic sequences both in contigs or closed genomes (annotated or not). Full genomes with conventional taxonomic information were collected across public databases (S1 Table ). For the first application, two groups were created: one including 22 genomes of B. cereus and the other with 22 genomes of B. thuringiensis. For the second application, 14 different groups were created corresponding to 14 different serotypes of Salmonella enterica subsp. enterica selected among the most frequently isolated in humans, animal and food  [20, 21].
Choice of genetic markers Both couples of primers and probes can be used within GTEvaluator workflow. For the application on Bacillus spp. , 22 couples of primers are included in the list input file (. txt) of the script 'GTEvaluator_matrixMaker'. For the application on Salmonella, 17 couples of primers and 51 probes are compiled. These molecular markers are selected from a large selection of scientific articles published between 1998 and 2014 (S2 Table)  [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]. The Bacillus spp. primers set were selected from studies focusing on toxins involved in gastrointestinal diarrhea and emetic syndromes: nonhaemolytic enterotoxin Nhe, Bce I, haemolysin BL (Hbl), hemolysin II (Hly II), cytotoxins K (Cyt-CK1 and CK2) and cereulide (Ces). Additional primers used to discriminate psychrotrophic and mesophilic strains were also included  [3] [4] [5] [6] [7] [8] [9] [10].
Evaluation of genetic markers for Salmonella enterica serotype discrimination The classification of Salmonella isolates into serotypes is the official typing method used to monitor the spread of the foodborne pathogen and to trace back origin of contamination during epidemiological investigations. However, the conventional serotyping scheme of Salmonella enterica is an expensive and time consuming method. These drawbacks have led to the development of various molecular "serotyping" assays based on genetic markers. We applied GTEvaluator to evaluate the discriminatory values of 68 genetic markers selected from the literature  [11] [12] [13] [14] [15] [16]  against genomes of the 14 most frequent serotypes encountered in human clinical cases  [20, 21]. The GTEvaluator analysis was performed on a dataset of 134 genomes with a heterogeneous number of genomes per serotype (1 to 20) to highlight the importance of this parameter.
Indeed, except for the CRISPR sequences that are able to discriminate Paratyphi A and Typhi  [11] , the other molecular markers cannot be used in isolation. The discrimination of the other serotypes can only be possible by using these genetic markers in combination. It is also important to emphasize that the size of the genome dataset of a given subgroup is critical because markers having poor statistical performance in a small dataset (i. e. high distance values and high uncertainties) may finally be appropriate with a larger genome dataset. It must also be noted that the diversity of genomes within polyphyletic serotypes must be taken into account. In the present dataset this parameter did not influenced the results because the molecular markers analyzed were selected from studies focusing on high-throughput profiling of S.
Concluding remarks We have presented two situations (Bacillus and Salmonella) in which GTEvaluator was able to rank genetic markers toward a genomes dataset and found that it is an appropriate tool to accurately evaluate the most suitable genetic markers among a predefined list of markers. The GTEvaluator results were further evaluated through a statistical approach to measure the relevance of markers. The results obtained with these two situations allowed us to discuss the parameters (specificity, sensibility, distance value and uncertainties of distance defined by a Bayesian stochastic approach) used to rank the markers. The size of genome dataset, the diversity of the genomes included in the dataset and the selection of plasmid and/or chromosomal sequences were also mentioned in the discussion.
Finally the results obtained on the "Salmonella dataset" showed that some serotype (Enteritidis, Kentucky, Paratyphi A, and Typhi) could be confidently assigned with one genetic marker, while others would need further developments. Based on WGS, single-nucleotide polymorphism (SNP)-analysis, whole genome-or core genome-Multi Locus Sequence Typing (MLST) are efficient and can be considered as ultimate typing methods  [27, 28]. However, beside WGS, typing or detection methods using polymerase chain reaction (PCR)-based assays present numerous advantages in term of timeliness, versatility or cost. In the future it is likely that microfluidic or room temperature PCR devices will continue to be developed and will find novel applications in environmental, medical or food sectors  [29]. Consequently, there will be a continuous demand on the capacity to design specific and sensitive molecular markers.
