dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , large homozygous regions, missing values, variation in recombination intensity among parents) that usually have a major negative impact on map quality. This included the use of multiple full-sib families (21), reduction in the amount of missing data through a bin-mapping strategy, and the transformation of the data from a cross pollination design to a back cross design, as outlined by Di Pierro et al. 20. The dedicated software HapAg was developed in support to this approach, which has been made public available at http://www. wageningenur. nl/en/show/ HaploblockAggregator. htm. Next, this map was used in FruitBreedomics for PBA based QTL discovery studies and in further works to improve the apple reference genome sequence  21  as a reference for the completion of other genetic linkage maps in apple  38, 47.
The progenies were genotyped with the 9K IPSC array. After filtering the SNPs for quality as described in the ASSIsT pipeline, polymorphic SNPs were grouped in haploblocks of 1 cM. Phenotypic (including raw and standardized data) and genotypic data were analysed with PBA software. We identified 47 QTLs, 22 coinciding with major genes and QTLs found in previous analyses and 25 new. Nearly half of the QTLs (47%) were detected only when analysing non-commercial progenies. This strategy also provided estimations on the narrow sense heritability of each trait and the estimation of the QTL genotype and breeding value of each parent  43, 50. In apple a series of fruit quality traits have been studied on 25 mapping populations from seven breeding programs from six European countries for which the phenotypic data were collected as part of the previous EU-HiDRAS  51  and EU-ISAFRUIT and some national projects.
Over 3200 crosses and over 3000 ancestors were examined for their founder composition. Usually, programs build on a genetically narrow framework of a few founders. However, overall a major part of the genetic make-up of modern germplasm is of highly diverse origin coming from up to 90 founders within a single breeding program. The data document the huge efforts of breeders to include new traits from very distinct genetic resources, including wild species. The results will be useful to breeders and policymakers in the EU and worldwide to define an agenda on breeding strategies and research priorities related to genetic diversity.
Genome wide studies on the structure of the genetic diversity in apple and peach Considering that a large number of apple and peach accessions had been maintained and evaluated for several traits and years in European repositories, we first surveyed and made available a list of the accessions existing within a number of key European genbanks (5477 apples and 2885 peaches), together with a short description of the traits and phenotypic records available, with their respective protocols. Common protocols and descriptors were also defined for both apple and peach for further phenotypic assessments. Afterwards subsets of accessions were selected for each collection (1560 apples and 1296 peaches) and their phenotypic records transferred to the FruitBreedomics database following the scales agreed.
The peach collections considered were those maintained by the University of Milano, CRA (Italy), INRA-Avignon (France) and IRTA (Spain) plus a valuable Chinese collection representing a large portion of peach variability absent in the European germplasm collections (Zhejing University, China). The apple collections were those maintained by CRA-W-Gembloux (Belgium), INRA-Angers (France), RBIP-Holovouzy (Czech Republic), SLU-Balsgard (Sweden), University of Bologna (Italy), and NFC-Brogdale (UK). Phenotypic records for phenology, fruit quality, tree architecture, and some disease resistance data were uploaded to the FruitBreedomics database. Thereafter subsets of accessions were selected as representatives of the collections based on curators' knowledge and/or available genotypic (SSR) data.
This subset of accessions was phenotyped for key selected traits during at least two additional seasons in their respective location. Genotypic diversity was evaluated in both species. In several apple collections, a large fraction of the accessions had been previously genotyped with SSR markers  [55] [56] [57]. Thanks to the FruitBreedomics project, a large number of additional apple accessions from various European germplasm collections were genotyped for diversity analysis with 16 common SSRs. Altogether, >2400 accessions were jointly analysed. The final sample included accessions from germplasm collections located in Belgium, France, Italy, United Kingdom, Czech Republic, Sweden, Switzerland, Spain, Russia and Kyrgyzstan. SSR data were used to identify synonyms and investigate the genetic structure of the whole gene pool, detecting differentiation according to the geographic origin of the accessions (West, South, North + East; Fig.
