dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2011  for a reuse in food packaging). Let us point out that the OTR satisfies functional specifications 2 of the IC2ACV DSS presented in Section 2. @Web is composed of two sub systems (see Fig. 4 ). The first one is an annotation sub system for the acquisition and annotation, with concepts of the OTR, of experimental data extracted from sci entific documents; those annotated data are being stored into a database. This sub system also allows the reliability of data sources to be assessed using the approach of  (Destercke et al. , 2013). The second sub system is a flexible querying system based on the approach presented in  (Destercke et al. , 2011). @Web is implemented using the semantic web standards (XML 7 , RDF, OWL, SPARQL): the OTR is defined in OWL2 DL, annotated tables in XML/RDF and the querying in SPARQL.
In practice, the groups are made so that their impact on reliabil ity can be estimated independently, while a group � may contain multiple criteria (e. g. number of citations and publication date). For each possible value of each group, the method then consists in assessing the reliability of a document or data having this partie ular value. After the groups have been formed, for each value ay, i 1,. , S,j 1,. , C;, an expert of the field from whieh data are collected gives his/her opinion on about how reliable are the data whose meta information is ay. This opinion is expressed lin guistically, chosen from a set of limited modalities ( or combina tions of them), e. g. ve,y unreliable, slightly unreliable, neutral slightly reliable, ve,y reliable and unknown. The number of modali ties is limited (usually 5 or 7), accounting for known limitations of human cognitive abilities  (Miller, 1956).
Eco design indicators (steps 2 and 3) As seen in the previous section, the first step of the pipeline pre sented in Fig. 3  allows knowledge extracted from heterogeneous data sources to be annotated and its reliability to be assessed. The second step consists in extracting annotated data from the RDF database to compute environmental impact indicators. Fol lowing functional specification 7 listed in Section 2, we now pre sent the computation of mass balance indicators denoted Efactors. The Efactor indicator can be seen as the total input quan tity of matter not valorised into glucose but required to produce 1 kg of glucose. This indicator is often used in survey papers dedi cated to biorefinery processes comparison  (Zhu and Pan, 2010; Barakat et al. , 2014; Chuetor et al. , 2015). Ali kinds of matters whieh are inputs of the process are taken into account (by exam pie, the biomass, water, chemieal reagents.
@Web may also assist the expert in the symbolic concepts entry task, by allowing him/her to navigate in the hierarchy of symbolic concepts belonging to the Biorefinery ITTR. For instance, in Fig. 13 , the expert may navi gate into the Biomass concept hierarchy from the Biorefinery ITTR and see the labels of the selected concept in the upper right corner of the snapshot. In the fifth and last step of annotation. called St orage, the anno tated data tables are stored in a RDF triple store which can be quer ied through either an end user querying interface or a SPARQL end point for open data access. The data annotated by the annotation sub system of@Web may be queried through an end user interface, whieh implements func tional specification 3 presented in Section 3. A detailed presenta tion of the flexible bipolar querying method whieh has been used to implement the querying sub system of @Web is given in  (Destercke et al.
, 2011 (Destercke et al. , , 2013)). It should be notieed that this query ing method simultaneously performs three kinds of reasoning: (1) inference using specialization relation defined in the Biorefinery OTR, (2) ranking according to fuzzy pattern matching between preferences expressed in the query and imprecise data, (3) ranking according to preferences expressed about data source reliability. In this paper, we present the implementation of the querying sub system through an example whieh illustrates the way data needed for Efactor indicator computation are extracted from the RDF triple store. For instance, the query presented in Fig. 14  has been built in order to compute the Efactor indicator assoàated with pre treatment processes of type Organosolv pre treatment for riee straw. First, the user selects the ontology IC2ACV which is the name associated with the Biorefinery OTR in @Web.
In this article, we have considered four tapies, each of them associated with one of the four pre treatment processes presented in Fig. 2. We have seen in the previous section that we use @Web queries to extract csv data files in order to compute the Efactor associated with a given tapie. We have implemented the computa tion of the three Efactor indicators in an Excel file in whieh have been previously stored data extracted from @Web. Graphical rep resentations are generated in VBA programming language executed in an Excel file to display an X Y plot for a given tapie and a given biomass where X corresponds to Efactor and Y to glu cose yield. For instance, in Fig. 16 , we show a ranking of pre treatments based on Efactor computation for the best experiment of considered documents. Each point corresponds to a given pre treatment of rice straw presented in a given document.