Acknowledgments We thank  Fabrice Touzain ,  Yannick Blanchard ,  Anne Brisabois  and  Patrick Fach  (Anses) for helpful discussions. This work was supported by a grant from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 643476  (COMPARE) and by the  Salmonella network, part of Laboratory for food safety of the Anses (France). Authors' information: AF (M. Sc. ), LG (Ph. D. ), NR (Ph. D. ),  SCS  (Ph. D. ), RL (M. Sc. ) and MYM (Ph. D. HDR) are salaries at the French agency for food, environmental and occupational health and safety (Anses), and perform research in the field of bacterial genomics for applications in food safety.
An interesting example of a knowledge-based method for the short text analysis is the method developed by  [11]  in the framework of the Probase 2 lexical semantic network. As a knowledge resource, Probase is positioned as a graph database helping to better understand human communication and resolve ambiguities related to the human common sense knowledge. The Probase-based tools allow obtaining a concept distribution based on different scoring functions (conditional probability distribution 3 , point-wise 1 Unlike the dictionary sense, the sense refinement reflects the typical use of a term, its sense activated by a particular context 2 https://www. microsoft. com/en-us/research/project/probase/ 3 Conditional probability distribution of two discrete variables (words) P (A|B) = P (A) ∩ (B) P (B) mutual information 4 etc.
). In terms of relationship type representation, the publicly available Probase release only provides the is-a relationship data mined from billions of web pages. In the recent years, the research experiments using Probase has been focusing on segmentation, concept mapping, and sense disambiguation. As part of this work,  [11]  introduced a method for short text analysis that has been tested for analyzing queries. Prior to the analysis of short texts, the authors acquire knowledge from web corpus, Probase network as well as a verb and adjective dictionary It is stored as a set of resources such as co-occurrence network, is-a network, concept clusters, vocabularies etc. They solve the segmentation task by introducing a multi-fold heuristic for simple and multi-word term detection. It takes into account the presence of is-a and co-occurrence relationships between the candidate terms.
e. the relation between "hospital" and "doctor"). Third, a probabilistic logic program answering the target task is created. Such program contains the evidence set, the rule base (RB, weighted first order logical expressions), and a query. It calculates the conditional probability P (Query|Evidence, RB). This approach has been tested on the SICK 7  data for the tasks of semantic textual similarity detection and textual entailment recognizing. The results showed a good performance of this method over distributional only and logic only methods. This kind of approach rather considers lexical relations (revealing some linguistic phenomena), than purely semantic (language independent, world knowledge phenomena) relations. As the knowledge based distributional method, it suffers from the difficulty to qualify the relationships as it uses mainly the co-occurrence analysis.
After applying classical NLP tools for segmentation, part-of speech tagging, syntactic analysis, the extraction process from texts focuses on the anaphora resolution and verb argument analysis. The actions are modeled on the basis of the instructional text without considering the implicit information proper to the cooking recipes. The underlying knowledge resource is the case-based reasoning system Taaable  [7]. Despite the variety of their theoretical background, the existing methods of cuisine texts analysis converge on the necessity to have a richer context around the terms present in the input text. Such context can be obtained using the following approaches: -specific meta-language such as MILK  8  , proposed as a part of the SOUR CREAM project  [23] , SIMMR  [12]   9. SOUR CREAM stands for "System to Organize and Understand Recipes, Capacitating Relatively Exciting Applications Meanwhile".
MILK has been proposed as a machine-readable target language to create sets of instructions that represent the actions demanded by the recipe statements. It is is based on first-order logic, but allows handling the temporal order as well as creation/deletion of ingredients. A small corpus of 250 recipes has been manually annotated using MILK (CURD (Carnegie Mellon University Recipe Database)). Similarly, SIMMR  [12]  allows to represent a recipe as a dependency tree. The leaves of the tree are the recipe ingredients and its internal nodes are the recipe instructions. Such representation supports semantic parsing. MILK tags have been used to construct the SIMMR trees. The authors also propose a parser to generate SIMMR trees from the raw recipe text. Machine learning methods (SVM classification) have then been used for instruction-ingredient linking, instruction-instruction linking using SIMMR; -dynamic structures such as latent vector used by  [17]  and described earlier in this section; -graph-shaped resources (ontologies, semantic networks) and their use by projection on the text under scope (as described earlier in this section).