2 ). Overall, the apple genetic diversity was very high, but with a weak structure, confirming large gene flow across Europe  58. Genetic diversity of >1500 peach accessions was evaluated with the Illumina 9K peach SNP array. Data allowed the detection of synonyms, detecting 173 groups encompassing 473 cultivars with at least two identical cultivars (i. e. , >98% of their SNPs genotypes identical); consequently 300 cultivars were removed. The 1240 unique peach accessions grouped in three main populations, which correlated with breeding strategies (breeding and local landraces) and also with geographic origin (Occidental and Oriental (Asia)  59. SNP data also showed that most cultivars coming from modern breeding programs had an uneven distribution of homozygous and heterozygous regions along the genome, with large monomorphic regions followed by highly heterozygous regions.
Given the high values of the kinship found (k = 0. 58 on average), these results suggest that the monomorphic regions are identical by descent as a consequence of a high level of co-ancestry between peach cultivars. Genotypic data together with phenotypic information obtained before and during FruitBreedomics allowed the definition of core collection (CC) representative for partner's repositories as well as a first tentative European CC per species intended for future research studies. Such data have been extremely useful to identify clones and synonyms within and between collections, which will aid in a more efficient management of the apple and peach collections. These combined and co-ordinated datasets represent a significant development in the co-ordination of European genetic resource collections for both apple and peach, and will offer valuable base for continued efforts in bringing further germplasm collections together in the future.
They were uncorrelated to the genomic relatedness of each family to the training population. Significant responses to genomic selection were obtained in 47 trait-family combinations out of 97, i. e. , the top 10% and bottom 10% differed significantly. Figure  4  illustrates the dependency of accuracies to phenotypic distribution with two contrasting traits, pre-harvest dropping with highly asymmetric distributions in the three families where it was assessed, and percent over-colour with almost symmetric distributions  57. In peach, a set including >1100 seedlings (largely overlapping with those used for a parallel PBA study, see section 'PBA') was used as a basis to assess the feasibility of GWS for fruit weight, sugar content and titratable acidity. SNP data (9K IPSC array) were integrated with phenotypic data collected over 3-5 years in Italy, France, and Spain.
Conclusion and perspective FruitBreedomics was a very important endeavour! It was an integrative project gathering 28 institutes and companies representing 14 countries and 367 stakehoders. FruitBreedomics made significant breakthroughs in the development of tools, methodologies and knowledge for the fruit scientific community. The most significant ones are (i) the new genomic tools (20K and 487K apple SNP chips) which are the foundations of further genetic and genomic studies, (ii) softwares like ASSIsT, FlexQTL which helped identifying robust and informative molecular markers and building genetic maps, (iii) new discovery and fine mapping approaches like Pedigree-Based Analysis (PBA) and GWAS that allowed the discovery of additional molecular markers closely linked to important traits such as fruit quality and biotic and abiotic resistances to be used for genetic studies and marker assisted breeding.
The project also gave a deeper insight on the European apple and peach germplasm and breeding population structure that allows breeders to choose for new parent material to grasp new variability and introduce it into their breeding programs. FruitBreedomics also provided the breeders with a huge number of pre-breeding materials and new phenotypic methods to assess traits of interest for apple and peach, in particular methods to assess fruit texture, resistance to Monilinia in peach and drought tolerance in apple. FruitBreedomics further developed pipelines for MAB on apple and peach. Although FruitBreedomics ended in 2015 still with important results to be published, it has generated many short-term impacts for both fruit scientific and breeding communities. For example, peach resequencing data have generated a new add-on Illumina peach 18K SNP chip integrating ca 9000 SNPs in the already existing 9K IPSC chip.
