dataesr/scientific-paragraphs-categorization huggingface dataset:
To manage efficiently web services descriptions, we extract all features that describe a web service from the WSDL document and store them into a relational database. We recognize both WSDL versions (1. 1 and 2. 0). During this process, we proceed in three steps (see Figure  1 ). The first step consists of checking availability of web service and validating the content of WSDL document. The second step is to get the WSDL document and read it directly from the WSDLURI to extract all information of the document. In this step we describe the features to extract from the WSDL document: (1) the name, the documentation and the version of the WSDL, (2) WSDL types used by messages to transmit information between web services. Data types are often specified using a XML Schema Definition (XSD). We extract all kind of elements and types that can be as simple or complex types as a set of elements and/or attributes, and (3)a set of services declared in the WSDL document.
For each service we extract the name, the documentation and a set of endpoints. Then, for each endpoint we extract the name, the address (which defines the connection point to web service. It is typically represented by a simple HTTP URL) and the binding(Name, Type, Style, Transport protocol). The binding specifies the interface as well as defining the SOAP binding style (RPC/Document) and transport SOAP protocol. The interface defines the operations to be performed for a web service, and the messages that are used to perform the operation. We also extract for each operation the name, the documentation, the input and the output parameters. The input/output parameters can be referred to the previously extracted types/elements. Finally, the third step is dedicated to save the extracted WSDL features into a database.
The interface defines the operations to be performed for a web service, and the messages that are used to perform the operation. We also extract for each operation the name, the documentation, the input and the output parameters. The input/output parameters can be referred to the previously extracted types/elements. Finally, the third step is dedicated to save the extracted WSDL features into a database. The extracted information will be used during the generation of representations(B, RBTT and SR). Before presenting the methods for calculating representations, we discuss some text-processing standard used thereafter. 1. Tag removal: This step removes all HTML tags, CSS components, symbols(punctuation, etc. ). 2. Splitting and remove a stop words: Some terms are composed by several words separated by a capital letter; we use therefore regular expression to extract these words.
Rules Based Text Tagging of web services descriptions (RBTT) A lot of services have very detailed descriptions, especially when they offer several operations with their own descriptions. The main question is how to recognize significant parts or entities in the text description and how to use the filtered information to describe the web service? Our approach consists in the definition of extraction rules to identify, extract and annotate relevant multi-word terms from web service descriptions. This approach has been already used for biological data  [19]. The processing steps(Tokenization, Part-Of-Speech tagging, Extraction and output generation) of Information Extraction have been implemented as modules using the Lingua Stream platform  5. Lingua Stream  [20]  is an integrated experimentation environment targeted to researchers in natural language processing (NLP).
Rules Based Text Tagging of web services descriptions (RBTT) A lot of services have very detailed descriptions, especially when they offer several operations with their own descriptions. The main question is how to recognize significant parts or entities in the text description and how to use the filtered information to describe the web service? Our approach consists in the definition of extraction rules to identify, extract and annotate relevant multi-word terms from web service descriptions. This approach has been already used for biological data  [19]. The processing steps(Tokenization, Part-Of-Speech tagging, Extraction and output generation) of Information Extraction have been implemented as modules using the Lingua Stream platform 5. Lingua Stream  [20]  is an integrated experimentation environment targeted to researchers in natural language processing (NLP).
EVALUATION We have considered different web service sources like WebservicesX. net 7 , xMethods. net 8 and seekda. com  9. Our general UDDI architecture allows using different UDDI registries, which are used through specific wrappers. We have collected around 8,500 services. However, these services are not classified into categories. This is a real problem when we want to evaluate the usefulness of our representations using precision and recall measures. For this reason, we have considered a new web services source, i. e. service-finder. eu 10 , which classifies its services using an ontology  [18]. The number of services, which are classified into a category by service-finder is only reduced to only 1,647 web services. 465 services among them are not available: they are discarded. The collected web services are multi-languages.
Reputation Manager: It allows calculating the symbolic reputation of web services. • Clustering Manager: It allows creating the clusters of web services. • WSDL Manager: It allows parsing, extracting features from WSDL documents and save them in the WSDB database. • WVMC rawler: It is a dynamic web service that runs automatically to collect Web services published in other UDDI registries. • WSDB Manager: It allows managing the WSDB database (CRUD -Create/Read/Update/Delete operations).
