dataesr/scientific-paragraphs-categorization huggingface dataset:
4. 3. Only entries with parallax uncertainty ≤ 1. 5 mas in the Hipparcos catalogue are used for the comparison below, consisting of 86 928 entries in the primary data set. Values from the Hipparcos catalogue are denoted with subscript H, those from the primary (TGAS) data set by T. In all comparisons we first consider the global differences, i. e. including all sources irrespective of their position, colour, and other characteristics. It should be kept in mind that the resulting statistics are indeed only valid on a global level. The data are in general very inhomogeneous, and as soon as they are broken down according to position, colour, etc. , a much more complex picture emerges with sometimes much stronger systematic differences and locally higher dispersions. In this section we focus on the dependence on position (i.
e. regional systematics) and, to some extent, on colour. The median differences, especially in right ascension, show a markedly larger scatter in the ecliptic region than in other parts of the sky. This is partly explained by the lower number of sources per pixel in that region, but mainly reflects the variation of Hipparcos proper motion uncertainties with ecliptic latitude. The propagated Hipparcos positions are clearly not good enough to validate the TGAS positions on a small scale, but do not indicate any large systematics on a semi-global scale. For example, the median differences computed separately for octants of the celestial sphere differ from the global value by at most 1 mas in ∆α * and 0. 6 mas in ∆δ. A stricter validation of the TGAS positions is possible by means of VLBI data (Sect.
For Gaia, this will be remedied in future data releases. In the meantime, it is important to characterise the correlations that exist in Gaia DR1. ρ p f (τ) = p i f j σ p σ f , (D. 1) where p i and f j are the residuals of observations in the preceding and following field of view, respectively, and the average is taken over all residual pairs for which the time difference t it j is τ (to the nearest second). The normalisation factor is σ p σ f = 0. 38 mas 2 , from which the cross-covariance can be recovered. 13  The cross-correlation function exhibits the characteristic pattern expected from modelling errors in the attitude spline. Given that the knot separation is 30 s, it may seem surprising that the zero-crossings have a typical separation of only about 20 s; however, this is expected for an attitude spline fit using a small value of the regularisation parameter λ (Sect.
1992 , Michalik & Lindegren 2016). Further details will be discussed elsewhere. Special algorithms and software packages to recover both basic-angle and differential variations have been developed and tested in AGIS. This software will be used in the astrometric solutions of future data releases to mitigate these effects. The software was however not used for the current baseline solution, for which we instead assume that the BAM provides adequate corrections. Nevertheless, for validation purposes we have made TGAS runs where the harmonic coefficients C k,m , S k,m are estimated as global parameters for k = 1. 8 and m = 0, 1 (but excluding C 1,0 ). The time interval covered by the current data is not long enough to reliably estimate the linear time-dependent coefficients (m = 1). Results for the time-independent coefficients (m = 0) are shown in Table  E.
Acknowledgements. This work has made use of data from the  ESA space mission Gaia , processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The Gaia mission website is http://www. cosmos. esa. int/gaia. The authors are members of the Gaia DPAC. This work has been supported by:  MINECO  ( Spanish Ministry of Economy) -FEDER  through grant  ESP2013-48318-C2-1-R  and  ESP2014-55996-C2-1-R  and  MDM-2014-0369  of  ICCUB (Unidad de Excelencia 'María de Maeztu '); the  Netherlands Research School for Astronomy (NOVA) ; the  German Aerospace Agency DLR  under grants  50QG0501 ,  50QG1401 50QG0601 ,  50QG0901  and  50QG1402 ; the  European Space Agency  in the framework of the  Gaia  project; the  Agenzia Spaziale Italiana (ASI)  through grants  ASI I/037/08/0 ,  ASI I/058/10/0 ,  ASI 2014-025-R.
