dataesr/scientific-paragraphs-categorization huggingface dataset:
We developed a benchmark for assessing the performance of the structural superpositions and the resulting sequence alignments by way of various measures. We evaluated the performance of ten available programs, based on experimentally determined structures of homologous RNA molecules. In addition to structural measures (RMSD and derivatives), we used sequence-based scores (e. g. , SPS) as measures of the accuracy of structural alignments. These measures assess the similarity of the superposition-derived sequence alignments returned by the benchmarked programs and reference alignments generated from manuallycurated covariance models from the Rfam database. Moreover, we look into the structural characteristics that account for the differences in accuracy and identify the features that may be important for the interpretation of a superposition obtained with the benchmarked tools.
A new method of superposition We developed a new method, called SupeRNAlign, for flexible pairwise superposition of RNA 3D structures. SupeR-NAlign iteratively superimposes the RNA structures and splits them into fragments to maximize the local fit. In this work, we define a fragment of RNA 3D structure as a set of at least four ribonucleotide residues, physically connected with each other (covalently or non-covalently), which is used as an independent unit of structural superposition. A detailed illustration of SupeRNAlign workflow is presented in Figure  1. As an input, SupeRNAlign takes two RNA structures in the PDB format (further called the 'reference structure' and 'aligned structure', respectively). The first superposition is performed on the entire structure. SupeRNAlign may split the aligned structure into fragments, using a special procedure implemented in a software tool ClaRNet developed in our laboratory (see the following section).
Successfully superimposed fragments (or sub-fragments) of the aligned structure are frozen and merged with fragments frozen in the earlier iteration(s). When all the fragments have been processed, the global alignment and the list of pairwise distances for all residues are updated. After each iteration, the exit condition is tested: if all residues have been frozen or the arrangement of fragments has not changed since the last iteration, the processing ends and structural superpositions and sequence alignments are returned. A user can obtain the final solution as well as all intermediate superpositions and corresponding alignments--though this is not a default option. To further improve the quality of sequence alignments yielded by SupeRNAlign, we enabled its coupling with T-Coffee  (26) , which uses structural alignment data produced by our program to generate a T-Coffee library file--similarly to SARA-Coffee  (13).
The interactions recognized by ClaRNA include canonical and non-canonical base pairing, stacking, basephosphate and base-ribose interactions. The detected contacts are then evaluated and those with a ClaRNA score above a threshold of 0. 6 are considered further. ClaRNA also takes into account all covalent bonds between residues adjacent in sequence. The RNA molecule at this point can be represented as a graph, with residues as nodes and interactions as edges (Supplementary Figure  S1 ). Since the purpose of the program is to break selected covalent bonds between regions that are well connected, due to canonical base pairs and other interactions, the procedure assigns variable weights to different types of edges: 0. 5 for covalent bonds, 2 for canonical base pairs and 1 for all other interactions.
The key step in the procedure is the identification of the most densely connected clusters of residues using the Markov Cluster Algorithm  (29)  with the inflation parameter set to 1. 3, which has been established by a trial and error method. The final result is the division of the input structure into substructures; in the context of this work, these substructures are passed to SupeRNAlign for further processing, and can be subdivided further, based on the algorithm described earlier. A full list of ClaRNet parameters is provided in Supplementary Material (Supplementary Table  S6 ). An example of superposition of two tRNA structures by SupeRNAlign is presented in Figure  2. A more complex example--a pair of 16S rRNA molecules (PDB codes: 1FJG and 2AW7) -is presented in Supplementary Figure  S5  (graph for 2AW7) and Supplementary Figure  S6  (Su-peRNAlign workflow).
Benchmarked programs From the available programs for RNA structure comparison, we selected those that yield a superposition (either PDB file(s) or rotation/translation data) and/or a sequence alignment given two PDB files with single-chain RNA structures. Therefore, DIAL could not be tested because it did not meet these requirements (the Web server was available online, but in our hands it has not returned any results). Likewise, PRIMOS, which was mentioned in the Introduction, is available neither as a downloadable program nor as a Web server. Although RASS was available at the time of writing this manuscript, in our hands it was unable to return results for any of the pairs from the benchmarking set. We have informed the authors of RASS and DIAL about the inability to obtain results of our superpositions.