• Reputation Manager: It allows calculating the symbolic reputation of web services. • Clustering Manager: It allows creating the clusters of web services. • WSDL Manager: It allows parsing, extracting features from WSDL documents and save them in the WSDB database. • WVMC rawler: It is a dynamic web service that runs automatically to collect Web services published in other UDDI registries. • WSDB Manager: It allows managing the WSDB database (CRUD -Create/Read/Update/Delete operations).
Published data compilation The official National Natural Patrimony Inventory (INPN: http://inpn. mnhn. fr) currently lists 138 Araneae species for French Guiana. This list was checked and augmented by a complete survey of the literature from a database of Neotropical spiders  (Brescovit et al. 2011). Species names were checked with reference to the world catalog of spiders 13. 0  (Platnick 2012 ) and Prószyński's (2012) catalog of world.
Data analysis An accumulation curve plotting the number of collected individuals by the number of M-S found was drawn to assess the species richness found in French Guiana. This accumulation curve was drawn with only seven of the sampling sites, because a quantitative protocol accompanied with photography of each specimen were applied only at those sites  (Vedel and Lalagüe 2013). Although the number of sites (seven) for any statistical study is low, a rough estimation of the total number of spider species found can be nevertheless computed with a high standard deviation to consider. A logarithmic tendency curve  (Colwell and Coddington 1994 ) (and its equation) was also drawn by Excel open office to derive the rate of the M-S number increase from our samplings (Table  4  and Figure  1 ). In addition, to estimate the total species richness of French Guiana we computed, from our sampling, the two most widely used estimators: the Chao 1  (Chao 2005, Gotelli and Colwell 2010)  was manually calculated, and the Jackknife computed online (http://www.
Acknowledgements The first author would like to thank the SEAG (especially  P-H. Dalens  and  S. Fernandez ) for setting up most of the sampling trips contributing to this study, all the specialists which identified and provided specimens for this study (Courtial C. , Edwards G. B. , Gonzales A. B. , Huber B. , Lehtinen P. T. , Lise A. A. , Maréchal P. , Santos A. J. , Ysnel F. ) and  Greg Lamarre  and  Stéphane Brûlé  for constructive discussions. We are indebted to  Dr. Christopher David Williams  who kindly corrected the English of this article. The authors thank four reviewers who improved substancially the manuscript with their comments. We also thank the following people for allowing and partly organizing the sampling trip :  Maël Dewynter , ONF conservator of the natural reserve of Nouragues,  Philippe Gaucher , Technical director of the Nouragues ;  Marguerite Delaval , ONF conservator of the natural reserve of la Trinité ; for the mission in Mount Itoupé,  Jean-Claude Duss , director of Club Med ;  Frédéric Mortier , director of  Parc Amazonien de Guyane ,  Cécile Guitet  chief of patrimoine naturel du  Parc Amazonien  and  Nicolas Surugue , in charge of ecological mission.
Specimen were finally cleared and hardened in glycerol/ethanol baths and stored in 87% glycerol. Femur length was measured by means of the Cell software (Olympus). Immunohistochemistry. Cells cultured on poly-L-lysine-precoated glass coverslips were washed, fixed with 4% paraformaldehyde and permeabilized with 0. 1% Triton X-100 (Sigma-Aldrich), as reported by Akchiche et al. 14  Nonspecific binding sites were blocked in phosphate-buffered saline containing 1% bovine serum albumin (BSA) and incubation was performed overnight with an antibody against one of the following proteins: Stat3 (rabbit monoclonal, 1/200, Cell Signaling Technology, Ozyme, Saint-Quentin en Yvelines, France), phospho-Stat3 Tyr 705 (mouse monoclonal, 1/100, Cell Signaling Technology), phospho-Stat3 Ser 727 (mouse monoclonal, 1/100, Enzo life sciences, Villeurbanne, France), actin (goat polyclonal, 1/200, Santa Cruz Biotechnology, Tebu-Bio, Le Perrayen-Yvelines, France), NF68 (rabbit polyclonal, 1/300, US Biological, Euromedex, Souffelweyersheim, France).
