dataesr/scientific-paragraphs-categorization huggingface dataset:
Out of the 85 cases from the Weng benchmark that can be used for In-terEvDock2 benchmarking, 16 contain a multimeric partner. The InterEvDock2 top 10 consensus contains an acceptable or better solution for 7 out of these 16 cases (44%). This success rate is comparable to the overall success rate of InterEvDock2 on the much larger PPI4DOCK benchmark (29%) and on the 85 cases of the Weng benchmark (32%). Additionally, docking using multimeric partners has the advantage that potentially 'sticky' interface regions involved in multimeric interactions of one partner are buried in the multimeric interface and therefore masked for the docking process. In Table  1 , the InterEvDock2 performance is also compared to the performance of the widely used rigid-body docking program Zdock3. 0. 2  (45) , assessed on the same 812 complexes from the PPI4DOCK benchmark.
For each case, 54 000 decoys are generated and ranked by Zdock3. 0. 2. In 126 out of 812 cases (15%), an acceptable or better solution is found among the top 10 decoys. Altogether, these benchmarking results highlight the added value of the In- The benchmark is made of the 812 targets of the PPI4DOCK benchmark (1417 cases)  (37)  for which pairs of co-evolved MSAs with more than 10 sequences could be obtained and FRODOCK 2. 1 (13) was able to generate at least one acceptable or better decoy  (44)  among the top 50 000 decoys. In the upper part of the table, top 10 success rates are reported as the number of cases (and percentage between brackets) for which at least one model out of 10 is an acceptable or better solution. Assessed methods are InterEvScore  (32) , SOAP-PP (34), FRODOCK 2. 1 (13), InterEvDock2 consensus (this work and (  17 )) and Zdock3.
Description of docking case studies from input sequences and using constraints To illustrate the biological relevance of InterEvDock2 predictions, we consider two docking case studies derived from the PPI4DOCK benchmark (Figure  4 ). The first case is a complex between the Rho-family GTPase Cdc42 and the conserved, catalytic domains of exchange factor intersectin. Details of this interaction (PDB identifier: 1ki1) and structure-based mutagenesis revealed key features of the activation of Cdc42 by intersectin  (48). This case was tested on the InterEvDock2 server by providing input sequences of the interacting regions in the two partners. Unbound templates were imposed for both proteins as in the PPI4DOCK benchmark; otherwise the automatic template search might  consensus model found by InterEvDock2 for docking between unbound homology models of Cdc42 (green, modeled using an unbound template at 54% sequence identity) and the conserved, catalytic domains of intersectin (cyan, modeled using an unbound template at 25% sequence identity).
The model is superimposed on the reference crystal structure (PDB identifier: 1ki1) (gray). It is acceptable with interface RMSD 4. 03 Å. (B) Best model found in the InterEvDock2 top 10 consensus for docking between PPI4DOCK unbound homology models of the RING domain of IDOL (green) and UBE2D (cyan) when four residues experimentally known to be important for the interaction are used as constraints (with default distance 8 Å). The model is superimposed on the reference crystal structure (PDB identifier: 2yho) (gray). The model is acceptable with interface RMSD 2. 29 Å and is ranked first of the top 10 consensus. The four residues used as constraints from chemical shift mapping are shown as green spheres (M388, V389, C390 and C391). have found the bound partners belonging to PDB identifier: 1ki1 or other bound templates.
The unbound templates (4f38A and 3odoA) have sequence identities of 54 and 25% with the modeled regions of Cdc42 and intersectin, respectively. Among the top 10 consensus models returned by In-terEvDock2, one acceptable solution is found as top 2 (Fig-  ure 4A ). The second case illustrates the interest of docking with user-defined restraints. We consider a complex between the RING domain of E3 ubiquitin protein ligase IDOL and ubiquitin-conjugating enzyme E2 UBE2D (PDB identifier: 2yho) (49). This interaction is involved in the regulation of cholesterol uptake. Nuclear magnetic resonance (NMR) chemical shift mapping was used to confirm the interacting region prior to crystallographic studies. This NMR analysis showed four residues (M388, V389, C390 and C391) in the RING domain of IDOL to have particularly high chemical shift variation upon binding of UBE2D.
Figure1. Workflow of the InterEvDock2 pipeline. Eight steps can be performed in InterEvDock2 depending on the user input, out of which three mandatory steps (iv), (vii) and (viii) are always performed as they were in the original InterEvDock pipeline (17) , except that the FRODOCK algorithm was updated to version 2. 1 (13) and the consensus calculation was slightly modified to save time and improve results. New features are available allowing the user to provide only an input sequence for one or both partners: (i) if the user does not provide a template, search for a suitable template using HHsearch (38, 39) ; (ii) if the user provides a template but no query-template alignment, alignment of query sequence with template sequence using MAFFT (40); (iii) once a template and a query-template alignment are available for each partner with no user-provided structure, comparative modeling using a RosettaScripts (41) protocol based on RosettaCM (42)  to build a 3D model for (at least part of) the input sequence.