Conclusion and prospect ln this paper we have proposed a decision support system for eco efficient biorefinery process selection based on an ontology based semantic approach. The ontology is used to guide the anno tation of potentially incomplete or imprecise experimental data retrieved from the bibliography in order to store them in a struc tured database. Moreover a model has been used to assess the reli ability of data sources. Finally, a ranking of biorefinery processes has been computed in terms of glucose yield and Efactor indicators taking into account data imprecision and reliability. The interest of Efactors is to give an overview of the process mass balance. lt may be considered as a "local" indicator (because it is based on data at process scale) and "inventory level" indicator (because it uses physical tlows ).
lt completes the classical process yield in order to rank different options. The less input consuming and waste generating process is generally preferred. Used with the reliability indicators, Efactor gives interesting information at an early deàsion stage at research or laboratory scale. Other indi cators could be considered regarding the environmental balance. Therefore, a perspective of our work will be to compute life cycle indicators to complete the list of assessment criteria for decision makers. The application of life cycle assessment (LCA) according to the ISO 14040 standards  (ISO, 2006)  would deepen the analysis through the system boundaries extension (inclusion of inputs life cycle with the use of life cycle inventory databases) and through the potential environmental impacts calculation.
vivax. Thus, SWGA precludes the need for in vivo amplification in non-natural host species, which can result in sequence loss or other artefactual adaptations  29. SWGA also obviates the need for large blood draws, can be used in resource poor settings, and allows to characterize parasite genomes from dried blood spots. Finally, SWGA can be adapted to other microbes and/or host species, as long as appropriate reference genomes are available for all sample constituents. For example, we have recently amplified Plasmodium parasites from infected mosquito DNA  30  , identifying vectors as a potentially valuable source of pathogen genomes. To increase the utility and versatility of SWGA, we are developing an automated pipeline that permits the design, selection and in silico evaluation of SWGA primers for different pathogen and host combinations (https://github.
These specimens were collected from nonhabituated apes living in remote forest areas, with a two-letter-code indicating their field site of origin as previously reported  7, 59. DNA was extracted from whole blood and RBCs using the QIAmp Blood DNA Mini Kit, the Puregene Core Blood Kit (Qiagen), or the NucliSENS miniMag extraction kit (Biomerieux). Sample collection was approved by the Ministry of Environment and Forestry in Cameroon, and by the Ministry of Forest Economy and Sustainable Development in the Republic of Congo, respectively. All samples were shipped in compliance with Convention on International Trade in Endangered Species of Wild Fauna and Flora regulations and country-specific import and export permits. Laverania species identification. The Laverania species composition of ape blood and faecal samples was determined by limiting dilution PCR (also termed single-genome amplification), followed by phylogenetic analysis, essentially as described  60.
For each sample, multiple single-templatederived amplicons were sequenced and their species origin identified by phylogenetic analysis (see GenBank accession codes in Supplementary Table  8 ). This analysis permitted the identification of samples that represented single (or near single) Laverania species infections for SWGA (Supplementary Table  1 ). Laverania-specific real-time PCR. To determine the amount of Laverania DNA within a blood or faecal sample, DNA was subjected to quantitative (q)PCR using a 7900HT Fast Real-Time PCR System and the Power SYBR Green qPCR kit (Life Technologies). Laverania-specific forward (5 0 -ACATGCCACATGGAAAAG CTT-3 0 ) and reverse (5 0 -CTGGGGCCTTGGTAAATCCA-3 0 ) primers were used to amplify a 144 bp fragment of the nuclear ldh gene. PCR cycling conditions included 2 min at 50 °C, 10 min at 95 °C and 40 cycles of 15 s at 95 °C and 1 min at 60 °C.
