dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation of the Representation by Entity Using OWL For several years Web Ontology Language (OWL) has been used as a standard for implementing ontologies (W3C, 2004a). In its simplest form, it enables the representation of concepts (class), instances (individuals), attributes (data properties) and relationships (object properties). The construction of an ontology in OWL, doubled with the Java taxonomy, is not done automatically. Each concept of the ontology is built so that it can be instantiated in Java but yet does not exactly reflect the Java tree structure. For example, the ScratchMatrix class in Java is inherited from the DenseMatrix class developed by Bjørn-Ove Heimsund in the framework of the Matrix Toolkit Java (MTJ) library. MTJ proposes a native implementation and a Java interface of the library Linear Algebra PACKage (LAPACK) originally written in Fortran, then used in MATLAB for solving systems of linear equations.
You can see that the details of the Java implementation are not useful at the level of the ontological description. Nevertheless, it is essential to have the possibility of instantiating a Java matrix using OWL code and reciprocally being able to express an instance of the ScratchMatrix class in OWL (see the Java Figure  13  and OWL tree structures below). For each concept of the ontology, a procedural attachment method was developed using JENA (an open-source Semantic Web framework for Java), each Java instance having a homologue in the ontology is capable of generating OWL content and possesses a constructor that can accept OWL contents as a parameter. JENA is currently one of the most complete engines. It implements RDF, RDFS and OWL as well SPARQL queries. Moreover, a forward (Rete), backwards (logic programming) and hybrid chaining engine is available.
The Link with CIDOC-CRM The ontology developed in the framework of the GROPLAN project takes into account the manufactured items surveyed, as well as the method used to measure them; in this case, photogrammetry. The surveyed item is therefore represented from the measurement point of view and has access to all the photogrammetrical data that contributed to its measurement in space. Two ontologies are aligned in this context; one dedicated to photogrammetrical measurement and the geo-localisation of the measured items, whereas the other is dedicated to the measured items, principally the archaeological artefacts, describing their dimensional properties, ratios between main dimensions, and default values. These ontologies are developed with close links to the Java class data structure that manages the photogrammetric process as well as the measured items.
It was therefore important to ensure the coherence of the measured items and potentially propose a possible revision. For more information, see  [22] [23] [24] [25] [26]. The extension of the CIDOC-CRM ontology is structured around the concept E22 Man-Made Object. The root of ItemMesurable developed in GROPLAN extends this concept. The mapping operation is done in Java by interpreting a set of data held by the Java classes as a current identification of the object: 3D bounding box, specific dimension such as maximum diameter or rim diameter in case of amphorae. These attributes are then computed in order to express the right CRM properties. For example, the amphorae typology, which is strongly connected with the E52 Time-Span, in our point of view, the amphorae typology is linked with some relations between maximum height and maximum diameter (and of course others).
Although some work have already been achieved  [28, 29] , but not enough to clearly hold the close range photogrammetry process, from image measurement to artefact representation. As the link between the data structure of the Java classes (Figure  13 ) and the GROPLAN ontology (Figure  14 ) is not trivial, the calculation of the properties of the individuals represented in the ontology is not a simple reproduction of the existing values in the corresponding Java instances. Let's take a simple example: The unit in which the sizes of the measured items are expressed. For CIDOC-CRM, these sizes can be represented by the class E54 Dimension, which possesses a property Sensors 2015, 15, 30351-30384 P91 has unit. It is effectively rigorous to assign a unit to a size. Nonetheless in the scope of the Java class data structure representing the photogrammetrical process as well as the measured items; it's not exactly the same thing.
3D Object Recognition 3D shape retrieval is still an open field of research. Knowing that until now it is difficult to find an automatic method that is efficiently able to identify a 3D model with occlusion. Many existing 3D shape retrieval methods require a complete surface model of a query object in order to find it in the dataset  [4, [30] [31] [32] while others seek to find a signature that is invariant to rotation of the object  [33] [34] [35]. We are mainly interested to partial shape retrieval methods and we refer the reader to a survey article by Yi et al. [36]  for more details. In this work, we present a novel object recognition approach that deals with partial objects. The purpose is to search for a partial object in a training dataset that contains full 3D objects (point clouds).
Measured items do not have a reference to a property unit but towards a photogrammetrical model that contains a set of images in which this item was seen and measured, as well as a reference system defining the unit for expressing 3D measurements. Property P91 is therefore filled in indirectly by the Java instance that possesses a reference to the model that generated it. Sensors 2015, 15, 30351-30384 4. 3. 3D Object Recognition 3D shape retrieval is still an open field of research. Knowing that until now it is difficult to find an automatic method that is efficiently able to identify a 3D model with occlusion. Many existing 3D shape retrieval methods require a complete surface model of a query object in order to find it in the dataset  [4, [30] [31] [32]  while others seek to find a signature that is invariant to rotation of the object  [33] [34] [35].
