dataesr/scientific-paragraphs-categorization huggingface dataset:
For model selection, the stepwise removal of terms has been used, followed by likelihood ratio tests (LRT). Term removals that significantly reduced explanatory power (P <0. 05) were retained in the minimal adequate model. All statistical analyses and graphics were performed using R 2. 15. 2 and GraphPad Prism v. 5. 0 software, respectively. The formula ((Hn-Ho) / (Ho-Hs))*(N/S) was used to calculate the Fried competitiveness index (C) for the ratio 100:100:100 as described by Fried,  [27] , where Hn and Hs denotes hatch rate from eggs of females mated with untreated and sterile males, respectively, Ho is the observed egg hatch rate and N is the number of untreated males and S is the number of sterile males.
Seedlings of N. benthamiana were mechanically inoculated with crude sap of leaf sample infected with LMV-Muju in 1X phosphate buffered saline, pH 7. 4, and grown in insect-free incubation room maintained at 23 o C with 16 h light period. Disease symptoms of the test plants inoculated with LMV were observed at 10 days post-inoculation (dpi). At 21 dpi, each plant showed systemic mosaic symptoms (Fig. 1b ). We also detected LMV from test plants by RT-PCR. In order to determine the complete LMV genome sequence, 8 primer sets were designed from well conserved regions of four complete LMV sequences (GenBank accession no. X97704, X97705, AJ278854, and AJ306288) obtained from NCBI GenBank database (Table  1 ). Total RNA was extracted from LMV-Muju with the TRI Reagent (MRC, USA) and cDNA was synthesized using random N25 primer with RevertAid Reverse Transcriptase (Thermo scientific, USA) according to the manufacturers' instructions.
PCR was performed with 32 cycles using the procedure (95 o C, 30s; 53 o C, 30s and 72 o C, 2 min), followed by additional extension cycle at 72 o C for 5 min. Amplified RT-PCR products were cloned and sequenced as above. Assembly of all the sequences was conducted using the DNAMAN version 5. 0. 10 program. The complete genome of LMV-Muju (GenBank accession no. KF955619) has 10,080 nt in length, including non-translated sequences at both termini, as for previously characterized isolates (Fig. 2 ). Phylogenetic analysis was conducted using CLC Main Workbench 6. 1. 1 program. LMV-Muju was classified based on nucleotide sequence divergence of the NIb-CP junction between positions 8936 and 9151, and it was sorted into LMV-Common belonging to LMV-RoW (Fig. 2, 3 ). This result suggested that LMV-Muju may be seedborne and a non-resistance-breaking isolate.
Acknowledgments This study was supported by a grant (Project No. 609002-5 ) from the  Screening Center for Disease Resistant Vegetable Crops of Technology Development Program for Agriculture and Forestry, Ministry for Food, Agriculture, Forestry and Fisheries, Republic of Korea , and carried out with the support of " Cooperative Research Program for Agricultural Science & Technology Development  (Project No. PJ00912703 )",  Rural Development Administration, Republic of Korea.
Statistical analysis All data were coded and analyzed using the Statistical Package for Social Sciences (SPSS) version 20 (SPSS Inc. , Chicago, IL, USA). All tests were two-tailed. According to the Bayesian interpretations, the chance of replication in future studies is low for p values between 0. 05 and 0. 01, moderate for p values between 0. 01 and 0. 001, and high for p < 0. 001  [4]. First, descriptive statistics (means, standard deviations, and frequency tables) were calculated for the items and subscales proposed by Costa and McCrae  [5]. Second, with the aim of studying the structure of the NEO-PI-3, a confirmatory factorial analysis (CFA), was conducted at the facets level (see below); a targeted rotation of principal components was also evaluated using congruence coefficients with the American normative sample.
Confirmatory factorial analysis CFA was carried out with the lavaan package  [9]  running in R  [10]. The lavaan package has been shown to generate the same results as other software packages  [11]. Mardia's kurtosis was used to check for multivariate non-normality: Mardia's kurtosis = 1,194, z = 30. 78, p <. 0001. Maximum likelihood estimation with robust standard errors and the Satorra-Bentler scaled test statistic were used to test CFA models; this method was chosen because it was unlikely to be affected by deviation from normality in data  [12]. Chi square is the traditional fit index used to evaluate an overall model as it assesses the magnitude of discrepancy between the sample and the fitted covariance matrices  [13]. However, the use of the chi square test to assess this model fit was found unsatisfactory for a number of reasons  [14] , including its sensitivity to sample size.