Introduction SOFTEAM is a French middle-sized company that provides the Modelio modelling tool. Modelio. 1 is an enterprise-level open source modelling solution delivering functionality for business, software and infrastructure architects. It is a comprehensive MDE workbench tool supporting the UML2. x standard. Modelio provides a csentral IDE which allows various languages (represented as UML profiles) to be combined in the same model. Modelio proposes various extension modules, enabling the customization of this MDE environment for different purposes and stakeholders. The Team Work Manager is SOFTEAM's solution to team collaboration in Modelio. It allows Modelio users, after a minimal software and hardware investment, to efficiently share and work together on models stored in a central repository accessible in a local network or in the Internet.
It automates version control and configuration management, making sure every developer has access to the last version of the shared model and works on a uniform configuration. From the point of view of the developer, a repository is divided into Projects, which contain: Model elements, Extension modules used by the user and Configuration information. A repository needs to be installed, configured and maintained by the users in private machines. A SVN repository may store different projects and different teams may work in the same repository. Developers use the Modelio desktop client to access a central repository on a SVN like workflow: committing modifications to model elements, receiving updates from other users and using merges/locks to deal with concurrent work. By its participation on the MODAClouds project, SOFTEAM intended to move its modelling services to the Cloud in order to relieve the burden for our clients in supporting the necessary infrastructure.
Proposed Architecture In order to simplify this migration, the architecture of our Cloud solution relies on the implementation of a component called Administration Server (Fig. 12. 1). The Administration Server allows clients to create and manage user accounts, define roles, and create modelling projects and associate users and roles to specific projects. The Administration Server is designed as a JEE application which provides a web accessible user interface support implemented with Java Server Faces 2 and service behaviour supported by Entity Java Beans components. This application is linked with an relational database to ensure persistency of application data. The Administration Server can provision computing resources in order to maintain the established level of quality of service. Cloud Services managed by a Administration Server are delivered as Cloud-enabled applications.
These applications are deployed on the provisioned Cloud resource. Once deployed in Cloud resources, services usually need to be configured and accessed by clients. The Administration Server needs to make sure that the necessary projects, users and permissions have been created and set up once a Cloud agent has been installed. Standard protocols are used for both activities. Web Services enable the deployed agents to be configured. Moreover, TCP/IP protocols will allow Modelio desktop based clients to connect to an agent, independently from which Cloud it has been deployed. The only dependency of this design to the specific Cloud provider is the communication between the Administration Server and the Cloud provider in order to deploy, monitor and eventually migrate services. The actual code to interact with the Cloud provider is however encapsulated in a Web Service usually installed on the Administration Server.
Modelling with Creator 4Clouds We used MODAClouds Creator 4Clouds Functional Modelling tool to describe the architecture of Constellarion's Administration Server along with its modelling services. We have also used this model as input to other design and runtime tools. During the first MODAClouds phase we considered two kinds of services: SVN and HTTP At the highest level, the CCIM shows the services that compose Constellation: the Administration Server and the Administration Database connected by an interface provided by the Administration Database and required by the Administration Server. Still at the CCIM level, Fig. 12. 3 shows the QoS constraints associated with the most important operations provided by the Constellation modelling services. For Fig. 12. 3 CCIM QoS constraints on MODAClouds IDE fragments, 15 s is the target average time for reading model modifications, and 60 s is the target average time for writes.
This is a typical example of advanced deployment configurations Constellation needs to support. Our experiments showed that the Conference Service is more scalable than the current solution. The Fig. 12. 6 presents the usage model of our users, obtained through observation of typical users. It considers users that connect to modelling through their full workday. Five percent of the time they interact with Constellation, they connect to an existing project, which is translated onto the sequence of calls we see on the top of the figure. Ten percent of the time, they read updates from an SVN model, seventy-five percent of the time they get data from HTTP fragments and ten percent of the time they perform SVN commits. In addition to usage models, we provided models of user workload throughout the day (see the Fig.
