dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions We have developed a method that uses two images of an intact retina to derive the location of a retinal injection. This bypasses the need to cut and flatten the retina, improving the accuracy of the localisation as the tissue undergoes less distortion, and saving the experimentalist time. By analysing the data in a coordinate system native to the shape of the eye, we avoid the problem of representing a spherical structure in a flat coordinates. To verify our results we analysed the data in three different ways. We found good correspondence between the coordinates of our IntactEye method and of Retistruct, which uses flattened images and then folds them back onto a sphere. Both methods produce coordinates in the native curved space of the eye. We have solved the abstract problem of deducing the location of a point on a spherical object from two images, and implemented it for our use analysing mouse retinae, where there are no discernable landmarks for orientation within the retina.
Availability and requirements Project name: IntactEye. Project home page: http:// github. com/hjorthmedh/IntactEye. Archived version available at: http://dx. doi. org/10. 6084/m9. figshare. 1605574. Operating system(s): Any platform that runs MATLAB 2014b or later (tested on mac, linux and windows). Programming language: MATLAB. Other requirements: None. Any restrictions to use by non-academics: GPL/MIT.
By adding a database to the model, attributes can be assigned to the elements composing the model. Among the techniques mentioned in this section, the most appropriate in our project is the laser scanning one. Photogrammetric techniques have been excluded for the modeling part, because several areas in the St. Lawrence's Chapel are dark and very narrow. However they were useful for texturing purposes. Conventional surveying techniques are kept for the establishment of the reference network and for assessment purposes. Regarding the level of details, it will be adapted to the requirements, which are different given the floors of the Chapel. Finally, the assignment of attributes to the model will be considered in a last step, during the creation of a phased model.
The EDL implemented in CloudCompare is particularly appreciated for the rendering of dense point clouds  (Boucheny, 2009). Also mesh models might be displayed in some of these viewers. However, these free viewers are more meaningful for the transmission of data to the contractors than for the general public use, because of the data amount to be managed. Among the free easy-to-use tools for visualizing mesh models, the PDF3D format is rather useful when 3D models must be transferred to people who are not equipped with 3D modeling software or do not want to become familiar with. For the purpose of visualization and communication of 3D models, several large-scale projects deserve to be mentioned, like for instance Paris in 3D (http://paris. 3ds. com/Patrimoine), realized by Dassault System Group.
ACKNOWLEDGEMENTS We would like to thank various people for their contribution to this project, especially  J. -J. Bigot  ( Inrap ) and  C. Gaston  ( Inrap ),  S. Bengel  ( Fondation OEuvre Notre-Dame ),  S. Potier  ( Inventive Studio ) and  S. Ristow  ( university of Cologne ) for their scientific contribution, as well as the DRAC for the authorizations and the  INRAP  for the financial support.
To do so, one needs to optimize structural parameters, such as membrane radius, backplate radius, air gap, cavity height, membrane thickness, of the design of the acoustic sensor (Figure  1 ). In addition, the membrane tension and material uses to fabricate the device needed to be optimized, as well. As a large number of parameters are involved in acoustic sensor optimization, numerical simulation can be a powerful and economical tool for virtual device prototyping. However, the extensive computational effort is involved in numerical simulation and thus it usually takes a substantial amount of time to complete simulation runs of a complex structure. This paper presents a new design scheme for acoustic sensor optimization that combines numerical simulation using the COMSOL Multiphysics software and design of experiments (DOE) approach to optimize the acoustic sensor of the proposed design to obtain the acoustic resonator.
To estimate the coefficients of the model, a CCD was built with some additional points corresponding to a space-filling design to cover the entire domain. A total of 62 experiments were employed and listed in Table  4. These experiments were performed using numerical simulation, and for each experimental run, , |˂ξSe˃|fr1, , ∆ and |˂ξSe˃|fr2 were collected for further analysis and empirical model building. During this study, "nemrodW" statistical software  [31]  is used to develop experimental strategies and search for optimal settings.