A primer design pipeline that is applicable to the genomes of other organisms is under development (https:// github. com/eclarke/swga). SWGA protocol and validation of primer sets. SWGA was performed essentially as described  19  , following established phi29 amplification protocols, but using primers designed to selectively amplify Laverania genomes (Fig. 1 ). Amplification conditions included a 1 h ramp-down step (35 °C to 30 °C), followed by a 16 h amplification step at 30 °C. Phi29 was then denatured for 10 min at 65 °C, and the SWGA product was stored at 4 °C. To validate the SWGA primers, genomic DNA extracted from cultured P. falciparum parasites (a subclone of NF54 generated by Kirk Deitsch, Weill Cornell Medical College, which is isogenic with 3D7) and human CD4 T cells (obtained from the Human Immunology Core of the University of Pennsylvania Center for AIDS Research) were mixed to generate human DNA preparations containing 5, 1, 0.
gaboni reads to the much more divergent P. falciparum genome (Supplementary Table  1 ). Illumina sequencing runs and accession codes are listed in Supplementary Table  9. Assembly of P. gaboni and P. reichenowi draft genomes. Draft genomes were generated for the P. reichenowi strain PrSY57 and the P. gaboni strain PgSY75 using reference-guided de novo assembly with post-assembly genome improvements  12, 21. First, working drafts of the PrSY57 and PgSY75 genomes were generated by iteratively mapping (non-chimpanzee) reads to the PrCDC and Pf3D7 references, respectively, using Geneious 6 (Biomatters Limited, http:// www. geneious. com). This mapping process, which was repeated 10 times, resulted in a sequence that represented the read mapping consensus at all positions with greater than or equal to fivefold coverage.
At positions with lower coverage, the sequence of the reference (Pf3D7 or PrCDC) was used instead. All reads were then re-mapped to this consensus using two iterations. The resulting draft reference represented the mapping consensus at all positions with greater than or equal to fivefold coverage, with positions with less than fivefold coverage denoted by 'N's. Before de novo assembly, error correction was performed on short-insert libraries from each sample using String Graph Assembler (SGA 0. 10. 12, https:// github. com/jts/sga) as previously described  12. For the P. gaboni sample PgSY75, reads were also normalized using KHMER  61  , which uses k-mer frequencies to estimate and normalize genome coverage in a reference-free manner, thus facilitating subsequent de novo assembly. This process yielded 11 million reads.
1 bin. This was done by mapping all reads from PgSY75 and PrSY57 to their respective draft assembly and bin using SMALT, and then performing de novo assembly of the remaining unplaced read pairs using SPAdes 3. 1. 1 (ref. 64). SPAdes was run using the default multicellular mode parameters, except for the k-mer length (-k) that was set to 21, 33, 55 and 77. For PgSY75, the resulting contigs were corrected using iCORN, using only unmapped read pairs from the previous step and added to the unplaced bin. For PrSY57, the combined unplaced bin contigs were screened for contaminating P. gaboni sequences by performing blastn searches to a combined database of PrCDC, Pf3D7 and PgSY75 chromosomes. Contigs were only retained if their best match was to a P. reichenowi contig, exhibited Z90% identity, and had an E-value r10 À 15.
Pairwise inter-species genetic distances were calculated in R using the ape package  67  with the TN93 model of DNA evolution. Genes with unusually high or low inter-species distances were manually inspected and the respective alignments or masked regions were corrected if necessary. If the best alignment required insertion of a gap not divisible by three, the gene was excluded from intra-species diversity analyses (since these required sequence translations). Inter-species distances were calculated using all available orthologues for Pf3D7, PrCDC, PrSY57, PgSY75 and PgSY37. Intra-species diversity. For P. falciparum, intra-species diversity was calculated using previously published parasite sequence data sets of geographically diverse field isolates collected in Bangladesh, Cambodia, DRC, Gambia, Ghana, Guinea, Laos, Myanmar, Nigeria, Thailand, Kenya and Vietnam (Pf3k 1.