Evaluation and discussion The system has been evaluated using a incompatibility annotated corpus of 1 500 recipe titles. The evaluation data has been partially collected using structured document retrieval simultaneously with the raw corpus constitution. Relevant meta-tags (HTML tags present in micro-formats 29  ) have been used to pre-build incompatibility scores. The overlap between the different diets (vegan recipes are compatible with a vegetarian diet, low fat recipes are convenient for the low calories diet etc. ) has been taken into account. The labels have been adjusted and enriched (LowSalt, Kosher diets) by hand by the authors of the system because the structured meta-tags do not contain such information. The obtained evaluation scores are binary. For now, as our system is under improvement, we estimated that the score returned by the system should be ≥ 0.
Conclusion We introduced the use of a lexical-semantic network for nutritional incompatibility detection based on recipe titles. The scores obtained by our system can be used for building specific resources for machine learning tasks such as classifier training. Our experiments showed the implementation of a graph browsing strategy for incompatibildetection. Knowledge resource projection on raw text is a relevant method for short text analysis. Among the perspectives of the approach, we can list: -exogenous approaches (using external resources and processes to populate the graph) for the graph population; -endogenous approaches to the graph population (i. e. propagating the relevant relationships over the RezoJDM graph, relationship annotation); -mapping the resource to other existing semantic resources (domain specific ontologies, other wide-coverage semantic resources such as BabelNet  [19] ); -making the system evolve towards a multilingual (language independent?) nutritional incompatibility detection.
Sentences Involving Compositional Knowledge. This dataset includes a large number of sentence pairs that are rich in the lexical, syntactic and semantic phenomena (e. g. , contextual synonymy and other lexical variation phenomena, active/passive and other syntactic alternations, impact of negation, determiners and other grammatical elements). Each sentence pair was annotated for relatedness and entailment by means of crowd-sourcing techniques. http://clic. cimec. unitn. it/composes/sick. html.
In the presence of functional epistasis, allele frequency drift and changes in frequency of causal alleles due to selection will cause the response to artificial selection from the same base population to differ among replicate lines as well as within the same line over time  [2]. Unless interacting loci are identified and co-introgressed, a favorable allele at one locus may be detrimental in a different genetic background. Although it was shown that an additive model may explain a major part of the genetic variance in different datasets  [5] , this model does not explicitly capture any kind of interaction which may be present in biochemical pathways that connect gene expression with the ultimate target phenotype. Therefore, statistical models that incorporate interactions between loci have been viewed as potentially beneficial for genomic prediction  [2, [6] [7] [8] [9].
Methods We conducted an in silico divergent genomic selection experiment in Drosophila using the sequence based virtual breeding (SBVB) software  [15]. SBVB can use real sequence data for founder animals in a simulated population and simulates the genomes and phenotypes of offspring in a very efficient and flexible manner according to specified genetic architectures. Although SBVB is essentially a gene-dropping algorithm, it allows implementing selection by efficiently reading and writing haplotypes (see Additional file 1: Figure  S1 ).
Data We downloaded the public SNP data from the 205 inbred lines of the Drosophila melanogaster Genetic Reference Panel v2 (DGRP Freeze 2. 0, http://dgrp2. gnets. ncsu. edu/ [17] ). SNPs from chromosome 4 and indels were removed, resulting in 3,954,651 SNPs that were used for analyses. Chromosome 4 in Drosophila is normally ignored in population genetic studies since it is very small, does not recombine and is mostly heterochromatic. Missing values were imputed with Beagle4  [18]. SBVB allows the specification of variable recombination rates along the genome and between sexes as well as sex chromosomes. We used the genetic map from Flybase (www. flybase. org) and allowed for the fact that no recombination occurs in male Drosophila.