We present a high-precision mass model of galaxy cluster Abell 2744, based on a stronggravitational-lensing analysis of the Hubble Space Telescope Frontier Fields (HFF) imaging data, which now include both Advanced Camera for Surveys and Wide-Field Camera 3 observations to the final depth. Taking advantage of the unprecedented depth of the visible and near-infrared data, we identify 34 new multiply imaged galaxies, bringing the total to 61, comprising 181 individual lensed images. In the process, we correct previous erroneous identifications and positions of multiple systems in the northern part of the cluster core. With the Lenstool software and the new sets of multiple images, we model the cluster using two cluster-scale dark matter halos plus galaxy-scale halos for the cluster members. Our best-fit model predicts image positions with an RMS error of 0.
69 , which constitutes an improvement by almost a factor of two over previous parametric models of this cluster. We measure the total projected mass inside a 200 kpc aperture as (2. 162 ± 0. 005)×10 14 M , thus reaching 1% level precision for the second time, following the recent HFF measurement of MACSJ0416. 1-2403. Importantly, the higher quality of the mass model translates into an overall improvement by a factor of 4 of the derived magnification factor. Together with our previous HFF gravitational lensing analysis, this work demonstrates that the HFF data enables high-precision mass measurements for massive galaxy clusters and the derivation of robust magnification maps to probe the early Universe.
2014; Coe et al. 2015; Richard et al. 2014). The first two clusters observed as part of the HFF initiative are MACSJ0416. 1-2403 (Mann & Ebeling 2012 ) and Abell 2744  (Abell et al. 1989) ; to date lensing studies based on these data have been conducted by  Jauzac et al. (2014 Jauzac et al. ( , 2015)) ;  Diego et al. (2014) ;  Grillo et al. (2014) ;  Zitrin et al. (2014) ;  Wang et al. (2015) ;  Ogrean et al. (2015). In this paper, we focus on improving earlier stronglensing analyses of Abell 2744 (also known as AC118 and MACSJ0014. 3-3022), a very X-ray luminous cluster at z=0. 308, featuring L X = 3. 1 × 10 45 erg s -1 in the 2-10 keV band  (Allen 1998 ) and 1. 4 × 10 45 erg s -1 in the 0. 1-2. 4 keV band  (Ebeling et al. 2010). This system has been extensively studied, and was first identified as an active major merger by  Giovannini et al.
HUBBLE FRONTIER FIELDS OBSERVATIONS The HFF observations of Abell 2744 (ID: 13495, P. I: J. Lotz) were obtained with WFC3 between October 25 th and November 28 th 2013 in four filters (F105W, F125W, F140W, and F160W) for total integration times corresponding to 24. 5, 12, 10, and 14. 5 orbits respectively. The HFF ACS observations were obtained more recently, between May 14 th and July 1 st 2014, in three filters (F435W, F606W, and F814W) for total integration times corresponding to 24, 14, and 46 orbits, respectively. We use the self-calibrated data (version v1. 0) with a pixel size of 0. 03 , provided by STScI 4.
HFF multiply-imaged systems The secure identification of multiple-image systems is key to building a robust model of the mass distribution within the cluster lens. The first detailed strong-lensing analysis, using pre-HFF data of Abell 2744, identified 34 images of 11 background galaxies in the redshift range 2 < z < 4  (Merten et al. 2011). Based on the same data, but in a community-wide effort for the HFF mass model initiative, the number of secure systems increased to 18, comprising 55 images. Three of these systems have been spectroscopically confirmed, with redshifts of 3. 98, 3. 58 and 2. 019 for systems 3, 4, and 6, respectively (see  Richard et al. 2014; Johnson et al. 2014). For the present study, we scrutinised the new, deep HFF ACS and WFC3 images, using the predictive power of the  Richard et al.
Comparison between pre-HFF & HFF CATS models The main discrepancy between pre-and post-HFF data models lies in the derived magnification maps. The following quoted magnification values are sampled from the magnification field at the particular positions, and best-fit redshifts of a small subset of lensed galaxies. Using a set of 13 multiple images common to pre-HFF and HFF data models, the pre-HFF model of R14 (including the corrected System 3, and thus featuring four cluster-scale halos) provides a median magnification of 3. 56±0. 14, with a position RMS of 0. 44. When the N and NW substructures are removed, and therefore only two cluster-scale halos are included, the same pre-HFF model yields a median magnification of 4. 69 ± 0. 32 (RMS=0. 47 ). We use this comparison to obtain an estimate of the systematic uncertainty in the mean magnification due to the model of 0.