Cell Death and Disease Â 60 magnification) and unbiased cell counts in randomly selected fields were performed with a BX51WI microscope (Olympus) coupled to an Olympus camera (U-CMAD3) or a confocal microscope (Nikon Instruments, Champigny sur Marne Cedex, France), and analyzed using Cell software. Labeled cells were calculated as a percent of total cells after Dapi counterstaining. Western blotting. Western blot analyses were performed using standard procedure with chemiluminescence using ECL system (Amersham Biosciences, Orsay, France), as previously detailed. 49 Antibodies against the following proteins were used: Stat3 (rabbit monoclonal, 1/1000, Cell Signaling Technology), phospho-Stat3 Tyr 705 (mouse monoclonal, 1/1000, Cell Signaling Technology), phospho-Stat3 Ser 727 (mouse monoclonal, 1/250, Enzo life sciences), Bcl-2 (goat polyclonal, 1/1000, Santa Cruz Biotechnology), Bcl-xL (rabbit polyclonal, 1/1000, Cell Signaling Technology), Bax (rabbit polyclonal, 1/1000, Santa Cruz Biotechnology), cleaved caspase-3, Asp 175 (rabbit polyclonal, 1/1000, Cell Signaling Technology), cleaved caspase-9, Asp 353 (rabbit polyclonal, 1/1000, Cell Signaling Technology), Src (rabbit polyclonal, 1/500, Cell Signaling Technology), phospho-Src Family Tyr 416 (rabbit monoclonal, 1/1000, Cell Signaling Technology), p44/42 MAP kinase (Erk1/2, rabbit polyclonal, 1/500, Cell Signaling Technology), phospho-p44/42 MAP kinase Thr 202 /Tyr 204 (rabbit polyclonal, 1/500, Cell Signaling Technology), Akt (rabbit polyclonal, 1/1000, Cell Signaling Technology), phospho-Akt Ser 473 (rabbit polyclonal, 1/1000, Cell Signaling Technology), phospho-p38 MAP kinase Thr 180 /Tyr 182 (rabbit monoclonal, 1/500, Cell Signaling Technology) and phospho-SAPK/JNK Thr 183 /Tyr 185 (rabbit monoclonal, 1/500, Cell Signaling Technology).
Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, mouse monoclonal, 1/1000, Abcam, Paris, France) was used as an internal standard. Polyvinylidene difluoride membranes were incubated for 1 h at room temperature with the corresponding horseradish peroxidase-conjugated preadsorbed secondary antibody (1/5000, Molecular Probes). Quantity One software, associated with the VersaDoc imaging system (Model 1000, Bio-Rad Laboratories, Marnes-la-Coquette, France), was used to quantify signals. RNA extraction. Total RNA was extracted from H19-7 cells (10 2 -10 7 cells) and from 0. 5 mg of embryonic midbrain tissues using the mirVana miRNA Isolation kit (Applied Biosystems, Foster City, CA, USA) following the manufacturer's instructions. miRNA is isolated using a two-step procedure. In the first step, samples are disrupted in a denaturing lysis buffer, and then subjected to acidphenol/chloroform extraction.
Quantification was performed with RNA polymerase II (Pol II) as an internal standard with the following primers: forward 5 0 -GCATTAACAT CAGGAACAATAAAGGC-3 0 and reverse 5 0 -GATCTCTCTAAAGTTGACCT CATTGG-3 0. Real-time PCR was carried out using the DNA-binding dye SYBR Green I for the detection of PCR products. Temperature cycling was 15 min at 95 1C followed by 50 cycles consisting of 94 1C for 10 s, 56 1C for 15 s and 72 1C for 15 s. Results were expressed as arbitrary units by calculating the ratio of crossing points of amplification curves stat3 mRNAs and internal standard by using the RelQuant software (Roche Diagnostics).
Real-time PCR was carried out on a Step One Plus LightCycler (Roche Applied Science). Incubations were performed in a 96-well plate at 95 1C for 10 min for enzyme activation, followed by 40 cycles of PCR: denaturation (95 1C for 15 s), and annealing/extending (60 1C for 2 min). Data analysis was performed with the software provided by the manufacturer (Step One Plus). In situ hybridization: The in situ detection of miR-124 was performed on paraffin-embedded sections from normal and methyl donor-deficient brain tissues by locked nucleic acid (LNA)-oligo in situ hybridization, as previously described by Kloosterman et al. 53  Briefly, the slides were deparaffinized in xylene, rehydrated in decreasing concentrations of ethanol and treated with proteinase-k for nucleic acid release. Slides were then re-dehydrated and prehybridized in hybridization buffer with 0.
