dataesr/scientific-paragraphs-categorization huggingface dataset:
Ontologies offer a semantic layer to knowledge graphs that can be mined by Semantic Web compliant engines in order to consolidate inconsistent and incomplete data, and to discover new added information. Finally SPARQL (Sparql Protocol And RDF Query Language) provides an expressive query language to retrieve triple patterns, as well as different protocols and formats to send queries and their results across networks. In the project, we publish each medical database with a SPARQL endpoint, enabling to send query and to retrieve results through HTTP. This Semantic Web layer provides a reasoning capabilities layer enabling the data consolidation, and interoperability with external linked data sources, such as Dbpedia  4. Fig. 3  represents the core of the Electronic Health Record ontology, semEHR, that we designed to describe the SQL medical data of FedEHR.
"Patient" and "Physician" are typed with eponym subclasses of foaf:Person 5. A "Patient" has a "Medical Bag" (semehr:MedicalBag) which contains multiple "Medical Events" (semehr:MedicalEvent) occurring during follow-up care. The measured clinical variables (semehr:ClinicalVariable) are attached to corresponding medical events with the property semehr:hasClinicalVariable. Properties are also defined to describe the different attributes of classes such as the date of a medical event or the value of a clinical variable. This core is an extensible basis that can represent the variety of medical data encountered in the GINSENG project. In particular, we defined various subclasses of semehr:MedicalEvent in order to represent the taxonomy of the multiple types of medical events. For instance, the class semehr:Test is a subclass of semehr:MedicalEvent which has multiple subclasses to represent different types of medical tests, including blood test and Pap Test.
B. Distributed linked health data querying with KGRAM-DQP Regarding the context of the project, and the distribution of medical data, it seems more appropriate to implement a single data federator that provides a uniform view over a set of federated and autonomous healthcare data centers. The principle consists in decomposing and rewriting a unique SPARQL query into a set of subqueries sent to the different medical databases. Results are retrieved, joined and merged into a single set of results finally presented in a unified way. Corese/KGRAM  [21]  is a generic and versatile Semantic Web factory aiming at representing, querying and reasoning on Knowledge Graphs. It is fully compliant with the W3C Semantic Web standards (RDF/RDFS/SPARQL). It has been extended with federation capabilities (KGRAM-DQP) to address distribution scenarios, particularly relevant in the context of the GINSENG project and more generally in Linked Open Data  [19]  scenarios.
6 ADICAP thesaurus is a french codification for lesions of anatomic pathology http://www. adicap. asso. fr/ Each "Producer" can either serve local or remote graph-based data sources. In addition, to address data heterogeneity issues, "Producers" also act as data mediators, rewriting incoming subqueries into the data source native query language, and finally transforming the native results into a graph-based representation. In practice, KGRAM-DQP handles federated SPARQL queries through an iteration over all subqueries, which are sent concurrently to the data sources connected, wrapped into simple "CONSTRUCT" SPARQL clauses that generate triple results. Candidate results returned are finally joined into the result graph. In spite of subqueries concurrent execution, the federated approach can lead to inefficient querying scenarios, due to the amount of remote queries and transferred results finally joined into the federator.
C. Bridging local health data and open linked data To illustrate the medical data federation capabilities, we implemented a scenario combining medical data (in this case the ADICAP code) and public demographic data for the needs of epidemiological studies. The GINSENG distributed knowledge base contains the necessary EHRs, while demographic data are provided by the French DBpedia instance (semantic data extracted from wikipedia). The SPARQL query illustrated in Fig. 5  first searches for patients whose disease is characterized by the "BHGSA3FO" ADICAP code (lines 4 to 6) and their postal code. These triple patterns are sent to the SPARQL endpoints of each medical partner. Then a SERVICE clause (line 8) indicates that the triple patterns enclosed (lines 10 to 12) are sent to the DBpedia data source.
This subquery searches for the total population corresponding to postal codes of the Auvergne region in France. Once retrieved, results are joined, based on the postal code so as to count the number of patients affected by the disease encoded "BHGSA3FO" per geographic area. The size of the corresponding population is then displayed. This federated querying scenario has been simulated on a standard laptop computer. Three SPARQL endpoints have been implemented as illustrated in Fig. 4.
A. The Virtual Imaging Platform The Virtual Imaging Platform (VIP)  [13]  is a web portal dedicated to the simulation and processing of medical data on distributed computing resources. VIP exposes scientific applications as services, mainly for medical simulation and neuro-image analysis. To handle the computing and storage needs of these applications, it is supported by the European Grid Infrastructure (http://www. egi. eu), where it consumes about 50 CPU years every month. To date, more than 500 users from 50 countries can access VIP. While scientific computing was the initial motivation for VIP, the current evolution of scientific practices toward so-called Big Data leads to the integration of various data repositories in the platform. The semantic databases exploited by the GINSENG project are an example of such integration.
