dataesr/scientific-paragraphs-categorization huggingface dataset:
Ontology Requirements Specification The needs for the development of shorter and more powerful breeding schemes is a strong motivation for sharing phenotypic information linked to genes of interest and traits. Building an open and shared database for marker-based assisted selection (MAS) in bread wheat was the SAMblé project objective (2010-2014)  [24]. The SAMBlé database should support both short-term MAS-related goals of breeders, the intended users, and long-term research goals of researchers on underlying biological mechanisms of phenotypes. The information considered for the database was the existence of links between one or more markers and genes of agronomic interest in bread wheat. The information sources were the scientific literature, gene catalogs and in-field and high-throughput phenotyping experiments.
In scientific papers, phenotypic information is frequently linked to varieties, genes or markers and traits as in Fig. 8 , which makes it extremely relevant for breeding  [15]. This information was first automatically extracted from the literature, then assessed against reference material and elite material (335 varieties) under field conditions for different traits of interest. Finally, the markers that gave the best results and could be used in breeding selection schemes were recorded in the database to be queried by the partner breeders  [24]. The traits considered in the SAMBlé project were related to four main large topics, namely, disease resistance, resistance to abiotic stress, plant development, and baking quality. Representative queries of the breeders were, "which alleles and markers are involved in resistance to rust (e.
g. , leaf rust, stripe rust, stem rust)" and "what are the varieties tested. " Same question arises for "bread making quality (e. g. , flour quality, color, composition, mechanical property, crumb firmness)?". A general objective of SAMBlé was to develop a shared database with the information collected by the project that would be easily searchable. The WTO was designed to support this goal. The ontology should support queries on traits and phenotypes at various levels of aggregation combinied with other criteria on markers, genes, and varieties. To this purpose we created WTO as deep non-strict hierarchies of traits, phenotypes, and environment factors. Non-strict means here that one concept may have several direct parents forming a direct acyclic oriented graph. It covers the large set of topics of the SAMBlé database, ranging from development or resistance to stress to food quality.
The core model was then extended by reusing information from three external sources: scientific literature, the Catalog of Gene Symbols for Wheat  [25]  and GrainGene database  [26]. The biotic stress response, diseases, and pathogen WTO subtrees (see Section 2) were then significantly restructured by wheat disease experts. We adopted the Obo-Edit tool as ontology editor, to make it easier for biologists and breeders to revise and enrich WTO, compared to more powerful but less user friendly tools.
Other external sources of wheat trait terms To identify complementary relevant trait terms, we also used the Catalog of Gene Symbols for Wheat (WGC)  [30]  available online at the Wheat Genetics Resources Database of Japan as a PDF file at the date of WTO building in 2011. The main contribution to WTO from the catalog was related to plant morphology (e. g. , plant height) and physiology (e. g. , response to photoperiod). The GrainGenes  [26]  database was also used for the study of biotic stress response. GrainGenes is a comprehensive resource for molecular and phenotypic information for wheat maintained by U. S. Department of Agriculture and mirrored by MaIAGE. Grain-Genes web pages listed general traits and specific traits for wheat, barley, and oat species from which we identified some wheat disease names and their pathogen agents.
WTO evolution In 2017, with the purpose of using WTO for managing other phenotypic databases than the SAMBlé one, we evaluated WTO scope with respect to two external resources. The WIPO (the Wheat INRA Phenotype Ontology formerly named the INRA Wheat Ontology)  [31]  developed by URGI-INRAE and the "list of wheat descriptors for Characterization and Evaluation" of the NARO GeneBank project. A few more morphology terms such as 'presence of awn' , 'glume pubescence' , 'glume color' were then added to WTO.
WTO for marker-assisted selection To be used as the conceptual formalization of the SAMblé database schema, WTO was integrated into the MAS (Marker Assisted Selection) knowledge model detailed in Nedellec et al. 's study  [15]. The MAS model was designed to manage the entities and relations of the SAMBlé database. It contains 8 entity types and 14 n-ary relationships for the representation of the genotypic and phenotypic information and relationships collected from the literature and experiments of the SAMBlé project. The main MAS model entities are 'Marker' , 'Type' , ' Allele' and 'Gene' , 'Trait' and 'Phenotype' and 'Variety'. 'Type' represents the type of method used to identify the marker, e. g. , amplified fragment length polymorphism, microsatellite. The main relationships are 'Marker tags Gene in Variety' between markers, genes and varieties, 'Trait has Phenotype in Variety' between traits, phenotypes and varieties and 'Gene expresses Phenotype in Variety' between genes, phenotypes, and varieties.