Acknowledgements This work has been funded under the  EU seventh Framework Programme  by the  FruitBreedomics  project No. 265582 :  Integrated Approach  for increasing breeding efficiency in fruit tree crops (http://www. fruitbreedomics. com/). The authors would like to thank sincerely  Vincent Troillard  ( INRA Transfert ) who managed all the administrative and financial tasks of the project and all the participants.
In this study, indentation tests of graphene-based polymer nanocomposites were carried out to determine the local elastic mechanical properties. The samples consist of epoxy matrix with graphene additives. Additives were added at levels of 0% as a control, 0. 5%, 1%, 2. 5%, 5% and 10% by weight. The local elastic properties such as moduli and hardness were calculated. After each indentation, the prints were characterized using scanning electron microscopy (SEM). It seems that the local mechanical properties of nanocomposite samples were improved as the amount of nano-additives increased. Based on the curve displacement and surface imaging, we can conclude that the nano-additives influenced the overall plastic mechanical behavior of the samples. For simulating micro-indentation test, a finite element analysis model was developed using ABAQUS software and compared to experimental tests.
Generation and Meshing of Micro-Indentation Model A 3D structural solid element type C3D8R for specimen of composites based NGPs and epoxy matrix for modeling Vickers indenter was adopted by using Finite Element software ABAQUS. The mechanical properties are isotropic for epoxy matrix, NGPs and for the indenter (rigid) (Table  2 ). A homogenization procedure was necessary to evaluate the properties of the specimens with the different percentages of NGPs. The indentation was simulated as a contact between rigid indenter and elastic specimens; no elasto-plastic effect was considered. The specimen was fixed on the bottom and lateral sections, and the rigid indenter was animated by a translation movement in the direction of thickness. Figure  17a  shows the mesh of 1 {4 models. As the effect of indentation is very localized, Figure  17b  shows the studied part.
Generation and Meshing of Micro-Indentation Model A 3D structural solid element type C3D8R for specimen of composites based NGPs and epoxy matrix for modeling Vickers indenter was adopted by using Finite Element software ABAQUS. The mechanical properties are isotropic for epoxy matrix, NGPs and for the indenter (rigid) (Table  2 ). A homogenization procedure was necessary to evaluate the properties of the specimens with the different percentages of NGPs. The indentation was simulated as a contact between rigid indenter and elastic specimens; no elasto-plastic effect was considered. The specimen was fixed on the bottom and lateral sections, and the rigid indenter was animated by a translation movement in the direction of thickness. Figure  17a  shows the mesh of ¼ models. As the effect of indentation is very localized, Figure  17b  shows the studied part.
First Validation of Numerical Models We developed a numerical model of micro-indentation of the composites based on the implicit scheme using Abaqus FEA software. The force at each increment time was examined and correlated to the experimental variations to validate our numerical model and enhance the convergence of results. The boundary condition applied to the indenter is the displacement resulting from the experimental results (Figure  18 ). The correlations between numerical and experimental load are presented in Figure  19. A good correlation was found between the two methods. Comparable to experimental variation, the numerical response presents three different zones: load phase, 15 s of pause and unloading phase. A small difference is noted in the unloading phase beyond 30 s of loading.
The main objective of this work is to facilitate the identification, sharing and reasoning about cerebral tumors observations via the formalization of their semantic meanings in order to facilitate their exploitation in both the clinical practice and research. We have focused our analysis on the VASARI terminology as a proof of concept, but we are convinced that our work can be useful in other biomedical imaging contexts. In this paper, we propose (1) a methodology, a domain ontology and an annotation tool for providing unambiguous formal definitions of neuroimaging data, (2) an experimental work on the REMBRANDT dataset to demonstrate the added value of our work over existing methods, namely DICOM SR and the AIM model.