According to the authors' responses, these problems could not be solved in reasonable time, and hence both programs were excluded from our comparison. Table  1  presents the tools included in the benchmark with a list of algorithms employed by each of them. Eight of them are available as standalone applications, running on Linux and usually also on MS Windows (especially those written in multi-platform languages, like Python or Java). Two tools (iPARTS and Rclick) are available only as Web servers; in these cases, we wrote scripts for parsing results directly from the respective websites of the servers. R3D Align, SARA-Coffee, and SupeRNAlign are available both as standalone tools and as Web servers.
Benchmarking set We created groups of homologous and non-redundant RNA structures, using two datasets: Rfam (30) and RNA 3D Hub  (31). Two RNA structures were considered homologous if and only if their sequences belong to the same Rfam family, and non-redundant if they are not members of one class at the given resolution cut-off in RNA 3D Hub. First, we downloaded a full set of RNA 3D structure classes defined in RNA 3D Hub version 1. 89 at a resolution cut-off of 4. 0 Å. We used a dataset with reduced redundancy to retain only biologically relevant variation among the RNAs analyzed and to ignore variation due to different experimental conditions. Then, for each PDB code representative of a class, we downloaded the corresponding PDB file from the PDB  (32). From a total of 876 classes, we selected only those which could be used for the purpose of this benchmark; i.
e. , were not a sole structurally characterized member of an RNA family. All RNA sequences (after replacing modified residues with their unmodified counterparts with ModeRNA) within each PDB file were extracted using ModeRNA libraries  (27). Identical sequences within one PDB file were removed, and for each RNA sequence, a Rfam family was matched using cmscan from the Infernal 1. 0. 2 package (33) on Rfam covariance models. In the case of multiple matches, the family with a longer matching region or broader definition was assigned. Multiple sequences within one Rfam family were aligned with Infernal's cmalign and the resulting alignments were considered as true references. In the case of 16S rRNA, we manually searched for all non-redundant structures at http://www. rcsb. org and aligned their sequences using SSU-ALIGN (http://eddylab.
Programming tools The scripts used in this benchmark were written in Python (data collection) and R (statistical analysis and visualization). A 64-bit virtual machine with Ubuntu 14. 04 e150 Nucleic Acids  Research, 2017, Vol. 45, No. 16  PAGE 6 OF 10 (GNU/Linux 4. 4. 0-62) onboard and Python 2. 7. 6 and R 3. 2. 3 installed was employed to run the benchmark. For the processing of PDB files, Biopython 1. 65  (36)  and Mod-eRNA 1. 7. 1 Python libraries were used.
Therefore, other measures of RNA similarity should also be considered. Sum-of-pairs score (SPS) is dependent on the sequence alignments generated from 3D superpositions; i. e. , it measures the quality of sequence alignments obtained from 3D superpositions, as compared to the reference ones  (38). SPS was used in previous benchmarks  (39, 40)  and yields a normalized similarity between two alignments (from 0 for completely distinct alignments to 1 for identical alignments), iterating over columns and scoring each column that is identical in both alignments. SPS counts the correctly aligned residue pairs and is commonly used in assessing the performance of multiple sequence alignment algorithms. Here, as the references, we used sequence alignments based on covariance models for RNA families deposited in the Rfam database  (30).
Given a pair of the test and reference sequence alignment, we calculated sum-of-pairs score (SPS)  (41). Another score, 3SP (Secondary Structure Sum of Pairs) (42), is similar to SPS in that it calculates the number of pairs matching the reference alignment, but also secondary structure of the aligned RNAs is taken into account: the alignment is penalized for each Watson-Crick pair in the second structure not matching a pair in the first structure (i. e. , paired residues not aligned together). In this case, X3DNA (43) was used to determine secondary structures. Sequence alignments were inferred from superimposed structures using the pdb3aln software tool developed in our laboratory (available as a part of the SupeRNAlign software package), based on a modified Smith-Waterman algorithm. In pdb3aln, the penalty is calculated as the reciprocal of the distance between each pair of residues from both RNA structures.