As a matter of fact, it has been found that increasing the measure's complexity to comply with the CFA standard led to a reduced convergent and discriminant validity  [17]. When CFA failed to reach fit, the orthogonal Procrustes rotation was proposed as a method to test the replicability of the NEO-PI-3 personality factors  [18] [19] [20]. A dedicated script running in SPSS of the program that performs the Table  3  Mean values for the domains and the facets of the Greek NEO-PI-3 According to a shared convention, factor loadings higher than 0. 71 (accounting for 50% of variance or more) are considered excellent, 0. 63 (40%) very good, 0. 55 (30%) good values around 0. 45 (20%) fair, and values below 0. 32 (10% of variance) poor  [21]. Congruence between potentially homologous factors across samples was evaluated using the coefficient of congruence (CC).
Rhea (http://www. ebi. ac. uk/rhea) is a comprehensive and non-redundant resource of expert-curated biochemical reactions described using species from the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Rhea has been designed for the functional annotation of enzymes and the description of genome-scale metabolic networks, providing stoichiometrically balanced enzyme-catalyzed reactions (covering the IUBMB Enzyme Nomenclature list and additional reactions), transport reactions and spontaneously occurring reactions. Rhea reactions are extensively curated with links to source literature and are mapped to other publicly available enzyme and pathway databases such as Reactome, BioCyc, KEGG and UniPathway, through manual curation and computational methods. Here we describe developments in Rhea since our last report in the 2012 database issue of Nucleic Acids Research.
SUBMISSIONS TO RHEA Rhea welcomes submissions describing new reactions or suggesting updates to existing reactions. All submissions should be posted on our SourceForge Reaction Requests/Updates tracker (http://sourceforge. net/p/rheaebi/reaction-requests-updates) with relevant information (name, 2D structure. ) for each reaction participant and cross-references to other relevant databases and source literature where available. Reactions requested for a publication under review are assigned preliminary status during the peer-review process and acquire approved status once the manuscript has been accepted.
RHEA AVAILABILITY The Rhea web server (http://www. ebi. ac. uk/rhea) provides programmatic access as well as browsing, searching and download facilities. Details of common search options -including compound names, compound and reaction identifiers, reaction equations, EC numbers, UniProtKB/Swiss-Prot accession numbers, bibliographic citations and identifiers from external cross-referenced resources such as KEGG  (18) , EcoCyc  (19) , MetaCyc  (20)  Rhea RESTful web services allow reactions to be retrieved in BioPAX level 2  (22) , CMLReact (cmlreact)  (24)  or RXN CTfile (rxn)  (23)  formats by querying for their Nucleic Acids Research, 2015, Vol. 43, Database issue D463 identifier or other terms. Example queries are provided in the online documentation. Rhea also provides a BioJS component (BioJS.
FUTURE DIRECTIONS We are actively developing Rhea as a vocabulary for the functional annotation of enzymes in UniProtKB. This annotation is currently provided using the enzyme classification (EC numbers) of the Enzyme Nomenclature committee of the IUBMB and textual reaction descriptions sourced from the ENZYME database (25) (itself derived from In-tEnz). Our current work involves the translation of all outstanding IUBMB reactions (the majority of which involve generic compounds or polymers) into Rhea. We are also expanding Rhea to cover the hundreds of enzyme activities that are not yet described by the IUBMB classification (26-28), many of which already have textual reaction descriptions annotated in UniProtKB (one example being the aforementioned GDP-␣-D-mannose hydrolysis reaction RHEA:28105 described in UniProtKB/Swiss-Prot record P32056).
