dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Eric Dufour  and  Marten Szibor  for useful discussions, and  Tea Tuomela ,  Essi Kiviranta  and  Outi Kuronen  for technical assistance. AMMi (Association contre les Maladies Mitochondriales ) and  ANR  (Grant number  ANR-12-BSV1-0010 ) for financial support to RE-K and  PR ,  Academy of Finland  (Grant number  283157 ),  European Research Council  (Grant number  232738 ),  Tampere University Hospital Medical Research Fund and Sigrid Juselius Foundation  for financial support to HTJ, EK and KL.
We sought to explore the architecture of trials of calcineurin inhibitors for atopic eczema to document the extent to which comparisons with active treatments such as topical corticosteroids (TCS) might have been included or avoided. We identified all eligible randomized controlled trials (RCTs) using the Global Resource for EczemA Trials database. Network plots were produced where the nodes represented a treatment type and the lines between the nodes represented the number of trials or participants that were involved in the various treatment comparisons. A total of 174 RCTs for atopic eczema treatments were identified where pimecrolimus, tacrolimus or topical corticosteroids were compared with another intervention or a vehicle/emollient. Of 39 trials involving pimecrolimus and of 41 trials involving tacrolimus, 8 (20.
One could argue that topical corticosteroids have also been overly compared against vehicle preparations and that may be so (46 (36. 2%) of 127 involving topical corticosteroids made comparisons with a vehicle control). Although it should be recognised that 27 different types of topical corticosteroids have been included in the corresponding nodes in Figure  1 , whereas topical pimecrolimus is just one preparation and topical tacrolimus is just three strengths of the same preparation. Strengths of the study include the comprehensive inclusion of all published clinical trials obtained through the GREAT database covering the entire period in which the two topical calcineurin inhibitors were introduced.
GREAT database The Global Resource for EczemA Trials (GREAT) database contains records of all RCTs and systematic reviews of treatments for atopic eczema published since the inception of the  MEDLINE (1966)  and EMBASE (1980) (Centre of Evidence Based Dermatology). The search strategy used to populate the GREAT database is given in Supplementary Material 1. Searches are also carried out using the Cochrane Library and the Cochrane Skin Group specialised register of trials from inception, therefore the GREAT database also contains many records that are not in MEDLINE or EMBASE as a result of handsearching and other searches done by the Cochrane Skin Group. Further information on the GREAT database can be found at: http://www. greatdatabase. org. uk.
Selection criteria The GREAT database was searched to identify all RCTs for atopic eczema up until 4 th December 2014 that incorporated at least one treatment arm involving pimecrolimus, tacrolimus or topical corticosteroids as an intervention. Any RCTs that only involved intradrug comparisons, for example different doses of the same drug, were excluded as we felt it unlikely that these dose finding studies would have deliberately avoided a lower or standard dose.
, 2008). In order to assess the statistical significance of the C-score statistic a permutations procedure is applied which asks the underlying question: Given the number of studies in our network each comparing two treatments, and given the number of times each of those treatments appear in the network, what are the chances of observing the estimated C-score statistic? This procedure produces a 95% confidence interval and a p-value for the estimated C-score under the null hypothesis that there is no selective pattern in the network. A significant p-value (<0. 05) indicates that the estimated C-score is unlikely to be due to chance and that there are selective patterns in the choices of treatment comparisons in the network. Analyses were carried out using R software version 3. 1. 0 with the package EcoSimR and the NodeXL add-in with Excel.
a. Transportation: Those related to the €km and tolls in the case of private vehicles and ticket prices in the case of HSR. b. Transportation and accommodations: Hotel expenses are added to the costs. The data concerning the Hotel Price Index for all of the Spanish cities served by HSR was used (http://www. trivago. es). This index is based on bookings made on the website, so it represents an average value of the prices that are actually paid by customers per room per night. * As 'Promo' and 'Mesa' fares do not exist in certain connections, the lower ticket price for each case was considered instead.