The Monitoring Platform allows us to monitor specific metrics collected from business components of our case study deployed on different Cloud platforms. To achieve this goal, we integrated five components into our architecture: three componentsfrom the monitoring platform and two components developed using the API provided by platform components. The role of these is to exploit monitoring data in our application. To exploit the monitoring platform, we have integrated two components based on the API provided by the monitoring platform. These components ensure the intermediation between the monitoring platforms and business components of Constellation. They allowed us to implement a Cloud vendor independent agent monitoring user interface, and to integrate it to our commercial offering. • Constellation Data Collector: To collect business metrics from Constellation agents, we integrated into our architecture this extension of the monitoring platform.
INTRODUCTION The ribonucleic acid (RNA) plays fundamental roles in biology, including the transmission of genetic information, regulation of gene expression and catalysis of biochemical reactions  (1). Many RNA molecules or their parts (domains or motifs) fold into stable three-dimensional (3D) structures that define, at least partially, their ability to interact with other molecules and carry out their tasks within the cell  (2). However, experimental determination of RNA 3D structures is laborious and challenging, and the majority of known RNAs remain structurally uncharacterized. Although a limited number of experimentally obtained RNA 3D structures are available in the Protein Data Bank (3), there are a multitude of RNA sequences [e. g. in the RNAcentral database  (4) ] for which no 3D structure exists.
It can W316 Nucleic Acids Research, 2016, Vol. 44, Web Server issue predict RNA 3D structure from sequence alone, and, if available, can use additional structural information in the form of secondary structure restraints, distance restraints that define the local arrangement of certain atoms, and can jump-start the simulation with a 3D structure provided in a PDB file. Thus far, SimRNA has been available as a standalone package that required the user to have good computer skills and a powerful computer. Here, we introduce a web service that simplifies the steps of the stand-alone package, does not require the user to supply computing power and memory, provides a simple interface for the user, and displays the progress of the simulation in real time. This renders the approach available to an individual who is not necessarily an expert in RNA structure and does not have access to state-of-the-art 3D molecular modeling facilities, but who needs a model of the RNA 3D structure, for instance to design biochemical experiments, or may want to observe the conformational changes of the RNA as it folds.
Training (parametrization) and testing datasets The structure prediction engine and the default parameters of the simulation are currently (February 2016) identical to those of the published version of SimRNA version 3. 20  (14). Only one minor correction has been introduced to define the set of atoms frozen if a residue is specified as frozen. Sim-RNA v. 3. 20/1 was benchmarked against a variety of test structures including the dataset from Ding et al. (17) , the 2008 Das&Baker dataset  (18) , the 2010 Das&Baker dataset of RNA structural motifs  (19) , the Seetin&Mathews dataset  (20)  and published structures available from RNA puzzles  (21, 22). With the exception of the Das&Baker motifs dataset, which required restraints on the bordering parts of the crystal structure to do the study, all the sequences were tested both in de novo simulations and using various levels of restraints including secondary structure and distance restraints.
Example applications Figure  2A  illustrates an example of RNA 3D structure prediction with SimRNAweb, based on sequence information alone (without secondary structure or any other restraints). An experimentally determined molecule of RNA tertiary domain essential to HCV IRES-mediated translation initiation comprises two chains from the crystal structure (PDB id. : 1kh6). SimRNAweb run with default parameters folded it to a model that matched the reference structure very well (correct secondary structure, correct orientation of all structural elements, and RMSD of 5. 9 Å). The additional application of secondary structure restraints improves this model to 4. 6 Å (data not shown). Figure  2B  shows a predicted 3D structure for tRNA(Phe) from Escherichia coli (73 residues), for an input comprising sequence, secondary structure, and distance restraints.
ACKNOWLEDGEMENTS The authors want to thank  Yucong Zhu  and current and former members of the  Bujnicki group , in particular  Pawel Piątkowski ,  Catarina Almeida ,  Filip Stefaniak ,  Krzysztof Szczepaniak ,  Stanislaw Dunin-Horkawicz ,  Radoslaw Pluta ,  Shamba Sankar Mondal ,  Magdalena Zieli  ńska, - Lukasz Kozlowski Malgorzata Kurkowska ,  Grzegorz Chojnowski , and  Grzegorz -Lach , for their helpful comments and/or testing the server, and to all SimRNAweb users who provided feedback.