B. Interfaces to semantic databases Interfaces to semantic databases available in VIP implement the following use cases: 1. Experts (e. g. epidemiologists and computer scientists) develop and publish queries; 2. End users (e. g. healthcare professionals or health watch authorities) parameterize and execute published queries; Four tools were developed to implement these use cases: the Query Maker, Query Executor, Query History, and Query Explorer. The Query Maker is a tool for SPARQL experts to develop and publish queries (Fig. 6 ). Developers can enter the body of a SPARQL query, and use placeholders to define parameters instantiated when the query is executed. The query can then be saved and possibly published to end users. The Query Maker allows to describe queries as complex as SPARQL permits, and to expose them to endusers.
10 ). Its graphical user interface (GUI) completely hides the SPARQL backend. End-users can select variables in a graph, set the data sources from which these variables should be extracted, and possibly specify restrictions about their values. Underneath the GUI, a SPARQL query is built by the system: the SELECT clause of this query contains the variables selected by the user, the FROM clause contains the selected data sources, and the WHERE clause contains the variables and their restrictions. ORDER BY and GROUP BY clauses can also be specified using checkboxes available in advanced options. The resulting query can then be executed as previously described, and monitored in the Query History. The Query Explorer allows end users to develop their own queries with no exposure to SPARQL. This, however, limits the range of queries that can be described.
For instance, queries involving the OPTIONAL construct, aggregation functions such as SUM and AVG, or unions of multiple SELECT constructs currently cannot be described. Feedback from end users could help in determining whether this interface should be extended to enable a larger set of queries. The tools described in this section are available and testable in the production instance of VIP at (https://vip. creatis. insa-lyon. fr). This article describes the work in progress applied to a surveillance network for cancer and perinatal health through a distributed architecture. The network will allow federating, in a fully secured way, medical databases located in hospitals in order to make it available to epidemiologists for health watch studies. Different developments have been addressed to answer healthcare professionals' needs regarding patient's follow-up and pathology surveillance: Metamodels: the integration and the management of patient data sheets have been processed through the usage of metamodels adopting FedEHR metadata standards in the objective to extract pertinent information for epidemiologic studies.
Data semantic: the elaboration of an RDF repository allowing for data to be linked and to be "grounded" in semantic descriptions using a set of triples organized into a RDF Graph. The semantic engine Corese/KGRAM is used to query the RDF database with SPARQL language. Web semantic: the integration of an API to query the RDF database using SPARQL endpoints. This API is integrated to the VIP platform and through an iframe into the e-ginseng. org web portal. Future questions regarding the management of medical images will be triggered to complete the capabilities of the network by proposing close developments of tools and services validated by healthcare professionals; this is of key importance in order to offer the best achieved network.
Medical databases have been simulated for the moment with fictive anonymized patient information. Medical data size consists in around 5 Million triples without taking into account the DBpedia dataset. The query shown in Fig. 5 retrieves 209 results in about one minute. The query execution time can dramatically be reduced by manually grouping triple patterns into a UNION of SERVICE clauses leading to only few SPARQL queries sent over the network. Triple patterns grouping strategies open interesting optimization perspectives but need further investigation due to non-trivial data partitioning, mixing both horizontal (instances of the same data model split in several databases) and vertical (DBPedia data in a single store) data fragmentation.
The PANDA collaboration studies fundamental aspects of the strong interaction in the transition region between non-perturbative and perturbative QCD, investigating charmonium spectroscopy, hybrids and glueballs, hypernuclei, light and heavy meson production with antiproton beams. In this contribution we focus on leptonic final channels which give access to nucleon electromagnetic form factors. The expected precision on the electric and magnetic form factors of the proton in the time-like region and the radiative corrections to be applied to the data are discussed.
The number of running jobs in parallel increased from typically 100 at the beginning of the production to more than 800 at the end. This is due to the fact that new sites became operational during that period and to the optimization of the procedures. The achieved production speed was 6 × 10 6 events/day  [3]. The simulations using the official software of the experiment, PANDARoot, consists of five steps. The generated particles propagate through the detector using either GEANT3 (this study) or GEANT4. The information on the hits and the energy losses is digitized, including the electronic noise, into a response of the different detectors. The physical quantities such as momentum, ratio of energy loss along the path length in each straw tube (dE/dx), Cerenkov angle in the DIRC detector, and energy deposit in the electromagnetic calorimeter are reconstructed for particle identification.