No Kenyan strain was selected since all available samples were likely to represent multi-strain infections. After exclusion of subtelomeric genes, alleles from polymorphic sites were extracted from variant call format (. vcf) files; sites at which three or more samples had missing data (that is, no genotype called) or where the majority genotype was represented by o80% of mapped reads, were excluded from the analysis; otherwise samples with missing data were assumed to have the reference allele. Intra-species diversity (p) was determined by calculating the mean number of differences per site for all pairwise combinations of 11 P. falciparum strains plus the 3D7 reference. Sites masked in 3D7 (see above) were excluded from intra-species diversity calculations. For P. gaboni and P. reichenowi, intra-species diversity was calculated from the alignments used for inter-species genetic distance calculations, using the ape R package to count the proportion of non-masked sites that differed between the two strains available for each species (PrCDC and PrSY57 for P.
Acknowledgements We thank  Richard Carter  and  Brian Charlesworth  for helpful discussions, the staff of project  PRESICA  for fieldwork in Cameroon; the staff of the  Sanaga Yong Rescue Center  and the  Tchimpounga Chimpanzee Rehabilitation Center  for providing leftover blood samples from captive chimpanzees; the  Cameroonian Ministries of Health, Forestry and Wildlife, and Scientific Research and Innovation for permission  to perform studies in Cameroon; the  Ministry of Forest Economy  and  Sustainable Development  for permission to collect samples in the  Republic of Congo. This work was supported by grants from the  National Institutes of Health  ( R01 AI097137 ,  R01 AI091595 ,  R37 AI050529 ,  T32 AI007532 ,  P30 AI045008 ), the  Burroughs Wellcome Fund  ( 1012376 ), the  Agence Nationale de Recherche sur le Sida  ( ANRS 12125/12182/12255 ), the  Agence Nationale de Recherche  ( Programme Blanc ,  Sciences de la Vie ,  de la Santeet des Ecosystemes  and  ANR 11 BSV3 021 01 , Project  PRIMAL ) and the  Wellcome Trust  ( 098051 ).
2003; Pollard et al. 2006; Knowles 2009; Som 2015). Therefore, multiple gene data should ideally be first analysed separately to look for congruent genealogies and then possibly concatenated to derive a single phylogenetic tree. In practice, however, such an approach may not be trivial to implement. Indeed, handling and analysing multiple gene alignments can be a daunting task. While the R software is growing as a platform of choice for phylogenetic analyses  (Paradis et al. 2004; Jombart et al. 2010a; Kembel et al. 2010; Schliep 2011; Popescu et al. 2012) , tools for handling and analysing multiple gene data are missing. In this study, we introduce apex, a new R package which fills this gap. apex implements new object classes for storing and handling multiple genes data. This package is fully integrated and compatible with existing R standards for phylogenetic reconstruction and makes the analysis of multiple genes as straightforward as for single genes.
New object classes The main feature of apex is to provide classes for storing and handling multiple gene data. This is implemented through two novel formal (S4) classes, multidna and multiphyDat, which are respectively extensions of the DNAbin objects from the package ape  (Paradis et al. 2004; Popescu et al. 2012) , optimized for distance-based methods, and phyDat objects from the package phangorn  (Schliep 2011) , better suited for parsimony and likelihood-based approaches. As in any formal class, the structure of multidna and multiphyDat is defined by a collection of predefined 'slots', each containing specific information such as the number of individuals in the data set, the various gene sequences and additional meta-information. For the sake of simplicity, both classes have identical slots (Tables  1  and 2 ) and only differ in the way genetic sequences are stored internally: multidna uses bytes to code nucleotides (DNAbin objects, Table  1 ), while multiphyDat enumerates variable patterns in the sequences and can be used for DNA as well as amino acid sequences (phyDat objects, Table  2 ).
The functions 'read. multidna' and 'read. multiFASTA' build upon ape's procedures to read data in interleaved, sequential, Clustal or FASTA format. In addition, the function 'read. multiphyDat' enables imports of amino acid sequences with interleaved, sequential or FASTA format. Note that in this case, the resulting multiphyDat object can no longer be converted to multidna, which is restricted to nucleotide sequences only. Once imported in apex, data can also be converted to various formats (Table  3 ). Conversion from multidna to multiphyDat is implemented by mul-tidna2multiphyDat, while the reverse operation is performed by multiphyDat2multidna. Single-nucleotide polymorphisms (SNPs) can be extracted from the alignments and translated into a genind object, thereby providing access to a wide range of multivariate analyses  (Jombart et al.