On top of OM2M, another platform, SemIoTics, enriches the collected data with semantic descriptions, and makes them available to the robot through a REST interface. SemIoTics is driven by a knowledge base capturing knowledge about the devices of the system represented according to our core-domain IoT ontology, and about the environment shared by the robot and the devices (here, the apartment). It is a Java software developed to showcase the role of semantic web technologies in IoT data management, based on Apache Jena. The robot itself is also a semantically enabled agent, it uses a "common sense" ontology and a knowledge base to reason about its 3D environment, as described in  [5]. The knowledge specific to the robot relies on ontologies out of the scope of this paper, but its knowledge base can be extended with any ontology, including IoT-O (as it is done in this paper).
That is why the knowledge described in this paper is implemented in a dedicated knowledge base using IoT-O, ADREAM-Robot 10  : the ontology is shared by the robot and SemioTics, and each system has its own knowledge base. The synchronization between the knowledge bases of the different agents is out of the scope of this paper. The use case focuses on home automation, but IoT-O and our approach are generic enough to be adapted to other domains. For instance, it could be used to support an air quality monitoring system in a smart city, by describing the sensors that collect the data and the services the citizens can subscribe to. The usage of IoT-O and its module in the use case is double: it is used to model the observations about modifications of the apartment, allowing the robot to keep an up-to-date representation of its environment, but also to model the changes the robot wants to make into the apartment through its actions and through the connected devices.
The robot and SemIoTics have distinct knowledge bases, even if in fig. 4 , a unique knowledge base is represented as the two systems exchange knowledge freely through a rest interface. Consistence issues are not considered in this work, as only one smart agent interacts with the system. The process described in figure  4  structures the use case: data is first gathered by the sensors, and enriched by SemIoTics. The enriched observations are processed by the robot, which decides to perform actions represented as enriched actuations. These actuations are sent to SemIoTics, which translates them into raw commands for the actuators to perform. In complement to IoT-O, the dogont 31 ontology is used to describe the apartment and the location of devices inside it. Dogont is an ontology identified in the SAREF project, and it is imported by Poweront.
The enrichment process requires an approach specific to the data, either by writing a dedicated enrichment script, or by using semantic annotations in the data as in  [15] , where raw data is stored in relational databases and the database schema is annotated for enrichment. SemioTics uses a dedicated enrichment script that could in the future be extended by producing annotated data. The presence observation indicates the position of the person in the apartment, and the temperature observation measures the temperature at a given point in space and time, both in the form of ssn:ObservationValue instances. This enriched information is accessed by the robot through SemIoTics' REST interface, and it is used to update the robot's representation of the world. This representation of the world is stored in the robots knowledge base, and used as a context in the Analysis step.
Conclusion and future works This paper introduces IoT-O, a modular core-domain IoT ontology designed to be compliant with identified requirements. After a detailed presentation of its modules, an instantiation of IoT-O is presented in a home automation use case. IoT-O is used to bring semantic interoperability between SemioTics, a platform enabling semantic access to connected devices, and a robot. SemIoTics and the robot implements the MAPE-K loop, an autonomic computing pattern, and uses IoT-O at each step of the loop to describe knowledge about the connected devices and about the data they produce and consume. The ontology describes the evolving state of the robot's environment through sensor observations, and the capabilities the system offers to impact this environment through the devices.
In silico design of species-specific primers For designing species-specific primers for Xenostrobus securis we followed the approach described by  Ardura et al. (2015)  for Rangia cuneata but using the cytochrome oxidase I (COI) gene as a DNA Barcode instead of 16S rDNA. Briefly, COI gene sequences of a wide taxonomic range of mollusks were downloaded from public databases: GenBank (http://www. ncbi. nlm. nih. gov/genbank/) and BOLD (http://www. boldsystems. org/). They were aligned using ClustalW  (Thompson et al. , 1994)  application on BioEdit. Species-specific primers were developed within the COI region after aligning all the X. securis sequences retrieved from the databases, those generated in the present study, and a set of sequences from closely related bivalves; then a region with maximum identity with the target species and minimum with the rest was visually selected.