The connection of the MAS model to WTO is achieved through the straight forward alignment of two pairs of MAS and WTO classes: (1) the MAS 'Trait' class is aligned with WTO 'Plant property' class, the root of the trait subtree (2) the MAS 'Phenotype' class is aligned with WTO 'Phenotype' class, the root of WTO phenotype subtree. The other MAS classes (e. g. , Gene, Marker, Variety) are also connected to nomenclatures and catalogs (e. g. , Genes nomenclatures, Markers lists, and Variety catalogs) for data standardization. The integrated MAS and WTO model was successfully used for the management of SAMBlé database information and for information extraction from text.
Wheat Trait Ontology Usage WTO has been validated through the use by breeders and researchers involved in the SAMBlé project of two end-user applications, the SAMBlé database interface  [24]  and the Wheat literature semantic search engine AlvisIR. AlvisIR supports queries on genes, varieties, markers, phenotypes and traits extracted from PubMed references. Phenotype and trait expressions in text are normalized by WTO concepts. Fig. 10  gives an example of a semantic search for phenotypic information. The example query asks for documents where the gene 'Lr34' is mentioned in relation to the trait 'resistance to rust' in 'wheat' by combining the three keywords, 'lr34' , 'resistance to rust' , and 'wheat'. The first hit displays a document extract where 'adult plant stripe rust resistance' (underlined in green) is tagged by the query 'resistance to rust' keyword.
Discussion Beyond semantic search, ontology-based fine-grained information extraction is a key component of the integration of textual information with experimental and genetic data. However, the reference knowledge models often differ with the sources and the nature of the information. Their alignment and user query rewriting are a major challenge for data integration  [33]. Significant work has been done on Wheat Data Interoperability Guidelines  [34]  that focuses on Minimum Information About a Plant Phenotyping Experiment (MIAPPE)  [2, 35]. For experimental data, observation variables including traits but also observation protocol, unit of measure and development stage are critical for properly documenting the observations and determining if observations are comparable or not. This leads to building trait ontologies as WIPO  [31]  or Crop Ontology  [13]  where the trait leaves are database variable traits (e.
g. , 'Susceptibility to leaf rust' in WIPO,' Leaf rust severity' in Crop Ontology). Phenotypes, the values of the traits, (e. g. , 'Susceptible to leaf rust') are not conceptualized as classes but represented by the database numerical data as values of the trait variable. For instance, in WIPO the trait Disease  intensity score takes values on a 1 to 9 increasing scale (1, no disease; 9, very severe). Such ontologies are suitable for accurately documenting observations and for the computation of correlations by statistical tools. Conversely, WTO aimed at managing both traits and phenotype values represented by expressions, as they occur in the scientific literature. For instance, in the the leaf rust susceptible cultivar 'GA 100' phrase, the phenotype value is 'leaf rust susceptible' and the variety is 'GA 100'.
In this way, WTO representation then supports SAMblé data discovery by direct queries on traits and phenotype values (e. g. , 'Leaf rust susceptibility') at various levels of generality (e. g. , 'Rust susceptibility' , 'Fungal disease susceptibility') and their relation to other information (e. g. , cultivars). Similar queries on observation databases that follow MIAPPE recommendations would require the translation of numerical values by using value domains or thresholds, i. e. , discretization and hierarchization of the phenotypes. Moreover, the lack of depth of ontologies such as WIPO or Crop Ontology with a comb-like structure does not allow high-level queries. An example in WIPO, is the trait 'Susceptibility to leaf rust' , which is a direct subclass of the high level 'biotic stress trait' without intermediate levels.
Similarly, in the Crop Ontology the trait 'Fusarium head blight AUD-PC' is a direct subclass of 'biotic stress trait'. Another representative example is 'Nitrogen harvest index'. In WIPO and Crop Ontology, it has only one direct ancestor, which is 'Quality trait' , with 51 other sibling traits in Crop Ontology. In WTO, 'Nitrogen harvest index' has five successive ancestors: 'Nitrogen use efficiency' , 'Macronutrient use efficiency' , 'Nutrient use efficiency' , 'Growth' by increasing order of generality. The integration of the two sources of data, observations, and synthetic information from text in a same data management system should preserve the best of the two approaches. It would require the alignment of the ontology classes and the rewriting of the phenotype variable values to map them to qualitative descriptors.