Conclusions After a brief introduction on the FAIR facility and on the PANDA experiment, we have presented the results of a simulation dedicated to the measurement of the time-like electromagnetic form factors of the proton, based on the PANDARoot software and using the PANDAGrid. The efficiency of such a system allowed to perform a large scale simulation of the reactions p+ p → e + +e -and p+ p → π + +π - which is the main background. 10 6 events (10 8 for the background) for each of the three kinematical points have been simulated and analyzed for q 2 = 5. 4, 8. 4 and 13. 9 GeV 2. We have shown that the form factor ratio can be measured with a reasonable accuracy up to q 2 ≃ 14 GeV 2. Radiative corrections have to be taken into account: the necessity to include odd contributions and high orders will depend on the resolution and on the errors inherent to the measurements.
Motivation: Current advances in DNA synthesis, cloning and sequencing technologies afford high-throughput implementation of artificial sequences into living cells. However, flexible computational tools for multi-objective sequence design are lacking, limiting the potential of these technologies. Results: We developed DNA-Tailor (D-Tailor), a fully extendable software framework, for property-based design of synthetic DNA sequences. D-Tailor permits the seamless integration of multiple sequence analysis tools into a generic Monte Carlo simulation that evolves sequences toward any combination of rationally defined properties. As proof of principle, we show that D-Tailor is capable of designing sequence libraries comprising all possible combinations among three different sequence properties influencing translation efficiency in Escherichia coli.
, 2010; Antony, 2003; Kosuri et al. , 2013; Sharon et al. , 2012; Smith et al. , 2013). DoE is a general framework that fully integrates planning and analysis phases, and comprises three major steps. The first one consists in identifying the factors of interest and defining the range of values for each factor. In the case of molecular sequences, factors are properties of the primary sequence itself and can be typically identified by reanalysing available functional genomic data and published mechanistic studies. The second step consists in implementing a particular experimental design wherein multiple combinations of factor levels are selected to create an experimental dataset providing maximal information to relate the design factors to the response variable(s). For example, one of the most informative DoE is the full-factorial design, where all possible combinations of factor levels across the different factors are performed.
The resulting dataset not only permits to estimate the contribution of each factor to the measured response variable, but also robustly captures the interactions between the different factors  (Antony, 2003; Mutalik et al. , 2013). Last, the third step includes the collection of experimental data and definition of a model relating the multiple factors to the response variable(s). Of note, this can be an iterative process wherein models derived from the third phase can inform the design of a new set of experiments. Although implementation of experimental designs systematically varying easily manipulated factors can be straightforward (e. g. growth medium, pH, temperature or oxygen levels), the ability to design artificial sequences whose intrinsic properties can be systematically varied is not necessarily trivial (e.
Sequence analyzer D-Tailor provides a generic interface for multidimensional interrogation of DNA sequences. The software is designed with a modular architecture, so that users with basic programing skills can easily implement or extend modules for handling any sequence property of interest. Such modules can be implemented using custom Python code or scripts connecting to Fig. 1. D-Tailor enables multidimensional analysis and design of DNA sequences. D-Tailor provides a flexible and extendable architecture to interrogate different sequence properties (box in the middle). The left panel depicts an example of the retrieval process of two properties (RNA structure and motif prediction) from multiple input sequences that can come from either FASTA or CSV files. The resulting score profile can be used to identify general trends and further define ideal parameter ranges for the design objectives.
The right panel shows the design mode of D-Tailor, wherein a seed sequence is evolved to meet a user-defined combination of sequence properties. The figure depicts a full-factorial design for two different properties of interest (RNA structure and motif scores) with three levels each (low, medium and high), which yields a total of nine different combinations (colored areas). third party software (see the Tutorial available in the Supplementary Material). In analysis mode, D-Tailor reads a set of sequences in either delimiter separated (e. g. CSV) or FASTA format files. A property profile is then computed for each of the input sequences by successively calling the analysis modules specified by the user (Fig. 1 , left panel). D-Tailor currently comprises 14 different modules to compute various sequence properties involved in diverse mechanisms of gene regulation.
This collection of sequence property evaluators includes algorithms to score promoter regions or transcription factor binding sites based on sequence logos  (Thomas-Chollier et al. , 2011) , estimate translation initiation rates based on the Shine-Dalgarno (SD) sequence  (Shine and Dalgarno, 1975) , predict propensity to form RNA structures, calculate nucleotide composition or compute the codon adaptation index (CAI) for a given gene sequence  (Sharp and Li, 1987). Although the implementation of the different sequence property evaluators is usually self-contained within D-Tailor, the computation of specific properties may rely on third party softwares [e. g. UNAfold  (Markham and Zuker, 2008)  for the prediction of RNA secondary structure]. Together, these modules illustrate diverse implementation modalities and provide useful examples to guide future extensions (see Supplementary Material).