Full epistatic model using causal SNPs (E-QTN) As above except that G and D were obtained by using only the QTN. For a given set of markers (all or only causal SNPs), G was obtained from MM ′ / k j=1 2p j q j , where the elements of the m vectors for each individual are equal to -2p j , 1 -2p j , and 2 -2p j for genotypes B 1j B 1j , B 1j B 2j and B 2j B 2j , respectively  [21] , p j is the frequency of allele B 1j for the genotyped individuals of the population, q j = 1 -p j , and k is the number of SNPs. D was obtained as in  [7] : where the elements of the m d vectors for each individual are equal to -2p 2 j , 2p j q j and -2q Breeding values were predicted with the Bayesian generalized linear regression (BGLR) package  [24]. For prediction purposes, variance components were assumed unknown and, thus, estimated simultaneously (marginalized).
LCLs have been extensively genotyped and sequenced as part of large collaborative projects, such as the International HapMap  [18] , 1000 Genomes  [19] , ENCODE  [20]  and GTEx  [15]  projects. Despite their widespread use, there has been concern about using LCLs to model primary tissues, with two small-scale studies finding differences in gene expression profiles between LCLs and primary B cells  [21, 22]. While these studies found genes differentially expressed in LCLs compared to B cells, for example the over-expression of cell cycle genes, the regulatory mechanisms associated with this differential expression are not known. Here we performed a detailed investigation of gene expression and gene regulatory networks using two cell line and tissue pairs, LCL-vs-blood and fibroblast-vs-skin, to understand the regulatory networks mediating expression differences between the cell lines and their tissues of origin.
Pathways differentially expressed between cell lines and their tissues of origin The GTEx project collected post mortem biopsies from multiple tissues and created LCLs and fibroblast cell lines. For the analyses described here, we used only data from research subjects for whom primary tissue and matching cell lines were available. Data (version 6. 0) were available for 127 paired whole blood samples and LCLs, and for 244 paired full-thickness skin biopsies and primary fibroblast cell lines  [15] ; 89 subjects have data across all four groups. We did not find any clear separation of samples based on the year of analysis by the GTEx project (Additional file 1). The cell lines and tissues express similar numbers of genes mapped to similar functional categories (protein coding, antisense, pseudogene, lincRNA, and other; Additional file 1).
7 × 10 -09 , Fig. 4b ). The combined GTEx/ENCODE results suggest that cell cycle regulation involves a complex interplay between changes in the expression of regulatory TFs and alterations in the binding of these TFs to their targets. There is also extensive functional evidence that SMAD5 targets genes to inhibit cellular growth. SMAD proteins have a key role as signal transducers of the TGF-β family members to mediate growth inhibition and apoptosis  [40]. SMAD5 negatively regulates cell c Boxplot of the gene in-degree differences for the genes in the KEGG cell cycle pathway and for genes not in this pathway (significance measured using a t-test). Reduction of gene in-degree difference indicates that the genes in the cell cycle pathway are less strongly targeted by TFs in the cell line compared to its tissue of origin proliferation during embryonic hematopoiesis  [41] , in Bcell lymphoma  [35] , and it induces cell cycle arrest in response to shear stress in tumor cell lines  [42].
By investigating differential targeting of specific biological processes, we found the most striking difference in the regulation of processes associated with cellular proliferation. Processes including cell cycle, DNA repair, and DNA replication were more highly expressed in cell lines, where they had lower overall targeting by a number of cell cycle-associated TFs that are known to function as repressors. The top TFs found to negatively regulate the expression of cell cycle genes (SMAD5, IKZF1, USF1, USF2) have been previously shown to have a role as transcriptional repressors and as inhibitors of cellular proliferation in tumor cell lines  [35, 42, 44, 48]. We validated the negative correlation between the TFs and cell cycle target genes expression using ENCODE Chip-Seq as an independent data set.