9) for superpositions within AdoCbl variant RNA (RF01689), histone 3 UTR stem-loop (RF00032), lysine riboswitches (RF00168), and purine riboswitches (RF00167), they failed to produce accurate superpositions (aligning accurately at least 50% of the residues) for the hepatitis delta virus ribozyme (RF00094) U6 spliceosomal RNA (RF00026) and glycine riboswitch (RF00504). One reason for those discrepancies might be the difference in structure sizes. For instance, the difference in structure sizes for the lysine riboswitch (RF00168) is 7%, whilst in case of the glycine riboswitch (RF00504) structures obtained from the PDB database, the size differences are up to 94% (one of the structures--3OWW--contains only the glycine-sensing domain). Another possible reason is the identity of the compared sequences.
ACKNOWLEDGEMENTS We would like to thank the authors of the benchmarked programs for their help:  David Hoksza  (SETTER),  Emidio Capriotti  (SARA),  Anton Petrov  and  Ryan Rahrig  ( R3D   Align ),  Kristian Rother  (LaJolla), and  Chin Lung Lu  (iPARTS). We also thank  Astha ,  Catarina Almeida ,  Grzegorz Chojnowski ,  Stanislaw Dunin-Horkawicz  and  Marcin Magnus  for critical reading and comments on the manuscript.
FUNDING Polish National Science Centre [ 2012/04/A/NZ2/00455  to J. M. B. ,  2014/15/B/ST6/05082 ,  2013/09/B/NZ2/00121  to W. D. ];  Foundation for Polish Science (FNP)  [ TEAM/2009-4/2  to J. M. B. and 'Ideas for Poland' fellowships to J. M. B. ]; European Research Council (ERC)  [ 261351  to J. M. B]. Computing power was provided by  IIMCB , funded by  EU structural funds  [ POIG. 02. 03. 00-00-003/09  to  J. M. B. ]. Funding  for open access charge:  Polish National Science Centre  [ 2012/04/A/NZ2/00455  to J. M. B. ].
IV. SOCIOTAL ARCHITECTURE The architecture of the SocIoTal platform is designed thanks to the Architecture Reference Model (ARM)  [1]  provided by the EU project IoT-A that enables the generation of user-centric IoT platforms. The abstraction level offered by some of the views enables to consider the security and privacy objectives with a lot of flexibility in a rigorous framework based on the design process. The SocIoTal platform, Figure1, is built around a central component, the Context Manager that holds the database with the contextual information. All the components and enablers will interact with the Context Manager to access data in a confidential manner.
The Web User Environment provides to non-experts users an intuitive tool to manage their personal or communal IoT. The tool integrates other SocIoTal platform components in order to provide a simple-to-use, secure and privacy-aware personal dashboard (see Figure  2 ). Through it, users can manage their connected devices, share them with other people in joined communities and to set trigger-action rules to build simple alerts and personal micro-apps. The Mobile User Environment is a mobile application with a subset of operations provided by the web environment; more specifically it consumes the Web User Environment API to enable the addition and removal of devices to/from the users workspace, watching the readings from the devices, receiving push messages from the Web environment, etc. Adding a device to the user environment workspace can be done by scanning a QR code with the mobile app.
A. Platform Implementation and APIs The above proposed architecture was fully implemented over the lifetime of the project and is available on GitHub  [23]. Here the framework, in terms of libraries and applications, can be downloaded. A wiki provides API information and tutorials on how to setup and use the platform. In addition, public instances of the platform are available. These are instances that are open to public use in order to evaluate and test the APIs and other aspects of the project. The details of the public instance can be found on the SocIoTal website  [24]. Almost all developed architectural components expose an Application Programming Interface (API). The APIs role in the SocIoTal architecture is twofold: to provide a large set of IoT functionalities to third-party application developers through an open community API, thus instrumenting and fostering them to build new applications for smart neighborhood, communities and cities; to provide all the needed endpoints and libraries targeted to the internal system integration, intra-modules communications and future platform extensions.