We are mainly interested to partial shape retrieval methods and we refer the reader to a survey article by Yi et al. [36]  for more details. In this work, we present a novel object recognition approach that deals with partial objects. The purpose is to search for a partial object in a training dataset that contains full 3D objects (point clouds). Our algorithm is based on creating a dataset of partial 2D projections that are called level curves, whenever there is an enquiry, we search the dataset in order to find the correspondence. For creating the dataset, we take as input set of 3D objects. We subsequently produce a set of samples of level curves by using a viewing sphere. The level curves are a set of 2D planar contours that are the projection of points on several perpendicular planes.
Malassiotis et al. [40]  extract a descriptor from snapshots of the surface over each point using a virtual camera oriented perpendicularly to the surface around the point. Cornea et al. [41]  used the curve-skeleton of a 3D shape and compare between curves, by using Earth Mover's Distance  [42]  to evaluate the partial similarity. Sun et al. [43]  generate a sequence of 2D planar contour by projecting the geodesic circles onto the tangent plane. In this work, we introduced a new approach for partial 3D object retrieval from a database by using level curves. The originality of this work is to use a viewing sphere to extract the contours of the object from several viewpoints in order to create a database with complete information about the 3D objects. Next, the extraction of level curves that present the contours of the object at various levels in order to reduce the problem of matching between two 3D point clouds to a matching between 2D planar curves.
The Approach 3D object models are used in many applications, such as computer vision, computer graphics and Computer-aided design. We can find a large number of databases of 3D models on the Web such as: Stanford 3D Scanning Repository  [44] , NTU 3D Model database  [45]  and 3D Keypoint Detection benchmark  [46]. In this work, we used some 3D models from  [44, 46]  and by using viewing sphere on each 3D model, we create our dataset where 3D models are represented by level curves obtained from each viewpoint. To find out if a partial model is part of an existing 3D model of our dataset, we have to match level curves of that partial model against all other in our dataset. Sensors 2015,  15,  As mentioned in the previous section, our approach is based on curve matching, the fact that we believe that the best description of 3D objects are their contours, led us to think of the level curves.
Those curves can be extract by slicing out point clouds (3D models) using several planes with a regular step (see Figure  15 ). Two ways of slicing are possible: using one point of view i. e. , one "cutting" plane shifted along the model typically as level curve with horizontal plane in cartography. choosing the cutting direction from several point of view settable on a sphere defined around the studied object. Here we consider the latter case as it produce more complete information about the object, despite the larger number of curves that will be created which affects the computation time. Matching process aims to extract the best part from one curve that matches as whole or just part of the second curve, to solve this problem, we need to find the position where the query curve aligns the best curve from the dataset.
To measure the similarity between the query curve against the curves from the dataset, we slide the signature of the query curve on the current curve of the dataset. The small Euclidean distance indicates the position of the best fitting. To remove the false matches, we added a new step to compute the Euclidean transformation between points on the matched parts. The alignment error is computed by using Root Means Square Error (RMSE), which represents the sum of distances among the points of matched parts. This error and the similarity measure can be used as indications on the quality of the curve matching. Figure  15  on the right shows an example of curve extraction from the complete 3D model of amphora Ramon 2111-73. This curve is matched with the illustrated curves in Figure  16  which shows the problem of the direction of parameterization as it is highlighted in  [47].
Experiments and Results We first performed 3D partial object retrieval experiments using the publicly available database  [44]. This work has already been published in  [48]. In this paper, we work on a real case of the Phoenician wreck as shown in Figures  1  and 3. Figure  4  shows the first step of this work in progress. Our target is to make our automatic matching algorithm reaches the accuracy of the manual matching (which is indeed an effort and time consuming task) as shown in Figure  17. Although the proposed approach is able to correctly detect the position of amphorae, rotation alignment is not accurate enough in some cases due to the small overlap. A possible solution is to extend matching aspects by To remove the false matches, we added a new step to compute the Euclidean transformation between points on the matched parts.
The alignment error is computed by using Root Means Square Error (RMSE), which represents the sum of distances among the points of matched parts. This error and the similarity measure can be used as indications on the quality of the curve matching. Figure  15  on the right shows an example of curve extraction from the complete 3D model of amphora Ramon 2111-73. This curve is matched with the illustrated curves in Figure  16  which shows the problem of the direction of parameterization as it is highlighted in  [47]. Therefore, to solve this problem, each curve from the dataset is matched by using the first and second direction of the query curve parameterization, then the best match is kept. To remove the false matches, we added a new step to compute the Euclidean transformation between points on the matched parts.