Rubin et al. , Levy MA  and many others researchers have promoted the use of formal ontologies  (Rubin et al. , 2009; Levy et al. , 2009; Kahn J. R. et al. , 2011; Van Soest et al. , 2014)  for automatic reasoning on these data models and overcome the current limitations listed above. This work is based on the assumption that the use of semantic web technologies, in particular ontologies as well as reasoning capabilities, can make the meaning of neuroimaging assessments more explicit and facilitate their advanced exploitation and interpretation. We are convinced that this approach can be beneficiary to the whole domain of neuroimaging, and beyond to the whole domain of biomedical imaging. However, this work focuses on a limited domain, namely the domain covered by the VASARI terminology (for Visually Accessible Rembrandt Images terminology) 1  , used as a proof of a concept.
This tool was applied to a VASARI corpus called REMBRANDT data set (Repository of Molecular Brain Neoplasia Data)  (Madhavan et al. , 2009). Third, performing semantic queries and reasoning tasks on these data to show how this semantic description of VASARI features can facilitate the interpretation of the content of medical images. The remaining of this article is organized as follows: in Section Material and Methods, we describe the methodology for the design of the VASARI ontology. Section Results provides, first, an overview of the architecture and development details of both the developed ontology and the semantic annotation tool. Second, it demonstrates how we can automatically store and semantically manipulate RDF (Resource Description Framework) data of the REMBRANDT data set. In Section Discussion, we explain some modeling choices that we have made regarding ontology design and implementation, list accomplished work and enumerate remaining problems.
Design of the experimental work In our experimental work, we have developed a semantic annotation software of VASARI data using the VASARI ontology. This software enables the user to transform the informal description of the 30 VASARI features into a formal one. This software was applied to a corpus of VASARI data called the REM-BRANDT repository. The resulting semantic data set was used to evaluate the added value of our work; especially, we performed some reasoning tasks by formulating semantic queries as well as some consistency tests to detect inconsistent assertions. Negative findings are expressed by the use of negative qualifiers as for example none or expressions as indeterminate, not applicable, without etc. Non-existence of an entity or a quality/disposition. Examples: C1:Johns cerebral tumor is without an enhancing region.
Presentation of the REMBRANDT repository The REMBRANDT repository is freely accessible on this link16. The ultimate objective of the REM-BRANDT data set is to facilitate the discovery of significant correlations between clinical and genomic information in order to provide patients with more personalized treatments in the clinical context. The REMBRANDT data set contains 30 VASARI features labeled by 3 radiologists that concern 34 patients with GBM tumors. Features values are stored in an Excel document where each spreadsheet contains evaluations asserted by a radiologist.
Implementation details We have designed the VASARI ontology in the Ontology Web Language 2 (OWL2) format using the version 5 of the Prot ég é tool  (Tudorache et al. , 2013). To extract modules from the relevant OBO ontologies we used the Ontofox web interface  (Smith et al. , 2007). Our semantic annotation tool was developed with the JAVA language and created using the Net-Beans IDE 8. 0. 2 programming environment. To design the semantic annotation software, we used the version 2. 8. 3 of the JENA35 semantic web programming framework and the version 2. 3. 2 of the JENA-Pellet reasoner engine (external engine of the JENA API)  (Sirin et al. , 2007)  to perform automatic reasoning tasks. To execute some SPARQL queries, we have used the CORESE tool 3. 2 2. Note that instance data are serialized in the RDF/XML format.
Semantic annotation software of VASARI data The annotation software begins by reading as an input the set of imaging features values of the REMBRANDT repository and the VASARI ontology schema. Then, to semantically annotate the data the software realizes four main tasks. First, it instantiates the VASARI ontology based on the VASARI labeled values. Second, it describes imaging features by creating RDF triples that establish semantic links between instances. Third, it adds these triples as statements in an RDF graph. Fourth, it serializes data in the RDF/XML grammar and records the RDF graph in memory or in a JENA triple database (TDB). Note that the software stores separately the schema and the instance data (denoted by the terms Tbox and Abox in the following paragraph). It returns the generated RDF graph of the whole REMBRANDT dataset in 1.