The significant increase in magnification provided by our high-precision model is equally unlikely to originate from the core region, where the much deeper HFF data have enabled us to correct several misidentifications of multiply imaged systems, and thus to create a simpler mass distribution. Moreover, we have demonstrated that the removal of the third cluster-scale halo, used by R14 to model the cluster core, improved both pre-HFF and HFF mass models (see Table  2 ). Therefore, this third mass component cannot explain the observed discrepancy either. Finally, the HFF data have allowed us to identify 34 new systems in the core of Abell 2744, providing a highly constrained mass model of this central cluster component. These new identifications have the strongest impact in the north-western region of the cluster core where previously almost no multiple-image systems had been identified, thus leaving this part of the cluster almost unconstrained and subject to extrapolation from other regions of the core.
In Fig. 1  we highlight the pre-HFF multiple-image systems in dark blue; counter-images for systems 3, 8, 14, and 18 as well as several entirely new systems allowed us to map the mass distribution in this region much more accurately (see Fig. 1 ). We also extended the region within which multiple-image systems were found in the core farther toward the south. As shown in Fig. 3 , the mass density in this southern region is higher than predicted by the pre-HFF models, resulting in higher magnification in this part of the core. In conclusion, the plethora of new multiple-image systems discovered in the HFF data has led to significantly tightened lensing constrains in particular across the entire cluster core. The resulting set of constraints span the cluster core more comprehensively. Thereby we have demonstrated that the HFF model presented here is more precise than all previously published models for this cluster.
42 arcmin 2 for the HFF model using the set of multiply-imaged systems from  Wang et al. (2015). Lower values are measured using the pre-HFF models, σ(µ > 3) = 0. 36 arcmin 2 for the pre-HFF model with 4 cluster-scale halos, and σ(µ > 3) = 0. 29 arcmin 2 for the pre-HFF model with cluster-scale halos. For MACSJ0416, we quote σ(µ > 3) = 0. 26 arcmin 2 from  Jauzac et al. (2014) , almost a factor of two less. Following the trend of the HFF MACSJ0416 strong-lensing results, our present analysis of Abell 2744 demonstrates the power of HFF data to impressively reduce the statistical errors of both mass and magnification measurements without any changes in the analysis neither the modeling techniques employed. In the case of Abell 2744, the threefold increase in the number of multiply imaged systems afforded by the exquisite depth and quality of the HFF data improved our estimates of the uncertainty by a factor of ∼20 and ∼4 for mass and magnification, respectively.
ACKNOWLEDGMENTS We would like to thank the anonymous referee for his/her constructive comments and suggestions. JR acknowledges support from the  ERC  starting grant CALENDS and the  CIG  grant  294074. This work was supported by the  Science and Technology Facilities Council  [grant number  ST/F001166/1 ] and used the DiRAC Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk [www. dirac. ac. uk]). This equipment was funded by  BIS National E-infrastructure capital  grant  ST/K00042X/1 ,  STFC  capital grant  ST/H008519/1 , and  STFC DiRAC Operations  grant  ST/K003267/1  and  Durham University. DiRAC is part of the  National E-Infrastructure. MJ, EJ, and ML acknowledge the  Mésocentre d'Aix-Marseille Université  (project number: 15b030).
This study also benefited from the facilities offered by  CeSAM (CEntre de donnéeS Astrophysique de Marseille  (http://lam. oamp. fr/cesam/). ML acknowledges the  Centre National de la Recherche Scientifique (CNRS)  for its support, and the  Dark cosmology centre , funded by the  Danish National Research Fundation. EJ and ML acknowledge the  Centre National d'Etude Spatial (CNES)  for its support. JPK and HA acknowledge support from the  ERC  advanced grant LIDA. PN acknowledges support from the  National Science Foundation  via the grant  AST-1044455 ,  AST-1044455 , and a theory grant from the  Space Telescope Science Institute   HST-AR-12144. 01-A. RM is supported by the  Royal Society. Support for SAR was provided by  NASA  through  Hubble Fellowship grant  # HST-HF-51312. 01  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc.