Basically, the provided SocIoTal APIs are of two types: software libraries (e. g. , written in Java language) to be linked to requiring applications, like those available, for example, for Authorization and Identity Managers; Web APIs (adopting a REST principle) and programming language agnostic, like the endpoints exposed by the User Environment, the Context Manager, the Trust Manager and others. All the APIs documentation is open and available through a unique access point, the related and updated, API documentation hub  [25].
Data collection and related devices To constrain the soil water balance model on atmospheric forcing, hourly values of air temperature and relative humidity, wind speed, solar irradiance and rainfall were collected within Site 6 with a CIMEL ENERCO 400 station (CIMEL Electronics, Paris, France) by following meteorological standards apart from wind speed (measured at 2 m height rather than 10 m). These hourly meteorological data were further used to estimate short-grass reference evapotranspiration (ET 0 ) by means of the Penman approach for standard conditions  (Valiantzas, 2006). To constrain the soil water balance model on water fluxes within the unsaturated zone, the seven sites were monitored bi-weekly and after each event of heavy rainfall for the soil moisture profiles and the water table level.
Vegetation canopy structure was also monitored regularly. The data collection was conducted around solar noon, and it included three items. First, soil moisture profiles were sampled every 0. 2 m to the 2. 5-m depth with a Vectra 503-DR CPN neutron probe (NP) (Vectra, la Verrière, France), and completed for the upper 0. 15 m using a Soil Moisture Equipment TRASE 6050 time domain reflectometry (TDR) sensor (Soil Moisture Equipment Corp. , Santa Barbara, CA). The number of locations for collecting soil moisture profiles (Table  1 ) varied according to site size and soil heterogeneity derived from a pedological map. For each location, we beforehand characterized thickness and texture for soil horizons by using in-situ observations along with expert knowledge, and we ascertained soil surface properties using additional archive information from former studies in the same region  (Trambouze and Voltz, 2001; Bsaibes, 2007).
For topsoil (first horizon), the three other inputs of van Genuchten functions (Ks, a and n) were obtained from previous observations within the Peyne watershed  (Trambouze and Voltz, 2001; Bsaibes, 2007). For deeper horizons, these three inputs were determined using the module of HYDRUS-1D inverse mode as a calibration procedure. The latter consists of minimizing, over the simulation period, the differences between simulated and measured profiles of soil moisture. The initial guess for the three inputs were derived either from literature values wherever possible  (Trambouze and Voltz, 2001; Bsaibes, 2007) , or from the ROSETTA module of HYDRUS-1D otherwise. We simulated the soil water balance over the soil portion between 0 and 2. 5-m depth. We split this soil portion into a limited number of horizons in accordance with in situ observation and expert knowledge (between two and four horizons, including surface), and we next discretized the whole soil portion into 251 layers for numerical simulations.
Technology choices To fulfil the objectives of the VICTORIA software, AREVA based its developments on the ANSYS WORKBENCH platform. ANSYS, world leader for Computer Aided Engineering software, has a reliable experience of integrated simulation system. The ANSYS WORKBENCH platform gathers finite elements solvers (structural analyses, computational fluid dynamics (CFD) for example) with a common and compatible data models management. Also 3D modelling software is included to create geometries, edit, clean up or repair CAD imports. The ACT (ANSYS Customization Toolkit) product recently released by ANSYS enables to add customization to the WORKBENCH platform. This application programming interface permits to redesign the purpose of the traditional ANSYS analyses for radiation protection solvers.
VICTORIA Workflow The creation of a radiation protection analyses is based on a sequence of several steps: -Material database creation, -3D geometry importation and / or creation, -Model: material assignment to bodies, -Setup: simulations parameters (particles type, sources and tally creation), -Solution and results: including post-processing features: import of output files for 2D/3D rendering of tallies. Each workflow defined in a project is pictured on the project schematic front page. The project schematic offers a comprehensive and visual overview of the cases dealt. Duplication and modification of whole workflow can be done. The saved projects stored all the needed information ensuring traceability and reliable management of the models. As an overview, in the figure  1 , 2 MCNP analyses are run.