Discussion Phylogenetic analysis of multiple gene data can be a daunting task, requiring substantial prior data processing (e. g. comparing sequenced taxa across different loci, handling missing data) and replication and comparison of the analyses over all loci. So far, this could only be achieved in R using multiple ad hoc scripts (e. g. Bryson et al. 2013; Harris et al. 2013; Grummer   et al. 2015), thereby limiting the reproducibility and reliability of the analyses, and discouraging the investigation of potential phylogenetic incongruence  (Som 2015). The aim of apex is to streamline this process and make phylogenetic reconstruction with multiple genes as straightforward as with a single locus. To achieve this goal, apex implements new object classes which extend existing standards for DNA and amino acid sequences.
It provides simple functions for importing multiple alignments data from classical formats and software, seamlessly taking care of re-ordering and matching taxa across loci, identifying missing sequences, and optionally adding gap-only sequences to ensure matching of taxa for all loci. The resulting objects can be handled easily using various accessor functions, procedures for subsetting data by taxa, locus or site, and for concatenating selected loci. Additionally, apex implements a series of wrappers for the visualization and analysis of multiple gene data and implements a generalization of sequence-type polymorphism  (Maiden et al. 1998; Aanensen & Spratt 2005; Maiden 2006 ) directly compatible with standard population genetics methods  (Goudet 2005; Jombart 2008; Paradis 2010; Jombart & Ahmed 2011).
Acknowledgements apex was first created at the  Population Genetics in R Hackathon , which was held in March 2015 at the  National Evolutionary Synthesis Center (NESCent) in Durham, NC , with the goal of addressing interoperability, scalability and workflow building challenges for the population genetics package ecosystem in R. The authors were participants in the hackathon and are indebted to NESCent (NSF # EF-0905606 ) for hosting and supporting the event. TJ is funded by the  Medical Research Council Centre for Outbreak Analysis  and the  National Institute for Health Research -Health Protection Research Unit (NIHR HPRU ) in Modelling Methodology at  Imperial College London  in partnership with Public Health England (PHE). KS was supported in part by a grant from the  National Science Foundation  ( DEB 1350474 ).
ZK is supported by  United States Department of Agriculture (USDA) Agricultural Research Service (ARS)  grant  5358-22000-039-00D ,  USDA Animal and Plant Health Inspection Service , the  USDA-ARS Floriculture Nursery Initiative , the  Oregon Department of Agriculture/Oregon Association of Nurseries (ODA-OAN)  and the  USDA Forest Service Forest Health Monitoring Program. This is publication ISEM  2016-131. We are thankful to Github (http://github. com/),Travis (http://travisci. org/) and codecov (http://codecov. io)for providing great resources for software development. The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR , the  Department of Health or Public Health England.
Facts Extraction During this step, we construct from source code and additional artifacts (e. g. XML Configuration files) the source code model in accordance with the metamodel established in figure  2. We constructed this metamodel according to a recent survey conducted by  Vale et al. (Vale et al. , 2016). In this surveys, it is stated that components are often considered as sets of classes and interfaces are those classes which have a link with some classes from the outside of the component (e. g. a method call or attribute use from the outside). In order to perform the extraction of these information, we have designed and implemented an extraction engine that rely on the API provided by Eclipse JDT 1. The extraction engine depends on the component model, the underlying programming language and additional component-model specific resources like XML and Manifest configuration files.
3 software (Hologic, Bedford, MA, USA), following the manufacturer's recommendations and analyzed by one expert technician. We used the HSA software to analyze strength and geometrical properties of the hip  [30]. This program uses the distribution of mineral mass in a line of pixels across the bone to measure geometric properties of cross-sections in cut planes traversing the bone at that location  [31]. Three regions were analyzed: (i) narrow neck (NN), across the narrowest diameter of the femoral neck; (ii) intertrochanteric (IT), along the bisector of the neck-shaft angle; and (iii) femoral shaft (FS), 2 cm distal to the midpoint of the lesser trochanter (Fig  2 ). For each region, the distribution of the bone mass across the bone was extracted and geometric properties were derived using diverse formulas described elsewhere  [31].