It can be useful in plenty of domains like biomechanics, ergonomics, diagnosis, treatment planning, visualization, graphics, robotics, or, for example, helping deduce the anatomic consequences of an injury  [24]. Kerwin et al. have developed a three-dimensional atlas of the human embryonic brain using anatomical landmarks and gene expression data to define major subdivisions through 12 stages of development (Carnegie Stages 12-23). Virtual 3D anatomical models were generated from intact specimens using optical projection tomography. The 3D models and a preliminary set of anatomical domains and ontology are available on the atlas pages along with gene expression data from approximately 100 genes in the HUD-SEN Human Spatial Gene Expression Database  [25, 26]. Gasser  [27]  developed the Virtual Human Embryo  [28] , which is an atlas of digitally captured images of serial embryo sections corresponding to each of the 23 Carnegie stages.
Then, they were assembled to provide 3D images and holistic views of development. Moreover, this database is accessible to all researchers, teachers and students and can be used acknowledging the source of the images, i. e. , Virtual Human Embryo DREM Project. Full exploitation of ontologies goes far beyond providing standardized notations for explicit formalized knowledge on domains of interest. It requires equipping ontological statements with reasoning and querying capabilities in order to efficiently retrieve relevant and precise information. However, until now, only few existing works in biomedical ontologies (e. g. ,  [21] [22] [23] [24] ) take advantage of available automatic reasoners. Most of these works rely on ontologies expressed in OWL and use OWL reasoners such as Pellet  [29]  or Racer [30] that come with a high computational complexity in the worst case (as they are Description Logics reasoners).
Recently, RDF-based semantic environments such as Jena (http://jena. apache. org/) or Cwm (http://www. w3. org/2000/10/swap/doc/cwm) have included rules (a la Datalog) to perform inferences on top of RDF datasets. Datalog rules  [31]  and Description Logics  [32]  are two orthogonal decidable fragments of first-order logics that have been extensively studied in knowledge representation and in deductive databases. The interest of Datalog rules is that they are easy to read and write for practitioners and they have a polynomial data complexity while allowing expressing complex interaction between properties and recursivity. In addition, as it will be shown in our examples, rules allows to capture in a uniform manner OWL constraints that are useful in practice, such as property transitivity or symmetry, but also domain-specific rules with practical relevance for users in many domains of interest.
Our ontology is expressed in RDFS  [33]  which is broadly used in Linked Data  [34]  to express so-called light-weight ontologies with a rule-based semantics. Whereas OWL is often seen as an extension of RDFS, this is not exactly the case, mainly because RDFS allows to use the same identifier as a class, instance or property, which is not possible in Description Logics (at the basis of OWL profiles), in which the sets of instances, classes and properties must be disjoint. In addition, RDF allows to use blank nodes in class or property position. Similarly, the RDF query language SPARQL can query simultaneously the data and the schema and allows variables to stand for classes and properties. This goes beyond the first-order conjonctive queries typically considered in Description Logics. It is worth emphasizing that OWL and RDFS can interoperate, as there exists an RDF triple notation for most of the OWL constructs (in particular the OWL2 profiles), and the corresponding axioms can be expressed as logical rules.
To achieve this, we created different categories that describe entities' geometrical states and evolution, and a set of properties to link the different parts together. Secondly, we developed a software tool that generates a 3D graphical scene representing the embryo development. To do so, it queries the ontology and extracts all information necessary for modeling, visualizing and simulating the 3D scene. We focused on the development of the urinary system. We believe this system to be a good study case as its development involves a diversity of mechanisms that are representative of other evolution processes and a large variety of diseases caused by anomalies of these processes. We used the TopBraidComposer [40] tool which allows the creation and edition of lightweight ontologies in RDF format  [41]  format lightweight ontologies, and to query them using SPARQL  [42].