The siRNA sequence is (sense strand-indicated): 5 0 -UAAGGCACGCGGU GAAUGCC-3 0 , and mirVana miRNA Inhibitor Negative Control no. 1 was used as control for evaluation of the effect of the experimental miRNA inhibition. siRNA duplexes (25 nM final concentration for the first transfection then 10 nM for the second day re-transfection) were transfected with lipofectamine RNAiMax (Invitrogen). Three days post transfection, cells were harvested for RNA or protein extraction. Statistical analysis. Data were analyzed with Statview 5 software for Windows (SAS Institute, Berkley, CA, USA). They were compared by using one-way analysis of variance (ANOVA) with Fisher's test. P-value o0. 05 was considered to indicate significance.
The skin was shaved and cleaned with alcohol prior to electrode positioning. The data were wirelessly streamed during the treadmill and robotic-guided walking conditions and analyzed using Matlab 7. 0 (The Mathworks, Natick, MA) and SPSS statistical software (v. 18. 0 IBM). In the robotic-guided walking condition, the knee and hip angles in the sagittal plane and the forces exchanged against the machine at the knee and hip joints were recorded from the analog output of the Lokomat. In the treadmill walking condition (walking without the aid of Lokomat), an electrogoniometer was used to measure the knee joint angle in the sagittal plane. In all conditions, a foot switch was placed beneath the heel of the dominant leg to identify and segment the gait cycles. The values of the visual biofeedback from the Lokomat were recorded for every gait cycle and used for offline validation of each trial.
EMG signal analysis The raw EMG data were band-pass filtered (3 rd order Butterworth digital, bandwidth 20-400 Hz, roll-off rate of 12 dB/decade) to attenuate DC offset, motion artifacts, and high frequency noise. The EMG signals were rectified and were smoothed using a 50-point root mean squared (RMS) algorithm. The smoothed EMG signals were interpolated per each stride cycle in order to obtain average stride cycles with 101 points. Stride cycles were then averaged to obtain time-normalized gait cycles with 101 points. For each muscle and participant, each timenormalized EMG signal was amplitude-normalized by its maximal value obtained in all the conditions of speed and GF. Although averaging of EMG waveforms decreases the variability of the signal, inter-trial variability is reduced in the stereotyped muscular activity in the Lokomat  [22].
Kinematic and force analysis The kinematic and force data were averaged per each stride in order to obtain data time normalized, expressed as a percentage of the total gait cycle, i. e. , 0 to 100%. The angular range of motion (ROM) in the sagittal plane for hip and knee was computed by subtracting the minimum joint angle from the maximum joint angle for Lokomat trials for each condition of GF and speed. The ROM in the sagittal plane for knee during the treadmill walking was also calculated, for each speed. The time (% of gait cycle) at which the minimum and maximum angles occurred were also determined. The kinetic range of forces (ROF) in the hip and knee joints of the Lokomat was found by subtracting the minimum joint force from the maximum joint force for robotic-guided walking trials for each condition of GF and speed and also for each gait phase.
Acknowledgements This project is funded by the  European Commission , project " BETTER " (contract number  247935 ) and  Spanish Consolider-Ingenio Programme , project " HYPER " (contract number  CSD2009-00067 ) and  Università Degli Studi di Roma "Foro Italico ", research project "Dynamic sensorimotor interaction during locomotion: influences of perturbations and/or body unloading".
Collection of Human Tissue Samples Three patients treated for prostate cancer who were victims in the radiation oncology accident at the Public Hospital in Epinal, France, were included in this study. Post-treatment toxicities were all graded (grades III and IV) according to the Common Terminology Criteria for Adverse Events (3. 0 scale). Some endoscopic anorectum biopsies coming from the EPOPA trial (Patients Overexposed for a Prostate Adenocarcinoma, Clinical Trials. gov no. NCT00773656). This clinical study has received an approval from the French health ministry (http://clinicaltrials. gov/show/ NCT00773656). The rectal tissues from nonirradiated areas (obtained following the ethical guidelines of the Gustave Roussy Institute and the French Medical Research Council) were fixed in neutral buffered formalin and paraffin embedded.