Conclusion We proposed WTO, a reusable ontology of bread wheat traits and phenotypes and related environmental factors. The design of the model relies both on domain expert knowledge and the analysis of evidence published in the scientific literature. The WTO model is Genomics & Informatics 2020;18(2):e14 deeply structured, well reflecting the domain knowledge. It facilitates navigation and reuse for data and knowledge discovery. The model was designed to support the extraction and the management of marker-assisted selection information. WTO is also a contribution to the description of the link between genetic and phenotypic information. Concept synonyms were directly extracted from the literature, which turns WTO a suitable resource for Information Extraction and Information Retrieval. WTO has been assessed for its consistency through its use.
Acknowledgments The authors would like to thank  INRAE  biologist researchers, especially  Jacques Legouis  and  Thierry Marcel  for their contribution to WTO extension. This work was partially funded by  Oséo  through the  Quaero  project, by  FSOV  through the  SAM blé project. It has also received support from the wheat use case of the  EU   OpenMinTeD  project under the  H2020-EINFRA-2014-2  call, Project ID:  654021. The author work at the 6th  Biomedical Linked Annotation Hackathon  ( BLAH6 ) was supported by the  Database Center for Life Science (DBCLS).
We report experiments on the challenging minimum energy problem in Computational Protein Design (CPD). For this aim, we designed new larger instances that are well structured and supposed to be more difficult to solve than those generated in  [26]. We show the practical benefit of our approaches compared to TOULBAR2 and FIXBB 2  the simulated annealing algorithm provided by the Rosetta package for CPD. The paper is organized as follows. Section 2 recalls preliminaries. Section 3 presents UDGVNS. Section 4 describes our parallel version UPDGVNS. Sections 5-6 report experiments we performed. Finally, we conclude and draw some perspectives.
It also finds good initial upper bounds using LDS (with discrepancy limit set to 1) and stochastic local search GLS +  [12]. We used the standalone code of DAOOPT version 0. 99. 7g-UAI12 (with option settings -mplp=2000 -mplps=60 -slsX=20 -slsT=10 -t 30000 -orderTime=180 -jglp=1000 -jglps=60 -i 35 -m 4096 -match -y -lds=1 for 3600-second time limit). We tested three parameter settings as suggested in  [45] , controlling the time spent to compute initial lower and upper bounds. In the 3600sec setting, SLS is run 20 times with 10 seconds per run. The best solution found is used as an initial upper bound for an AND/OR exhaustive tree search. We compared also with an older version of TOULBAR2, namely  INCOP+TOULBAR2 19  [28]  won the UAI 2010 Evaluation at 20-minute time limit. INCOP+TOULBAR2 takes a starting solution from the best result of three runs of the IDWalk  [46]  local search algorithm (100,000 local moves per run).
It is followed by an exhaustive hybrid best-first search  [5]. IBM ILOG CPLEX 12. 7. 0. 0 (using parameters EPAGAP, EPGAP, and EPINT set to zero to avoid premature stop) was reported as being very competitive on some image analysis  [41]  and Markov Random Field problems  [28]. CPLEX explores its search tree using bestfirst search. It applies several heuristics methods to find good solutions before and during the search. We also compared with message-passing algorithms: LIB-DAI 20   [47] , winner of the UAI 2010 Evaluation at 20sec. and 1hour time limits, MPLP2 21  [8, 9] , and TRW-S 22  [6]. Note that LIBDAI and TRW-S are applied on 19 www. inra. fr/mia/T/toulbar2 version 0. 9. 8 with parameters -i -dee -hbfs. 20 bitbucket. org/jorism/libdai. git version 0. 3. 2 using UAI 2010 settings. 21 cs.
nyu. edu/ ˜dsontag/code/README_v2. html using 2. 10 -7 int gap thres. 22 github. com/opengm/opengm v2. 3. 5 with TRW-S v1. 3 stopping after 100, 000 iterations or 10 -5 gap thres. the original instances rather than the preprocessed ones as we found they produced better results without applying VAC first. All solvers read problems in uai tabular format, except CPLEX which uses the local polytope formulation (called support encoding in  [28] ). All computations were performed on a cluster of 48-core AMD Opteron 6176 at 2. 3 GHz and 384 GB of RAM with a 1-hour CPU time limit  23.