ACKNOWLEDGEMENTS The authors are grateful for the continuing contributions from many members of the scientific community to the continuing success of Rhea. In particular, we would like to thank  Marcus Ennis ,  Gareth Owen ,  Steve Turner  and  Janna Hastings  of the Cheminformatics and  Metabolism Team  at the  European Bioinformatics Institute (CM-EBI)  for many fruitful exchanges and expert curation of the ChEBI entries essential for Rhea. We also thank  Pablo Conesa Mingo  and  Kenneth Haug  from  CM-EBI  for their IT and database support as well as  Marco Pagni  of the Vital-IT group of  SIB Swiss Institute of Bioinformatics  for fruitful discussions and comments. We also gratefully acknowledge the software contributions of ChemAxon [https://www. chemaxon. com/products/marvin/].
g. Hou et al. , 2012)  it is still challenging to formulate an efficient and, at the same time, numerically stable Fluid-Structure Interaction (FSI) scheme. Another challenging aspect of the implementation is related to an energy conserving grid-to-grid mapping between the structural solver and the flow solver. The present work aims for extending the capabilities of OpenFOAM (an open source CFD software package,  Weller et al. , 1998)  to simulate springing and slamming-induced whipping responses on large vessels moving in waves in any heading. A theoretical description of the code is provided which covers: -The VOF-based free surface flow solver formulated in an ALE (arbitrary Lagrangian-Eulerian) frame -The flexible body motion solver where the local deformation is approximated by a modal superposition of dry mode shapes and the rigid body motion is solved for such that nonlinearity in the rigid body motion and its coupling with the local deformation is fully preserved -A partitioned FSI (fluid-structure interaction) scheme with the Aitken's acceleration  (Irons and Tuck, 1969)  for a strongly couple FSI solution -The transferring of the displacement and fluid forces between the flow and motion solver which in the present formulation allows the force distribution from the fluid solver to be transferred in the modal spaces; thus eliminating the need for a grid-togrid mapping of the force distribution.
Transferring the force distribution The equations of motion need the force distribution f from the flow solver. Farrell and Maddison (2011)  provide a very efficient grid-to-grid transfer based on the local Galerkin projection method. An implementation of the algorithm is provided by the OpenFOAM software package and has been considered in the present work. A grid-to-grid transfer must satisfy the dynamic continuity on the FSI interface presented in Eq. (  29 ) and the algorithm presented by Farrell and Maddison 2011 works well for the case where the source and target surfaces are fully overlapping. In the partial overlapping case, the implemented algorithm loses its energy conserving property. This can be easily confirmed by comparing the total force and total moment evaluated on the fluid mesh and the structural mesh.
(  30 ) is a non-linear least-square fitting problem. The solution provides not only the modal coefficients f q but also the position of the local coordinate R and the corresponding transformation matrix A. The estimated initial displacements are shown in Fig. 2. A FEM model of the flexible barge consisted of beam and shells elements is created in the commercial FEM software ANSYS and a modal analysis of the FEM model is performed with a lumped mass approximation. Fig. 3  shows the 2-node and 3-node modes where it is observed that the pontoons are discretized with shell elements and connected rigidly to the steel plates without any contact between them. The gaps between the pontoons (15 mm) are limiting the allowable local deflection that can be transferred to the CFD mesh. In total, 9 flexural dry modes are selected to describe the local deformation of the barge.
ACKNOWLEDGEMENTS The presented work is funded by a join research project between  Technical University of Denmark (DTU) ,  Bureau Veritas (BV)  and  Nippon Kaiji Kyokai (ClassNK). The third author acknowledges the support of the  National Research Foundation of Korea (NRF)  grant funded by the  Korea Government (MEST)  through  GCRC-SOP  (Grant No. 2011-0030013 ).
Notably, two transcripts have notably been identified, driven by two alternative exons 1 in the 5Ј-untranslated region (UTR)  (6) , which are responsible for differential transcription and subsequent cotranslational and post-translational modifications  (12) (13) (14). TNSALP is a homodimeric enzyme where each monomer is composed of 524 amino acids (57. 2 kDa)  (6, 12). TNSALP links to the cell membrane and functions as an ectophosphatase to hydrolyze monophosphated esters including inorganic pyrophosphate (PP i ), phosphoethanolamine, and pyridoxal 5Ј-phosphate  (15). Various domains were identified in TNSALP: (i) the active site, which includes a catalytic site and three ligands for metal ions (two zinc and one magnesium sites), and (ii) several mammalian-specific regions, i. e. the long N-terminal ␣-helix domain responsible for catalysis  (16) , the non-catalytic Ca 2ϩ -binding domain, and the crown domain  (17).