The alignment error is computed by using Root Means Square Error (RMSE), which represents the sum of distances among the points of matched parts. This error and the similarity measure can be used as indications on the quality of the curve matching. Figure  15  on the right shows an example of curve extraction from the complete 3D model of amphora Ramon 2111-73. This curve is matched with the illustrated curves in Figure  16  which shows the problem of the direction of parameterization as it is highlighted in  [47]. Therefore, to solve this problem, each curve from the dataset is matched by using the first and second direction of the query curve parameterization, then the best match is kept.
Experiments and Results We first performed 3D partial object retrieval experiments using the publicly available database  [44]. This work has already been published in  [48]. In this paper, we work on a real case of the Phoenician wreck as shown in Figures  1  and 3. Figure  4  shows the first step of this work in progress. Our target is to make our automatic matching algorithm reaches the accuracy of the manual matching (which is indeed an effort and time consuming task) as shown in Figure  17. Although the proposed approach is able to correctly detect the position of amphorae, rotation alignment is not accurate enough in some cases due to the small overlap. A possible solution is to extend matching aspects by considering other aspects such as colour and texture information. We first performed 3D partial object retrieval experiments using the publicly available database  [44].
Pattern Recognition and Measurement The goal of object recognition is to automatically and efficiently extract the relevant contents of a site; i. e. , determine the identity of the objects (of which a model is known a priori) present on the site as well as its spatial orientation. In the scope of the GROPLAN project, we plan to integrate a pattern recognition tool for exploring underwater archaeological sites. This will help the archaeologist when studying the site, notably during the recognition and orientation phases. There are two types of available data, on one hand a set of models describing the items potentially present on the site, which is currently limited to amphorae, but this database can later be broadened by the user. And on the other hand, a 3D point cloud, measured on the site, obtained through an automatic photogrammetry process and whose density is roughly the same as that used to describe the models of the sought items.
The study of the history of seafaring is the study of the relations of humans with rivers, lakes, and seas, which started in the Palaeolithic. An understanding of this important part of our past entails the recovery, analysis, and publication of large amounts of data, mostly through non-intrusive surveys. The methodology proposed in GROPLAN aims at simplifying the collection and analysis of archaeological data, and facilitating the establishment of relations between measurable objects and concepts. It builds upon the work of Steffy, who in the mid-1990s developed a database of shipbuilding information that tried to encompass a wide array of western shipbuilding traditions through time and relate conception and construction traits in a manner that allowed comparisons and the establishment of new relations between objects.
Around a decade later Monroy transformed Steffy's database into an ontological representation in RDF-OWL, and expanded its scope to potentially include other archaeological materials  [10, 60, 61]. After establishing a preliminary ontology, completed through a number of interviews of naval and maritime archaeologists, Monroy combined the database with a multi-lingual glossary and built a series of relational links to textual evidence that helped contextualize the archaeological information contained in the database. His work proposed the development of a digital library that combined a body of texts on early modern shipbuilding technology, tools to analyse and tag illustrations, a multi-lingual glossary, and a set of informatics tools to query and retrieve data  [10, [60] [61] [62] [63] [64].
Although still in an early stage of development, the GROPLAN approach has the potential to open a paradigm changing research direction. Even if we consider only questions related with the storage and sharing of primary data, GROPLAN has the potential to advance a set of basic rules of good practice in maritime archaeology. In 2001 the UNESCO Convention for the Underwater Cultural Heritage established the necessity of making all data available to the public  [65]. GROPLAN project builds upon this philosophy, proposes a way to share archaeological data, and will undoubtedly change the rules in the field.
cruzi itself or tissue modifications induced by parasites. Among immunomodulatory molecules, the human leucocyte antigen-(HLA-) G is a nonclassical histocompatibility class Ib molecule, which has a well-recognized role in controlling several branches of the immune response, inhibiting T cell proliferation, NK, and cytotoxic T lymphocytes, inducing regulatory T cells and tolerizing dendritic cells  [26]. These effects are primarily due to the preferential interaction of the HLA-G molecule with the Ig-like transcript (ILT)2, ILT4, and killer cell immunoglobulin-like (KIR2DL4) receptors, which induce inhibitory intracellular signals via tyrosinebased (ITIM) motifs  [27]. HLA-G mRNA has restricted expression in nonpathological conditions, for organs such as the placenta, thymus, heart, intestines, brain, and skin  [28] , and the constitutive expression of the HLA-G molecule has been observed on placenta, thymus, cornea, pancreas, brain, erythroid cells, and blood cell surfaces  [29] [30] [31] [32] [33] [34].
On the other hand, HLA-G neoexpression has been observed in several situations, including tumors, viral infections, engrafted tissues, and autoimmune and inflammatory diseases. Depending on the underlying condition, the expression may be advantageous when the blockade of the immune response is desirable, such as in autoimmune disorders and in allografting, whereas HLA-G expression in tumors and chronic viral infections may be harmful  [35, 36]. At the coding region, the HLA-G gene presents limited polymorphisms compared to classical HLA class I (HLA-A/B/C) genes, exhibiting only 50 alleles (The International Immunogenetics Database (IMGT), v3. 16. 0) and coding 16 different membrane-bound full length proteins. The regulatory regions also present several polymorphisms that coincide with or are close to binding sites for transcription factors (promoter region) or are targets for microRNA binding (3  untranslated region (3  UTR)).