MMP-3 immunolocalization was performed using the streptABC-HRP system (DakoCytomation, Trappe, France, http:// www. dakocytomation. com), and the EnVision ϩ System horseradish peroxidase (HRP) (DakoCytomation) was used as secondary reagent for all immunostaining sections. The color reaction was developed using the NovaRED kit (Vector Laboratories, Burlingame, CA, http://www. vectorlabs. com) and counterstained with Mayer's hemalun. The vascular and cellular densities were measured using image analysis software (Histolab; Microvision Instruments, Evry, France, http://www. microvision. fr).
We here address the problem comparing the resulting point clouds. Difficulties arise because of the highly differentiated acquisition processes, which result in very different point densities. We propose a new tool, designed to compare differences between pairs of point cloud or surfaces (triangulated meshes). Moreover, dealing with huge datasets requires the implementation of appropriate structures and algorithms. Several techniques are presented : point-to-point, cloud-to-cloud and cloud-to-mesh. In addition farthest point resampling, octree structure and Hausdorff distance are adopted and described. Experimental results are shown for a 475 m long canal tunnel located in France.
The main objective of this paper is to assess the accuracy of the photogrammetric model by taking as reference a static terrestrial laser scanning (TLS) model. A technique to evaluate accuracy is to compute a distance between a dataset and a 3D reference point cloud. However, calculating such a distance may be problematic. In particular, the two distinct acquisition processes lead to different number of points in both models. Moreover, the point density within the two clouds is non-uniform. Note that laser scanning stations are located on a bench, near a side wall, which implies an heterogeneous density of the cloud between both sides of the tunnel. Finally, the tunnel is quite long and the mass of recorded data may be very huge. Therefore specific computation structures will be required. We are interested in three different algorithms that compute a distance between the two models : point-to-point, cloud-to-mesh and cloud-to-cloud methods.
Facing huge datasets, we propose to implement these algorithms using a specific octree structure, for its capacity to quickly and accurately address points at a given location. Differences in sampling may be alleviated either by using meshing or by considering non-symmetric distances. The second contribution of the paper is that we evaluate the accuracy of the photogrammetric model. First, we need to check the reliability of the reference TLS model. Subsequently, we compare the photogrammetric model to the TLS model by using the three proposed accuracy measures. The paper is organized as follows. We first propose an overview of related works (Sec. 2). We introduce the three approaches we propose for comparing huge datasets (Sec. 3). In Sec. 4, we present the experimental setup for data recording.
However, this formal approach requires an uniform sampling. Our study deals with huge datasets, which implies using either an appropriated sampling  (Eldar et al. , 1997 , Bronstein et al. , 2008)  or specific computation structures such as an octree subdivision  (Girardeau-Montaut et al. , 2005). An original method is proposed in  (Lague et al. , 2013)  for surveying canyon surface changes using TLS. In this approach, the distance between two point clouds (Laser clouds and dense tacheometric survey) is obtained by locally estimating the surface normals and computing the distance in the direction of the normals. A roughness measure is considered for computing the surface normals and a local confidence range is calculated.
We will now consider a single subdivision level for simplicity. The subdivision algorithm proceeds as follows: initially, the original dataset is sorted by increasing x-axis coordinates and then two subsets are created with respect to a threshold value, which is defined as the sum of the abscissa of the starting point and d(PN ). The first subset will contain all points of the initial cloud with x-coordinate lower than the threshold value and the second one gathers all points with x-coordinates greater than the threshold. Then, the same work is repeated, taking these two subsets as initial data, by sorting them by increasing y-axis coordinates and thresholding, which yields four sets of points. Finally these four clouds are sorted again, by increasing z-axis coordinates and a new division takes place according to the already defined characteristics, and one obtains eight octants (see Figure  2  for example).
Proposed approaches The proposed approaches aim at measuring the accuracy of a 3D model. In this way, we have implemented three algorithms for computing a distance between a set of points and a reference dataset. Point-to-point algorithm The point-to-point approach aims at comparing two sets of points regularly distributed along the tunnel. The distances are computed only between similar points and the maximum distance is considered. Note that the number of points in both sets must be the same. Cloud-to-mesh algorithm In the cloud-to-mesh technic, Euclidean distances are computed between points in the photogrammetric model and meshes. The triangular meshes are computed from the lasergrammetric point cloud by Delaunay triangulation. As a consequence, accuracy corresponds to the quadratic mean of all Euclidean distances.