Data obtained from batch studies are heavily influenced by factors such as initial solution concentration  (Drizo et al. , 2002) , pH  (Cucarella and Renman, 2009) , and contact time  (McKay, 1996) , as well as by experimental conditions which are sometimes very different to real world applications -for example the use of unrealistic solid-to-solution ratios, and the agitation of batch samples  (Ádám et al. , 2007; Søvik and Kløve, 2005). Adsorption isotherms may also be determined using flow-through experiments, and the utilization of such methodologies, while not offering a 'solve-all solution', may help to address many of the shortcomings associated with batch studies. Buergisser et al. (1993)  note that the solid-to-liquid ratio used when determining sorption isotherms with flow-through experiments is much closer to ratios that might be found in real-life systems where the adsorption of contaminants plays an important role in the treatment of contaminated waters (e.
Processing of experimental data During each loading event, the effluent from the filter units was collected in large HDPE containers and was analyzed for PO 4 -P using a nutrient analyzer (Konelab20, Thermo Clinical Lab systems, Finland) after APHA (1998). The mass of phosphorus adsorbed per unit mass of filter media was calculated using Eqn. 1, and a graph of cumulative q e versus cumulative collected effluent volume, V, was constructed. The observed values of q e were then compared to values predicted by models created using coefficients calculated from the data collected during small-scale adsorption column experiments.
Verification of model's adaptability to variances in operating conditions In their 2013 paper, "Removal of phosphorus, fluoride and metals from a gypsum mining leachate using steel slag filters", Claveau-Mallet et al. (  2013 ) provided a supplementary file containing concentration and pH data for effluents from columns which were being used to treat reconstituted gypsum mining leachate. In the study, a total of ten columns were operated as five sets of two columns connected in series. These were continuously loaded with leachate for a period of 145-222 days, and effluent concentrations of fluoride (amongst other contaminants) were measured twice per week. Four of the columns were loaded with a low concentration leachate (L1), while the remaining six were fed with a higher concentration leachate (L2).
The effluents of those columns fed with leachate L2 showed variations in pH of 4. 15±2, while those of columns fed with leachate L1 showed variations of 1. 29±0. 67. Due to the effect that the large fluctuations in the pH of effluent from columns fed with leachate L2 had on the data, the model proposed in the current study was fit to the effluent fluoride concentration data from the columns fed with leachate L1. The columns were loaded continuously with an influent that had a fluoride concentration of 9 mg L -1. Effluent fluoride concentrations were measured twice weekly, and using these data, averages of effluent concentration were used to determine the amount of fluoride removed by the filter media (q e ). Given that the columns were loaded at a continuous rate by a peristaltic pump, the volume of leachate filtered (V) was taken to be analogous to the time (T) for which the columns were running (as V= αT, where α is the flow rate in L day -1 ), and a plot of q e versus V was developed from which the model coefficients, A and B, were computed from plots of Eqn.
Revised estimates of model coefficients were determined from experimental data collected at twohour increments, and a model using these coefficients was fitted to experimental data for the entirety of the period for which the filter was loaded. Figure  S2a  shows the HYBRID error of these models, against the time at which the model coefficients were determined. After an initial sharp drop-off in model error, there was little improvement in model fit past the 24hour mark. Figure  S2b  shows the goodness of fit to experimental data of models using coefficients determined after 24 and 36 hours of data aggregation, demonstrating that there was little benefit to be gained from experimental run times in excess of 24 hours. To study the accuracy with which effluent concentration, average daily effluent concentration, and total P loss could be predicted, a small-bore adsorption column containing media A was operated for an extended period of 156 hours.