For the early chronic infection, 10 2 bloodstream forms of T. cruzi were IP injected and mouse survival was verified daily for 60 days. Parasite load was analyzed in the heart at day 60 after infection. Gene expression was detected in the heart and esophagus at 24 and 60 days after infection (dpi), for acute and early chronic infections, respectively. Although Qa2, Qa1, and PD-1 are the encoded molecules of the H2-Q7/Q9, H2-T23, and PDCD1 genes, respectively, to facilitate understanding we will refer in this text to Qa2, Qa1, and PD-1 expression as synonyms of their respective gene expressions. Total RNA was extracted from tissues using the TRIzol reagent (Invitrogen, Carlsbad, CA) and was reverse transcribed to obtain cDNA with the SuperScript III (Invitrogen) transcriptase reverse. SYBR Green Mix-based real-time quantitative PCR assays were performed using a StepOnePlus system (Applied Biosystems, Foster City, CA).
Statistical Analysis. Allele and genotype frequencies were estimated by direct counting, and adherences of phenotypical proportions to expectations under the Hardy-Weinberg equilibrium (HWE) were tested by the complete enumeration method using the GENEPOP 3. 4 software  [50]. Linkage disequilibrium (LD) between variation sites at HLA-G 3  UTR was evaluated by means of a likelihood ratio test of linkage disequilibrium implemented at the ARLEQUIN software  [51]. HLA-G 3  UTR haplotypes of each individual were inferred for each group of patients (stratified according to the four clinical variants) and for the whole group of patients. Two distinct computational methods that do not take any prior information into account were used for this purpose: (1) the EM algorithm  [52]  implemented with the PL-EM software  [53]  and (2) a coalescence-based method implemented in the PHASE v2 software  [54].
Therefore, the haplotype pair of each subject was independently inferred by four approaches; whenever these four approaches resulted in nonconsensual inference for a given subject, he was removed from all procedures that used haplotype data as input. The frequency of each allele, genotype, or haplotype was compared between patients and controls by the two-sided Fisher exact test, with the aid of the GraphPad Instat 3. 05 software, which was also used to estimate the Odds Ratio (OR) and its 95% Confidence Interval (CI). For the HLA-G tissue expression, gene expression, and parasitism load, statistical analyses were performed using Student's -test or Mann-Whitney tests and the relationship between immunomodulatory and mediators of inflammation genes was performed using the Spearman Correlation, both with the aid of the GraphPad Prism 5 v5.
INF-, NOS-2, and IL-10 expression was increased in both BALB/c and C57BL/6 strains during the acute infection compared to controls and early chronically infected animals Figure  2 : Survival, parasite load, and parasitism of T. cruzi infected mice. The male 8-week-old BALB/c and C57BL/6 mice were intraperitoneally infected with 10 3 or 10 2 bloodstream CL strain forms for acute and early chronic infections, respectively. For acute infection, the survival was observed daily during 35 days (a) and for early chronic infection the survival was observed daily during 60 days (b). The parasite load was determined in the heart at day 24 after infection for acute infection (c) and at day 60 after infection for early chronic infection (d). Parasitemia was quantified microscopically for 29 days (e). The data represent the mean ± SEM ( = 6 mice/group).
Acknowledgments The authors thank  Ana Maria da Rocha  for technical assistance. This work was supported by  Coordenacão de Aperfeicoamento de Pessoal de Nível Superior  (Grant nos. CAPES/COFECUB 653/09 ,  PRODOC 288/05-5 , and  PNPD 02883/09-0 ),  Conselho Nacional de Desenvolvimento Científico e Tecnológico  ( CNPq Science Without Borders Program , Grant no. 236754/2012-2 ), and  Núcleo de Apoio a Pesquisa em Doencas Inflamatórias (NAP-DIN ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the paper.
Problem statement In this paper, we chose to work on a road network, composed of road segments (some of which can be footpaths in practice). A road segment corresponds to an edge of the network, it has a geometry that is a polyline and it is bordered by two roads junctions. In addition to this road network, we have a set of routes. For each route, we know the set of road segments that carry the route. A road segment can carry 0 to n different route segments (i. e. , parts of routes carried by a same road segment). Figure  2a  shows a road network composed of 5 road segments, and 2 routes (red and green) described by the road segments carrying them. Figure  2b  gives a possible layout of the routes symbol. It can happen that a route is locally carried by no road segment, because the route is locally off-trail, or because the road segment that carries the route is missing in the road dataset.