Computational Protein Design In Computational Protein Design (CPD), the challenge of identifying a protein that performs a given task is defined as the combinatorial optimization of a complex pairwise energy function over amino acid sequences and 3D geometry  [49]. This holds great interest for medicine, synthetic biology, nanotechnologies and biotechnologies  [50] [51]  [52]. We used the CPD problem as a difficult benchmark to test our tree-decomposition based methods. For that, we generated 21 large instances with small treewidth selected from the Protein Data Bank 24  (PDB). These instances have been selected on the basis of 3D criteria described in a supplementary material  25   maximum domain size from d = 383 to 450, and between 1, 623 and 6, 208 binary cost functions. The min-fill treewidth ranges from w = 21 to 68, resulting in small ratios of treewidth per number of variables, from w n = 0.
Conclusion and Perspectives In this paper we proposed a unified view of VNS methods including various LDS and neighborhood evolution strategies. Experiments performed on difficult instances, coming from a large graphical model benchmark, showed that our hybrid method has a much better anytime behavior than existing tree search methods and still being competitive for proving optimality. UDGVNS takes advantage of the good convergence properties of DGVNS and proves optimality in many cases. On structured or unstructured problems of large sizes, like CPD, UDGVNS obtains solutions of (very) good quality, thus outperforming the state-of-the-art FIXBB Rosetta Modeling software package used in the CPD field. We further proposed a parallel version of our method improving its anytime behavior. It remains as future work to manage dynamically the tree-decomposition associated to the instance to solve.
Acknowledgments This work has been partially funded by the  french Agence nationale de la Recherche , reference  ANR-16-C40-0028. We are grateful to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  ( Bioinfo Genotoul ) and the high performance center of  Cerist-Algiers in Algeria  for providing computing and storage resources. We thank  Mathieu Fontaine  for his contribution to the code of DGVNS.
2) TSP-LIB INSTANCES Both the second and third sets of instances are from TSP-Lib, which are real world application instances from the task of drilling holes in printed circuit boards and are usually used as the benchmarks for various routing and location problems. The two sets respectively consist of 15 instances from u1060 (|N | = 1060) and 15 instances from u1817 (|N | = 1817). For these 30 instances, p ranges from 10 to 150 and the nodes are given with two-dimensional coordinates. Thus, we need to calculate the Euclidean distance between each pair of nodes to get the whole distance matrix, i. e. , a complete graph. Our algorithm was programmed in C++ and the experiments were conducted on a Linux PC with Intel Xeon CPU E5-2609 v2 2. 50 GHZ processor and 32 GB RAM with 8 cores. The time limit for each given radius of each instance was set to 4 hours.
Next, we encode the instance for the given radius with a simplified graph into CNF format by sequential counter and parallel counter. So, there are two different SAT instances to solve with SAT solvers for each given radius. We finally solve these SAT problems transformed from set covering subproblems to judge if the given radius is feasible for the p-center problem by using Maple_CM, MapleLCMDistChronoBT and Sparrow2Riss-2018. Note that a SAT instance encoding a p-center instance consists of a relatively small subset of very long clauses only containing positive literals (constraint 1) and a large subset of clauses encoding the cardinality constraints (constraint 2). This fact is not exploited by the general-purpose SAT solvers we test in this paper, but could be exploited by a SAT solver specialized to solve the p-center problem.
In addition, for the instances of TSP-Lib, the SAT solvers can obtain the results showed in Tables  5  and 6  with less average computational time, and the results of TSP-Lib instances obtained by SAT solvers can be proven to be optimal solutions (See Section V-B for the proof). In sum, we present the summarized computational statistics of all the solvers on the two datasets (OR-Lib and TSP-Lib) in Table  7 , where column Sparrow gives the statistics of the Sparrow2Riss-2018 solver, column Exact shows the average statistics of the two exact SAT solvers (with the SEQ encoding), and column Ref presents the statistics of the best reference algorithm GRASP/PR since it outperforms other reference algorithms in terms of both solution quality and run time. Rows Num. and Avg. present the number of instances for which the optimal or the best known results can be obtained and the average computational time to reach the best known results for the corresponding solvers, respectively.