Semantic exploitation of the VASARI annotation data The developed software bases its reasoning on an inferred model generated with a reasoner. The information stored in the inferred graph is contained in a knowledge base (KB) that can be exploited in two ways: 1) accessed via SPARQL queries to retrieve data based on their semantics 2) checked via consistency tests. Querying a knowledge base: Let us consider this example to illustrate inference capabilities and demonstrate how the semantic format enables the exploitation of the anatomical knowledge coming from the FMA ontology. We suppose that a given KB is composed of an Abox that contains the following assertions: (A1) cte instance of CerebralTumorEpicenter, (A2) tl instance of Tempo-ralLobe, (A3) rch instance of RightCerebralHemisphere, (A4) cte bfo:located in at some time tl, (A5) tl regional part of rch.
For example lets suppose that (A1) ct instance of CerebralTumor and that one radiologist said that (A2) ct is a HemorrhagicCerebralTumor however, another radiologist said that (A3) ct is a NonHemorrhagicCerebralTumor. Reasoning on this knowledge base is impossible given because the two classes HemorrhagicCerebralTumor and NonHemor-rhagicCerebralTumor are defined as disjoint classes in the Tbox; this means that they cannot share the same set of instances (i. e. , ct). As a consequence, a classification error should occur when the reasoner performs inference tasks. The result of a consistency checking is provided via the object ValidityReport of the JENA API. This data structure encapsulates all detected inconsistent axioms and assertions. To generate explanations about inconsistencies, we have used the method explainconsistency().
VASARI features could be derived from the detailed image-based observations and measurements rather precede them, and our model provides the conceptual basis to make such enrichment of image processing systems. The Radiology Reading Room of the Future  (Gillies et al. , 2015)  will entail a reading room where in practicing radiologists interact with picture archiving and communication system software to identify, segment, and extract features from regions of interest. If prior studies obtained in the same patient are available, the previous regions of interest will be automatically identified by the reading software. As part of the reading, the extracted size, shape, location, and textural features will be automatically uploaded to a shared database and algorithmically compared with prior images to enable more precise diagnoses.
CONCLUSION We believe that neuroimaging data should be held in a structured format that makes their meanings explicit to the systems and thus facilitate their comprehension as well as management. Semantic data about imaging features (measurement values, qualities, lesion components, lesion localization, etc. ) are important (1) to support the clinical research on the development of new imaging biomarkers by combining clinical data with information coming from different medical domains (2) to improve the quality of the clinical healthcare that tend to provide personalized treatments to patients via the use of clinical guidelines that are based on evaluation criteria. In this paper, we employed the VASARI terminology as a proof of concept for the demonstration of the feasibility and the importance of making RDF and OWL data available to describe cerebral tumors observations and determining the key concepts and relationships that are central in their evaluation.
Since we learned to store typefaces in computers we can imagine type as lettering reproduced in a database (the typographic 'font') that makes the shapes of the original drawings available for composition. Lettering does not meet this typesetter's condition by itself. This clarification allows to distinguish between these two notions, which can sometimes be expressed confusedly in the typographic culture. In France, for example, the term "écriture" -which can imply a scriptural dimension -is often used to express typographical notions. Beyond confusion and inaccuracy, perhaps this indicates, in cartography, a more flexible or empirical approach of letters, because this discipline require them to fit in particular setting contexts.
The protocol Thanks to this analysis, we have identified and summarize three key issues that lead us to build a protocol: • Map background • Nonlinear text placement • Toponymic hierarchies The first two points are the main phenomena that disturb reading conditions. They are simulated and treated individually at first. First, we use this method with DTP software like Illustrator. We then transposed these tests into m. s. s. using TileMill, which allowed us to test this protocol with a more parametric/programmatic logic. This step allowed us to understand how text could be styled and parametrized in a GIS environment. Therefore it facilitated our learning of the construction of a more complex cartographic environment and the manipulation of geographic data. The third point brings another dimension to the previous categories in connecting issues relatives to the anatomic structure of the font face to the distribution problematic:.