Each iteration of the sequence evolver also comprises three steps: (i) the sequence being evolved is analyzed and a property requiring optimization (i. e. not within the target level) is randomly selected; (ii) the template sequence is then mutated following user-specified mutational rules (see below); and (iii) the feature scores of the resulting sequence are analyzed and evaluated with respect to the current design target [Equation (1)]. Every generated sequence is also screened for compliance to a user-defined set of rules meant to prevent the emergence of undesired properties in the final designed sequences (e. g. restriction sites, unexpected promoters or terminators). Only validated sequences are stored in the database. Next, if the new sequence matches the target combination (D ¼ 0), then the target is marked as completed and the evolution cycle is terminated.
CONCLUSION Advances in DNA-reading/writing technologies readily enable the construction and validation of complex genetic systems  (Gibson et al. , 2010). However, rules to successfully design synthetic sequences to functional specifications have been limited by measurements from biased natural samples and/or small scale controlled synthetic samples comprising at most hundreds of sequences (e. g. Allert et al. (2010) ,  Amit et al. (2011) ,  Barrick et al. (1994)   Salis et al. (2009) ]. This lack of knowledge strongly restrains the practical applications of ab initio design. Innovative experimental methodologies based on high-throughput technologies are scaling the characterization process up to tens of thousands of designed sequence variants, providing larger datasets to better understand sequence/activity relationships  (Dvir et al.
, 2013; Kinney et al. , 2010; Patwardhan et al. , 2009 Patwardhan et al. , , 2012;; Sharon et al. , 2012; Smith et al. , 2013). However dramatic, this increase in throughput remains limited in comparison to the sheer immensity of the sequence space. It is therefore crucial to reduce the dimensionality of the design space to a set of sequence properties of interest that can be independently varied to facilitate estimation of their individual contribution to the measured phenotype and further support predictable design of synthetic variants  (Allert et al. , 2010; Sharon et al. , 2012; Smith et al. , 2013). We developed D-Tailor as an extendable and flexible software platform for the multi-objective design of artificial sequences. It provides a generic interface to integrate multiple sequence analysis tools into a heuristic Monte Carlo search procedure capable of evolving sequences towards pre-defined design targets (Fig.
Longitudinal (z) mA modulation Changing the mA value along the z axis is Longitudinal (z) mA modulation which addresses the varying attenuation of the patient among anatomic regions. Figure  2. 4 shows a sample of longitudinal modulation. In this example the prescribed tube current curve is determined by using attenuation data from the CT projection radiograph and a manufacturer-specific algorithm. Therefore the only parameter that the operator defines for the system is the quality of the resulted image and the determination of the tube current is done by the attenuation information of the CT radiograph.
Proposed Denoising Algorithm As described previously, our method is proposed to improve the efficiency of the image denoising based on sparse representation and dictionary learning and design an image denoising technique for low-dose CT images. In order to obtain the higher efficiency and reduce the number of clusters, the preprocessing stage is introduced and the single image noise level estimation is developed to have an accurate update stage in the sparse representation step. In order to have a reliable comparison between our proposed method, CSR and well-known K-SVD, we have developed these algorithms in the MATLAB software environment. The proposed image denoising method is tested on different images listed in Table  5. 1. with different amount of noise variance. In all the experiments, the dictionary size for K-SVD algorithm is 64; CSR is tested with 30 and 64 dictionary atoms and WCSR method with just 30 atoms.
In respect to SSIM, Table  5. 3 demonstrates the improvement over CSR which implies better perceptual quality of the denoised images. The computation time for different images of size 512x512 with noise level of =20 for both proposed algorithm and CSR are summarized and compared in Table  5. 1 which confirms that WCSR outperforms CSR. All the experiments were done with an Intel core i7 processor of  1. 88  GHz with 12 GB of memory, using MATLAB R2012a environment and under Windows operating system. The comparison is done using the CPU runtime; although CPU time is not exact measure, it gives a rough estimation of complexity. In Figure  5. 2 and  5. 3 , some examples of denoising of medical and natural images with additive white Gaussian noise, are displayed using CSR, K-SVD and the proposed methods.
Proposed Low-Dose CT Image denoising In this section we present the simulation results focusing on lower dose CT scans to demonstrate the performance of our algorithm. The performance of our algorithm is compared with K-SVD algorithm proposed  [2]. The setting of experiments is the same as previous sections. For K-SVD algorithm, the experiments were performed with assumption of noise variance of 20 on low dose CT images and are based on 10 iterations. For quality metric calculations and training purposes, we are provided with a set of registered matching high dose and low dose CT scans. With the information provided with the CT scans database, we can obtain which image is high dose and low dose using the dose reduction strategies explained in chapter two. In our experiments the phantom size and slice thickness is fixed; tube potential is set to 120 (kV), and position in the field of view is the same for all images.