Data analysis Correlations of mussel densities and morphometric features, and between Xenostrobus and Mytilus densities, were assessed with STATISTICA v8. 0  (StatSoft, 2007). DNA sequences were visually inspected on BioEdit v7. 2. 5  (Hall, 1999). Every sequence was blasted against the GenBank Nucleotide database and a minimum of 98% nucleotide identity was set to assure the correct species identification. ClustalW  (Thompson et al. , 1994)  application on BioEdit were employed for sequence alignment. The number of haplotypes, haplotype frequency, haplotype and nucleotide diversities with their standard deviations were calculated with DnaSp v5. 10 (  Librado and Rozas, 2009). Arlequin v3. 11  (Excoffier and Lischer, 2010)  was employed for population structure analysis. The variance within and among sampling sites in Avilés estuary were tested by applying an analysis of molecular variance (AMOVA) with 10 000 permutations.
To inspect genetic differentiation between pairs of sites, pairwise φST's comparisons were done with 10 000 permutations. Additional sequences from Australia: KC509681-KC509741 (Colgan and da Costa, 2013); Vigo, Spain; Grau-du-Roi Camargue, France; Venice, Italy: FJ949108-FJ949122  (Pascual et al. , 2010)  and Pontevedra, Spain: JF430150-JF430154  (Guerra et al. , 2013)  were downloaded from GenBank to be compared with the results obtained. A sister species was also added as outgroup (Xenostrobus pulex: DQ917582). A simplified Bayesian Inference (BI) tree with 40 unique haplotypes was constructed in BEAST v1. 8. 2  (Drummond et al. , 2012)  for MCMC of 50 million chains, taking a sample every 1000 generations, with a tree prior model of stepwise Extended Bayesian Skyline Plot  (Heled and Drummond, 2008).
Genetic barcodes In 130 COI sequences a total of 105 polymorphic sites were found (114 mutations). All were parsimony informative, and 27 of these changes were non-synonymous. This rendered 67 different haplotypes of 554 base pairs (GenBank accession numbers KR024822 -KR024888). Levels of genetic variation, haplotype and nucleotide diversities are shown in Table  1. At all sites the haplotype diversity was higher than 93%, meaning that many sequences were single haplotypes (singletons). High nucleotide diversities were also observed. Pairwise φ ST values between sampling sites were not significant except between AV02 and AV06 (φ ST = 0. 102, p = 0. 026). These were respectively the sites with higher and lower density of X. securis. Individuals from site 2 were combined regardless of the two length groups because an AMOVA with all groups together could not be rejected (φ ST = -0.
014, p-value = 0. 595 ± 0. 002). Despite a clear difference in the proportion of private haplotypes among sites (Table  1 ), the hypothesis of a single population in the six sites sampled within Avilés Estuary could not be rejected (AMOVA with all sites, φ ST = -0. 017, p-value = 0. 202 ± 0. 001). A phylogenetic tree based on a region of 462 bp within COI Barcode (Fig. 2 ) shows the relationships between new and previously described haplotypes in other studies. The sequences obtained from Avilés Estuary were grouped within two main clades. One of the haplotypes (within Clade 1) was identical to other sequences obtained from native populations as well as from other European regions (nearby Galicia, France, Italy). Two sub-clades seem to occur within Clade 2, which on the other hand contains many new haplotypes from Avilés not previously reported in public databases.
Xenostrobus securis-specific primers The alignment of reference sequences from GenBank allowed to identify two regions within the COI gene conserved in the analyzed and previously studied X. securis specimens, but different in other species. The primers designed were: XSminiCOI-F: 5′-TCTATGGAYATRATYTTTCCTCG-3′ and XSminiCOI-R: 5′-GCAGTYACAGYYATAGACCA-3′. The primers were BLASTed in silico and did not retrieve significant hits with any species except with X. securis COI gene sequences. These primers amplify a region of 310 bp within COI gene.