, 2015) , urge improved comprehension and characterizations of bat species. Although bats make up more than 20% of existing mammals with 1,400 species  (Lazzeroni et al. , 2018) , only 38 bat genome assemblies are currently available in the NCBI Genbank database. Several different sequencing strategies have been used in the past years to sequence bat genomes, including (i) high coverage long-read sequencing strategy, e. g. , 83x of PacBIO Eonycteris spelaea  (Wen et al. , 2018) , (ii) Illumina sequencing (e. g. , 93x for paired-end libraries and 67x of matepair libraries) for P. alecto  (Zhang et al. , 2013)  and also (iii) hybrid sequencing, e. g. , 145x of Illumina short reads and 24x of PacBio long-reads for Rousettus aegyptecus  (Pavlovich et al. , 2018). Combined technological developments applied to genome assembly, including Hi-C  (Ghurye et al.
Illumina Primary Analysis Raw reads were trimmed using cutadapt (version 1. 9. 1 with options: -q 30 -m 115 for DNA-seq and -q 30 -m 50 for RNA-Seq). For DNA-seq, resulting reads were corrected using lighter (version 1. 1. 1). Expected genome size has been predicted using kmergenie software (v1. 7016).
Secondary Scaffolding For gene-based scaffolding, RNA-Seq data has been aligned using hisat2 (v2. 1. 0) on the assembly. Gene annotation have been predicted using augustus (v3. 3. 1) with human training set. Finally, these data were integrated with AGOUTI (v0. 3. 2). For reference-based scaffolding, genome sequences of P. vampyrus (pteVam2), P. alecto (pteAle1), and R. aegyptecus (Raegyp2) were extracted from Genbank database and aligned with the solid-scaffold assembly using progressive Cactus (https://github. com/glennhickey/progressiveCactus). The software Ragout v2. 1 performed the scaffolding based on the multi genome alignment using both pteVam2 and pteAle1 assembly sequences as reference, Raegyp2 as outgroup and the solid scaffold assembly as target  (Kolmogorov et al. , 2018).
Scaff2link Script We developed an algorithm to integrate, in a conservative way, scaffolding results given by Ragout and AGOUTI (available at: https://github. com/jfouret/scaff2links). Briefly, this algorithm builds a graph with phylogenetic and gene-based linkage information as input for edges, and fasta formatted sequences as input for nodes (Supplementary Figure  S1 ). First, linear paths are simplified and then higher level of simplification is added using pattern of directed acyclic graph (DAG) (Supplementary Figure  S2 ). The pseudocode of the algorithm is given in Supplementary Material.
Genome Annotation Genomes from P. vampyrus (pteVam2), P. alecto (pteAle1), and R. aegyptecus (Raegyp2) and the Ma_sr-lr_union100 assembly were aligned using progressive Cactus (https://github. com/ glennhickey/progressiveCactus)  (Paten et al. , 2011). R. aegyptecus was used as outgroup to improve the multiple alignment process and has been chosen because it was a species close to the Pteropus genus with a good quality assembly. Gene annotation was performed using the Comparative Annotation Toolkit (CAT) v0. 1  (Zhang et al. , 2013)  automated pipeline with P. alecto as reference and P. medius as target with a step of ab initio gene prediction using Augustus 3. 3. 1  (Stanke and Waack, 2003). Of note, R. aegyptecus genome assembly has been used solely to improve the multiple genome alignment of the three Pteropus species genomes, however, R.
2. 9  (Stamatakis, 2014). Separately, bootstrapping was done with 100 iterations. The random seed "12345" was used at all steps with RAxML. The tree was rooted manually. A MSA subset with 500,000 sites was used for estimation of divergence times using soft fossil constraints with PAML v4. 9j mcmctree program  (Yang, 2007). Configuration files used in PAML are available in the Supplementary Material. The procedure has been repeated with another subset with no significant changes in age time and confidence intervals. The minimum age of fossil data, obtained from paleobiodb database 2  , were used as lower bound; all values used for the calibration were specified (Supplementary Figure  S3 ). An upper constraint was set for the root using the upper value of the 95% confidence interval available for the Chiroptera order on Time Tree database 3   (Kumar et al.