The increasing number of ALPL sequences found in public databases (NCBI and Ensembl) coming from genome and/or transcriptome sequencing in numerous vertebrate species enabled us to develop a new method for predicting disease mutations based on the evolutionary molecular analysis of protein genes  (24, 25). This method involves comparing a large number of primary sequences of a given protein arranged into a phylogenetic context (i. e. based on species relationships) with the aim of identifying sensitive positions. Such evolutionary analysis allows us to (i) understand how the protein and its various regions evolved; (ii) identify regions, motifs, and residues that play important functions because they were conserved during long geological periods (hundreds of millions years: Ma); and (iii) predict and/or validate point mutations that could lead to a genetic disease, and ultimately estimate their degree of severity.
ALPL Sequences and Alignment A total of 58 ALPL sequences, representative of the main mammalian lineages, were extracted from NCBI and Ensembl databases. They comprised nine published, full-length cDNA sequences, 14 computer-predicted sequences available from sequenced mammalian genomes, and 35 sequences obtained using Basic Local Alignment Search Tool (BLAST) in genomes being currently sequenced (Table  1 ). The unpublished sequences were validated through alignment to the published cDNA sequences using Se-Al v. 2. 0a11 software  (32). The intronexon boundaries were carefully checked. When necessary, the sequences were completed and/or corrected either using Blast search in the Ensembl genome database, in the NCBI trace archives, and in the whole genome shotgun repository sequences, or using de novo sequencing using PCR on genomic DNA extracted from ethanol-preserved tissues (see Table  2  for primers used).
Evolutionary Analyses Substitution Models-The substitution models for both the nucleotide and amino acid alignments were defined using an online model selection tool datamonkey  (33). Briefly, for each alignment, the software defines the best model by testing all possible evolutionary models. Here, the two best substitution models were (i) the Tamura-Nei model  (34)  for the nucleotide alignment; and (ii) the Jones et al. model  (35)  for amino acid alignment. Distance Tree-This approach is useful for the evolutionary analysis as it highlights variable sequences related to either large evolutionary distances or high substitution rates with regard to amino acid conservation through mammalian evolution. The 58 aligned mammalian ALPL sequences were computed using Molecular Evolutionary Genetics Analysis 5.
2. 1 (MEGA5)  (36). The alignment was restricted to the length of the human ALPL sequence by excluding the 12-nucleotide insertions in non-human sequences and the stop codon. This procedure resulted in a dataset containing 1572 nucleotide sites  (524 amino acids). This alignment was supplemented by a mammalian tree taking into account the recent mammalian phylogeny  (37). Maximum likelihood phylogenetic reconstruction was performed from the nucleotide alignment using the HYpothesis testing using PHYlogenies (HYPHY) software  (38). For each nucleotide, the probability for the observed data were calculated using the maximum likelihood method and taking into account the supplied tree. Analysis of Functional Constraints-As previously described (29 -31), the strong constraints corresponding to structural/ functional regions were searched on the nucleotide and amino acid alignments by Sliding Windows analysis in the HYPHY software and by SLAC analysis (datamonkey)  (39, 40).
Sequence Comparison of Mammalian TNSALP With a total of 58 species from 22 orders, our dataset is well representative of the current mammalian TNSALP diversity and it covers circa 220 Ma of evolution (Table  1 ). The length of the coding ALPL sequence, from ATG to the stop codon, was found identical in 53 species: 1575 bp resulting in 524 amino acids from the N-terminal methionine, Met 1 , found in the region encoded by exon 2, to the phenylalanine preceding the stop codon, Phe 524 , encoded by exon 12 (supplemental Table  S1 ). Short variations were only observed in five TNSALP. In the region encoded by exon 2, one or two amino acids were deleted before the cleavage site of the signal peptide in Marsupiala (wallaby, Tasmanian devil, and opossum); encoded by exon 3, two deletions and one insertion are found in an Afrotheria (hyrax); encoded by exon 11, one deletion in a Chiroptera (megabat); and encoded by exon 12, one deletion in opossum and wallaby, and three insertions in Marsupiala.
Validation of ALPL Mutations in Humans Our Predictive Evolutionary Chart-So far, of the 204 missense mutations listed in the ALPL mutation database, 145 positions only are affected as more than one amino acid substitution can occur in a same position (supplemental Table  S2 ). These mutated positions were indicated on the human TNSALP sequence and compared with the sensitive positions identified in our evolutionary analysis (Fig. 3 ). Among the 145 positions affected in humans, 113 concerned unchanged positions and 31 conservative positions were identified as sensitive positions in our evolutionary analysis, and one variable position. All human mutations on a conserved position and 8 on a conservative position were directly validated by our evolutionary analysis, which represents 82. 8% of the affected positions in humans.