Columns 1A, 1B, 4A, and 4B (Figure  4 ) had HRTs of 14. 2, 18. 2, 4. 8, and 4. 3 hours, respectively, though these variations in HRT appear to have had no significant impact on the goodness of model fit. Columns 1A and 4A were both loaded using wastewater with a constant fluoride concentration of 9 mg L -1 , while columns 1B and 4B were loaded with the effluents from columns 1A and 4A, thus receiving an influent with fluoride concentrations ranging from 0. 85-7. 66 and 5. 86-8. 63 mg L -1 , respectively. In general, higher MPEs were recorded for models fit to data from columns fed by an influent of varying concentration, and a greater variance in influent concentration resulted in a larger MPE. This would seem to indicate that variation in influent concentration had a negative impact on model fit, though with a maximum recorded MPE of 12.
The centers of mass are calculated from segmented puncta objects as defined by a thresholded, three-dimensional (3D) connected component analysis (see 'Methods'). Centers of mass are used in our calculations to facilitate computation of the over 9 million protein puncta collected across the 13 synaptic proteins in our ATD volumes (Figure 1-figure supplement 1; see 'Methods' for further description of the datasets). These synaptic vectors, which are oriented in many directions in the tissue volumes (Figure  2A ), are computationally aligned into one coordinate system, creating a registered synaptic volume (Figure  2B  and C, see 'Methods'). This volume contains the aligned centers of all protein puncta located within a 1 mm radius of the origin (PSD95 weighted center) of each synaptic vector (Figure  2C  and D ).
, 2012; Mineur et al. , 2002). To further simplify the visualization and statistical comparison of these distributions, the 2Dprobability histograms are rotationally projected to a onedimensional (1D) plot (Figure  4A ). In this plot, the origin is the PSD95 centers of the nearly 40,000 VGluT1 synapses registered in the dataset. The putative synaptic cleft (red dashed line, Figure  4 ) is 50 nm from the origin, as estimated from previous EM and super-resolution measurements  (Dani et al. , 2010; Mineur et al. , 2002). Assessing the plot, the VGluT1 distribution dips in the postsynaptic zone, demonstrating the expected anti-correlation of VGluT1 localization to the post-synapse. Furthermore, the distribution peaks at ~125-200 nm from PSD95 or about 75-150 nm from the cleft (Figure  4A ). These measurements corroborate previous analysis of presynaptic vesicle pools using EM  (Mineur et al.
It is satisfying to see in our datasets that in both Layers 4 and 5, VGluT1 synapses have little VGluT2 protein and vice versa (Figure  5A     protein visualization. A point that is further validated by the near-random distribution of VGluT1 and VGluT2 proteins in inhibitory synapses (Figure  5C  and Figure  5 -figure supplement 1C), which illustrates that even for a densely expressed protein, such as VGluT1 (Figure  1 ), the method is robust and accurate enough to reveal the lack of association between that protein and the inhibitory synaptic axes. Upon quantifying the distributional differences of proteins across Layers 4 and 5 of these three synapse classes, there are no significant differences between the two layers when comparing the same protein within the same synapse class (Figure  5  and This demonstrates that the overall synapse architecture of excitatory synapses remains stable between classes and layers, and that inhibitory synapse architecture is stable between layers as well.
, 2011; Huber et al. , 2002). Interestingly, in Layer 4 synapses only, a second FMRP peak can be detected presynaptically (Figure  6A  and B, black arrows). This peak is not pronounced in the empirical distribution of Layer 5 synapses (Figure  6A  and B ), which suggests that although post-synaptic FMRP localization is a general feature, the bulk of presynaptic FMRP is directed to Layer 4 rather than to Layer 5. Validation of this result was confirmed using EM conjugate AT  (Collman et al. , 2015). Four datasets were generated (for Layers 4 and 5 of both WT and Fmr1 KO mice), each covering a volume of 60 mm x 10 mm x 210 nm and consisting of both EM and light level immunohistochemistry (VGluT1, FMRP and FXR2). Using hand classification, we identified 115 WT Layer 4 synapses, 199 WT Layer 5 synapses, 113 KO Layer 4 synapses and 118 KO Layer 5 synapses.