Acknowledgements The authors would like to express their gratitude to all members of the  Department of Functional Biology  for their technical assistance. Also special thanks to the people of the Ecology area from the  University of Oviedo  for their helpful advice and use of equipment. The  International Master in Marine Biodiversity and Conservation EMBC +  is acknowledged by  FDD  for support. This work was supported by the  Spanish Ministry of Economy and Competitiveness Challenge  Grant  MINECO CGL2013-42415-R. It  is a contribution from the  Marine Observatory of Asturias (OMA)  and the Research Group  GRUPIN14-093.
Materials and Methods The specimens examined were deposited in the following collections: Terminology of external morphological characters primarily follows  Anufriev & Emeljanov (1988)  and  Gnezdilov et al. (2014) , except for the female genitalia nomenclature, which follows  Bourgoin (1993)  and  Gnezdilov (2002) , and with the male terminalia nomenclature modified after  Bourgoin (1988)  and  Bourgoin & Huang (1990). Nomenclature for wing venation follows  Bourgoin et al. (2015)  for the forewing and  Anufriev & Emeljanov (1988)  for the hind wing. Measurements and photographs at UDCC were taken using a digital imagery system consisting of a Nikon SMZ1500 mi-croscope, Nikon Digital Sight DS-U1 camera, and NIS Elements Imaging software (version 3. 0), and at the National Museum of Wales (Cardiff, United Kingdom) using a Leica MZ8 with JVC video camera KY F7OB with the software Synoptics Automontage.
Measurements were made directly from the specimens (for B. asymmetrospinata sp. nov. ) or from calibrated photographs (for B. simplex, and B. truncoptera sp. nov. ). Features ascribed to the genus are not repeated in the species level descriptions. Labels of name-bearing types are provided quoted verbatim using "/" to indicate a line break and "//" to indicate a new label and with supplemental information given in brackets. For other material examined, label data were rewritten to maintain consistency in pattern, beginning with the country, state, or province, followed by more specific locality information, collection date, and collector; the number and sex of specimens and the depository where the specimens are located are given in parentheses. Specimens at the UDCC (including loan) were provided 2D barcode labels, and label data were captured for online presentation (visualized at Discover Life at www.
Acknowledgments We are sincerely grateful to  Mick Webb  ( London, United Kingdom ) for the opportunity to examine the holotype of B. simplex (and for responding to subsequent inquiries),  Mike Wilson  ( Cardiff, United Kingdom ) for his hospitality in the  National Museum of Wales  and providing the facilities for photographing specimens, and to  Lois B. O'Brien  ( Tucson, Arizona ) for sending specimens for study. We thank  Terry Erwin  ( Smithsonian Institution, National Museum of Natural History ) for specimens from his Ecuador canopy fogging studies. First author (VMG) research is performed in the frames of the Russian state research project no. 01201351189 , his trip and research stay in UK was sponsored by the  Royal Society of London. Support  to the 2nd author (CRB) provided in part by the  University of Delaware, Department of Entomology and Wildlife Ecology and NSF Advancing Digitization of Biological Collections (ADBC)  award  1115103  (Digitization TCN: Collaborative Research: Plants, Herbivores, and  Parasitoids: A Model System for the Study of Tri-Trophic Associations ).
Three-dimensional (3D) reconstruction from heterogeneous sets of images normally results in low-resolution density maps. Thus, data heterogeneity analysis to isolate images of complexes of similar molecular compositions and conformations is a usual prerequisite to structural determination at high resolution. Biochemical procedures can usually be optimized so that the majority of complexes in the specimen, if not all of them, have the same molecular composition. However, the same composition rarely means the same conformation, due to the flexibility of complexes. Thus, conformational heterogeneity of specimens is usually analyzed by image analysis and classification methods. The reconstruction of different coexisting structures from the same sample will here be referred to as multiconformation reconstruction.