This offers the capability to convert any kind of CAD shapes to MCNP6. Figure  4  presents an extensive test case of this feature applied to model the entire quarter of a reactor building. More than 400 000 tetrahedrons are used in this model. However such technic has inherent drawbacks as an increase of the computer runs of a factor of ~10 for equivalent configuration modeled with traditional surfaces. However this functionality prefigures what could be in a next future a more global approach to deal with CAD integration for Monte Carlo simulation and demonstrates the versatility of the software using mesh algorithms available in the ANSYS platform.
All types of tallies can be defined in the GUI: point, surface, cell tally and also meshed tallies: structured tallies (FMESH and TMESH cards of MCNP and extended mesh of TRIPOLI) and Unstructured Meshes (EMBEE card of MCNP6). Response functions (flux, dose, reaction rate) and energetic bins are also callable. At the end of this step the solver input file is exported in a user defined directory. The solver run is not part of the VICTORIA workflow. The run is done according to the user preference (linux or windows) and the cluster availabilities.
The MCNP results of each tallies are then postprocessed to a CSV file containing the coordinates values of the center of each cells/tallies and the corresponding energy deposition value. Such file format is readable by the main finite elements software packages to run thermomechanical analyses. Such workflow is presented in figure  11  for the ANSYS Workbench software where the energy disposition is loaded as an external data of heat generation (W/cm 3 ).
Conclusions This paper presents recent progress of AREVA developments. The suite of engineering tools has been drastically upgraded in order to offer Monte Carlo calculations as a reliable and efficient solution for radiation propagation projects. The main development is the VICTORIA software (Various Instances CAD calculation TOol and Radiation-propagation Interface by AREVA) offering CAD integration for both MCNP and TRIPOLI solvers. The calculations running times have been decreased by using modern technics. The use of Monte Carlo solvers is widespread from conceptual design phase  [5, 6]  to decommissioning  [7]  for any kind of reactor fleet or nuclear facility. The versatility of the tools developed enables prompt answer to the needs as: -New plant design and life extension analyses (Fluence, heating, sensors responses and for waste characterization, inventories and dose to operators calculations.
The contents of the article are as follows: we firstly introduce the concept of transformation crystallography and quasi-crystallography, and illustrate this with an example of transformed honeycomb (6-fold) and Penrose-type (5-fold) lattice in the plane. We then numerically investigate elastodynamic wave propagation with finite-difference-time-domain (FDTD) software SimSonic in large scale structured soils deduced from transformation crystallography. Our graphenelike seismic cloaks are also studied using a morphing algorithm  [5]. We then consider similarities between invisibility cloaks for mechanical waves (counterpart of Pendry's electromagnetic cloak  [6] ) and thermal diffusion processes that have been theorized and experimentally validated by our research group at Institut Fresnel in Marseille and the research group of Martin Wegener at Karlsruher Institut für Technologie (note in passing thermal cloak controls in fact diffusion of high frequency phonons).
Numerical illustrations of TC and TCQ with FDTD The computer software SimSonic, which we use in this section to simulate propagation of elastic waves in crystals and transformed crystals is a freely available software designed by Emmanuel Bossy  [19] , which is based on finite-difference time-domain (FDTD) computations of the elastodynamic wave equations. SimSonic already serves as a tool for communities of researchers, teachers and students, and this package consists of several compiled programs and C source codes, freely available, under the GNU GPL license  [20]. SimSonic solves the Navier equations in the following form: ∂T ij ∂t (x, t) = d j=1 d i=1 C ijkl (x) ∂v k ∂x l (x, t) + Θ ij (x, t) (1. 3) ρ(x) ∂v i ∂t (x, t) = d j=1 ∂T ij ∂x j (x, t) + Φ i (x, t) (1. 4) where C ijkl is the rank-four symmetric elasticity tensor with d 4  [4]  entries (d = 2 or 3), T ij the rank-2 stress tensor and ν i the particle velocity (vector) field.
Principle of morphing Morphing literally speaking is an image transformation (underpinned by coordinate changes) that includes interpolation of colors: the colors of the source's image progressively fade away to reveal the colors of the destination's image. To summarize, morphing is based on a double interpolation, both on shapes and on colors, between two images. There are many freeware softwares that are capable of doing just this, however, here we work with Sqirlz Morph  [26].