RDF and SPARQL are standards recommended by the W3C for the semantic Web and Linked Data \footno-te{http://linkeddata. org/}. In addition, TopBraidComposer allow the addition of inference rules on top of RDF datasets and supports the application of these rules on RDF facts until saturation (i. e. , until all the possible facts that can be inferred using the Fig. 15  3D geometrical modelization of the cloaca's division process is driven by the ontology rules have been obtained). RDF datasets equipped with rules allow the capture of most of OWL \cite{owl} constraints that are useful in practice, such as the transitivity or symmetry properties, as well as domain-specific rules with practical relevance for users in many domains of interest. The 3D graphical tool was developed using the 3D open source software Blender  [43].
All the 3D surfaces where procedurally handled using Python [44] scripting within the Blender framework. The ovoids were handled as Blender parameterized primitives. The implicit surfaces were represented as a set of points along the trajectory of the ducts while the field function and the representative mesh of the isovalue was handled by Blender software. Finally, the shape representing the boundary of the embryo was created interactively using Blender interface. All the coordinates parameterizing the shapes and its animation were expressed locally with respect to the embryo main axes and size. The coordinates were queried within our Python script to be directly usable within the Blender framework. Our ontology is public available: [https://mybody. inrialpes. fr/mycfembryo/].
G-protein coupled receptors (GPCRs) are the targets of over half of all prescribed drugs today. The UniProt database has records for about 800 proteins classified as GPCRs, but drugs have only been developed against 50 of these. Thus, there is huge potential in terms of the number of targets for new therapies to be designed. Several breakthroughs in GPCRs biased pharmacology, structural biology, modelling and scoring have resulted in a resurgence of interest in GPCRs as drug targets. Therefore, an international conference, sponsored by the Royal Society, with world-renowned researchers from industry and academia was recently held to discuss recent prog-.
The recent introduction of a new generation of x-ray sources, x-ray free electron lasers (XFELs), producing ultra-bright pulses of coherent x-rays with an ultrashort duration, holds the promise to advance our Fig. 2  Crystal structures of β 1 AR bound to novel chemotypes developed by fragment screening and hit optimisation. Structures of the ligand binding pocket are depicted with only portions of selected transmembrane helices (H3, H5, H6 and H7) shown with the side chains (green) and ligands (yellow) depicted in sticks: a cyanopindolol, PDB code 2VT4; b compound 19, PDB code 3ZPQ; c compound 20, PDB code 3ZPR-figure adapted from the publication  (Christopher et al. 2013)  understanding of structure and function of these challenging targets. A novel approach using a membrane mimetic gel-like matrix known as lipidic cubic phase (LCP) for growth and delivery of membrane protein microcrystals for data collection by serial femtosecond crystallography (SFX) at XFELs  (Liu et al.
The data sets have now been completed. The pharmacological profile of each individual was assessed several times. Currently, data are analysed in multiple ways. Specifically, the impact of specific β 2 AR polymorphisms on the pharmacological profile is assessed. Additionally, the impact of sex, age, smoking and allergy history on β 2 AR pharmacology is evaluated. The study fills a gap in the field because it provides data on the pharmacological properties of GPCR polymorphisms in a physiologically relevant context. The results of the study will be submitted for publication to a peer-reviewed journal in spring 2015 orthosteric (endogenous ligand) sites are co-occupied may be different than when either site is individually occupied. Biased signalling is particularly relevant to receptor systems that have multiple endogenous ligands, and where exogenous mimetics are used clinically, as is often seen with class B receptors.
Networks of interactions and disease 'interactomes' can be built with the ultimate goal to understand disease networks and how they are influenced by the changes in small molecules and their properties. The polypharmacology associated with current typical and atypical anti-psychotics is complex, and as an example, the question of how do we go about designing a novel anti-   (Fechner et al. 2013 ) was introduced as an interactive way to review the rich pharmacology accessible in the ChEMBL database and identify some good starting points for drug design. The experimental polypharmacology associated with the hits can be complemented using target prediction ligand similaritybased approaches such as the similarity ensemble approach (SEA;  Keiser et al. 2009)  or broad panel-based predictive modelling approaches  (Ghosh and Jones 2014).