After a refinement of the two-reference alignment (using a decreasing angular step size and a decreasing search range) and a stabilization of the orientation parameters, a new reference may be added and the process repeated until the number of references starts to exceed the number of intrinsic divisions within the dataset, which can be observed as a poor reconstruction (usually from a very small subpopulation of particles) after adding a new reference  [72]. The particle images assigned to corrupt reconstructions are assumed to contain degraded particles and are removed  [72]. The incremental Kmeans-like approach was recently used for isolating rotated from unrotated 80S ribosome images (using a low-resolution 80S ribosome from rabbit as reference) before their further splitting guided by 3D variability analysis (see the next subsection), and this combined procedure resulted in solving 11 states with conformational and compositional differences along the elongation circle of human 80S ribosome  [73].
FREALIGN performs ML classification using E-M algorithm with fixed alignment parameters found by a non-ML parameter refinement. Rounds of non-ML parameter refinement are typically alternated with rounds of the E-M algorithm, which makes the method more efficient than implementations that are entirely based on ML  [12]. ML-based methods can be used with heterogeneous data in order to determine multiple co-existing conformational states, but they can also be used with globally homogenous data, in which case the most selfconsistent subset of particles could be isolated to obtain a single-state reconstruction at high resolution. A few examples of these two types of applications of RELION and FREALIGN ML-based methods will be cited here. It should however be noted that the initial models used by these approaches are usually obtained by other approaches (e.
g. , initial model generation methods available in EMAN2 and SIMPLE software packages  [3, 18] , random conical tilt technique  [93] , or selecting models from EMDB), which was also the case in the applications that are cited next. In the context of identifying the most self-consistent subsets of particles for reconstructing single states at high resolution, the RELION approach resulted in near-atomic resolution of different structures, from small complexes, such as a 170 kDa asymmetric gamma-secretase  [26]  and a 300 kDa tetrameric TRPV1 ion channel  [22] , to large complexes, such as a 4. 3 MDa asymmetric 80S ribosome  [31]  and a 5. 5 MDa tetrameric ryanodine receptor RyR1  [35]. In the same context, the FREALIGN approach resulted in near-atomic resolution of structures of complexes such as a 250 kDa asymmetric L-protein of vesicular stomatitis virus  [32] , a 465 kDa tetrameric beta-galactosidase  [27] , and a 540 kDa hexameric AAA+ ATPase p97  [37].
In the context of classifying heterogeneous data sets to identify and structurally characterize different conformational states that coexist in the specimen, the RELION approach was combined with the use of masks and a modification of experimental images to improve consistency of their comparison with model projections. This approach separates experimental images based on their differences in a region of interest, which involves subtracting a residual signal (the signal outside the region of interest, but inside the complex) from the experimental images. The modification of experimental images improves consistency of their comparison with model projections in the region of interest, but it requires a preliminary determination of image orientation and translation parameters and a creation of two 3D masks, one for the region of interest and the other for the entire complex.
Except for supervised multireference classification methods, all methods reviewed here belong to the class of unsupervised classification methods, which aim at finding actual conformational states without a prior knowledge about the distribution of states. Methods that do not use available 3D models (ab initio methods) have advantage over other methods regarding the model bias, but they base the 3D reconstruction of different conformations on conformational variability analysis and sorting in 2D space. Methods that attack the heterogeneity problem directly in 3D space are more straightforward, but as they use available 3D models (at least for initial image orientation), the potential model bias should be carefully considered when using these methods. Currently, the most popular 3D methods are maximum-likelihoodbased classification methods available in RELION  [6, 90]  and FREALIGN  [12, 91]  software packages.
Morphological traits and genetically modified phenotype Flower colour differed between the carnations FLO-40685-2 (purple) and the parental variety (cream). In the comparison of 27 qualitative morphological characteristics, no differences were found between carnation FLO-40685-2 and its comparator (i. e. the parental variety). In a trial performed in the Netherlands in 2000, 18 quantitative morphological characteristics were measured for carnation FLO-40685-2 and its comparator, and analysed statistically with a single-factor ANOVA. 9 Seven statistically significant differences between carnation FLO-40685-2 and its comparator were found (for plant height, length of fifth node, thickness of fifth node, petal length, petal width, number of styles and number of anthers). Data collected in a trial in Japan (season 1999-2000) showed a similar average time to flowering for carnation FLO-40685-2 and the parental variety.