3% of the deleterious missense mutations observed in human TNSALP. Eventually, only one missense mutation (0. 7%) did not fit with our evolutionary analysis. This mutation was reported on a conservative position (p. A177T), but was not predicted as deleterious because the same substitution occurred in the wild (supplemental Table  S2 ). Other in Silico Prediction Tools-To date, several in silico models dedicated for predicting the effects of amino acid substitutions in humans are available and easy to access (for review, see Ref. 42 ). We list the results obtained with TNSALP using five softwares (see Table  4  and supplemental Table  S3 ). To predict the effect of the substitutions, there are three categories of tools: the sequence and evolutionary conservation-based methods, such as SIFT or Mutation Assessor, which base their prediction on sequence conservation using a multiple sequence alignment but this approach is dependent to the multiple sequence alignment provided; the protein sequence and structure-based methods, such as PolyPhen2 or MAPP, which combine amino acid properties, location of functional site, secondary structure, and membrane topology; the supervised-learning methods like SNAP, which include for their algorithms neural networks, support vector machines and random forests, and naive Bayes classifiers.
The predicted mutations are considered as true positives, the nonpredicted mutations as false negatives, and the false predictions as false positives. PolyPhen-2 is software based on both sequence and structural information, and classifying mutations as deleterious or benign. This tool provided a correct prediction, for 93. 6% of the missense mutations known in ALPL, but erroneously considered 47 substituted positions, i. e. 9% of the 524 residues, with amino acids found in the nature as damaging the protein function. MAPP (Multivariate Analysis of Protein Polymorphism) is software combining both evolution and physicochemical characteristics like hydropathy, polarity, charge, volume, free energy in ␣-helix, and ␤-strand conformations. This powerful software detects "good" and "bad" amino acids for each position.
All known mutations of TNSALP were found except K264R and R450H. Nevertheless, MAPP also detected as bad 139 substituted positions (26. 5%) with residues found in nature. Mutation Assessor is designed to predict the functional impact of amino acid substitutions, through evolutionary conservation in the protein family and subfamily. This software provided a correct prediction for 82. 8% of TNSALP missense mutations. A total of 109 moderate phenotypes were predicted by the software, but 10 of the 24 moderate phenotypes reported in the literature were not validated. Similarly, Mutation Assessor predicted 59 positions as leading to a severe phenotype when substituted, but did not validate seven of the severe phenotypes known in the literature. Also, Mutation Assessor wrongly detected 48 substituted positions (9.
1%) with residues found in nature as leading to diseases. SIFT (Sorting Intolerant From Tolerant) identifies deleterious mutations using only an evolutionary approach. This method is based on the degree of conservation of amino acids in sequence alignments derived from closely related sequences obtained by PSI-BLAST. This software did not detect 50% of     S2 ). the mutations known in the literature and erroneously predicted 30 amino acids (5. 7%) as nontolerated although they are found in nature (e. g. K27W, N53K,. ). SNAP (Screening for Non-acceptable Polymorphisms) is a neural network using derived protein information as, for instance, secondary structure, evolutionary conservation, and solvent accessibility to predict the effect of mutations on the protein. This tool validated 69% of the missense mutations reported in the literature for TNSALP, but performed false prediction for 18 positions (3.
4 ). 126 GTVGVSAA 133 (Fig. 4A ) and 144 GNEVTSILRWA 154 (Fig. 4C ) are both encoded by exon 5 (chain X), but in the first motif, the first three amino acids are located in the 12-Å sphere around the phosphate group  (49). The third motif, 348 ALHEAVEMDRAI 359 (Fig. 4B ), is encoded by exon 10 (chain Y) and contains an acidic cluster, giving a pI of 4. 65, more acidic than the collagen binding site. These three sequences are not referenced in the peptide database. Interestingly the three-dimensional model shows that these three motifs are at the surface of the molecule and very exposed, A and C on the front side and close enough to each other to form a unique domain, and B on the rear side (Fig. 4 ). A and C motifs are also very close to the N-terminal ␣ helix of the other monomer and probably contributes to the homodimer interface, whereas the role of motif B remains to be elucidated.