ACKNOWLEDGEMENTS InterEvDock2 and Zdock3. 0. 2 benchmarking on PPI4DOCK was done through granted access to the  HPC resources of CCRT  under the allocations  2015-7078 ,  2016-7078  and  2017-7078  by  GENCI  ( Grand Equipement National de Calcul Intensif ). We are very grateful to  Pablo Chacon  ( FRODOCK 2. 1 ),  Andrej Sali  ( SOAP-PP ),  Tal Pupko  ( Rate4Site ),  Marco Biasini  (ProteinViewer),  Johannes S öding (HHblits and HHsearch)  and the  Rosetta-Commons community  for making their methods available for implementation in the InterEvDock2 server.
FUNDING French Infrastructure for Integrated Structural Biology (FRISBI)  [ ANR-10-INSB-05-01 ];  Agence Nationale de la Recherche   ANR-IA-2011-IFB [ANR-11-INBS-0013 ];  CHIPSET  [ ANR-15-CE11-0008-01 ]. Funding for open access charge:  Agence Nationale de la Recherche  Grant  CHIPSET [ANR-15-CE11-0008-01 ]. Conflict of interest statement. None declared.
The global view of the ATLAS Event Index system has been presented in the 17th ACAT Conference. This article concentrates on the architecture of the system core component. This component handles the final stage of the event metadata import. It organizes its storage and provides a fast and feature-rich access to all information. A user is able to interrogate metadata in various ways, including by executing user-provided code on the data to make selections and to interpret the results. A wide spectrum of clients is available, from a set of Linux-like commands to an interactive graphical Web Service. The stored event metadata contain the basic description of the related events, the references to the experiment event storage and the full trigger record and can be extended with other event characteristics.
ATLAS Event Index The ATLAS  [1]  Event Index  [2]  (presented in the 17th ACAT Conference  [3] ) is a system which provides experts and users with event-level metadata services. A user can find information about specific events from the Event Index data rather than from event dataset files. Event Index stores data GUIDs (Global Unique IDentifiers) as well as a variety of other event-wise information. All data are organized in TagFiles, which are currently implemented as Hadoop [4] MapFiles. A set of TagFiles is a TagSet, which can be accessed in the same way as a TagFile. All TagFiles are registered in a Catalog. Each operation on the Event Index database is implemented as a transformation of TagFiles, its result is again a standard TagFile subject to common interface. TagFiles can be processed in the most general way by applying any valid (Java) code to them.
Interfaces External access to data is available via several interfaces: • Linux-like command line which requires a small local client. • Interactive Graphical Interface is just a simple graphical version of the command line interface for various commands (like Event Lookup command el (Figure  2 )). Results are represented in a graphical view (like dataset overlaps table (Figure  4 ) or trigger statistics table and histogram (Figure  3 )). The data oriented interface (Figure  1 ) presents all existing datasets with actions available to each of them. • Stateless Web Service serves as an interface to other ATLAS services. All interfaces implement the same functionality. External access is protected by the CERN Shibboleth  [5]  system.
Commands The most generic access is possible by using two general commands available as Linux commands and as Web Service): • catalog command allows searching and modification of the TagFile Catalog. • ei command gives full searching access to all data. Result of each request is a new TagFile. User can specify search and assembly criteria using any valid (Java) code. This command is very rich, but more complex to use. There are several special auxiliary processors available to users, allowing more complex operations, like generating statistics histograms for selected TagFile variables. Some frequently-used operations are available as special wrapper commands. The most important are: • el is Event Lookup command to locate of events based on their event numbers. • ti is Trigger Interface command to create special statistics tables about event triggers.
Informal Description in a Nutshell We aim at achieving rendezvous of two asynchronous mobile agents in an infinite grid and in a deterministic way. It is well known that solving rendezvous deterministically is impossible in some symmetric graphs (like a basic grid) unless both agents are given distinct identifiers called labels. We use them to break the symmetry, i. e. , in our context, to make the agents follow different routes. The idea is to make each agent "read" its label binary representation, one bit at a time from the most to the least significant bits, and for each bit it reads, follow a route depending on the read bit. Our algorithm ensures rendezvous during some of the periods when they follow different routes i. e. , when the two agents process two different bits. Furthermore, to design the routes that both agents will follow, our approach would require to know an upper bound on two parameters, namely the initial distance between the agents and the length (of the binary representation) of the shortest label.
2: if both agents follow this route concurrently from the same node, then they meet. Pattern Seed can be seen as such a route, and Procedure Berry (whose code is shown in Algorithm 3) consists in executing Pattern Seed from each node at distance at most α. Hence, unless they meet, the later agent completes its execution of Pattern RepeatSeed before the earlier one starts executing Seed from the same node. Note that P ushP attern uses as many patterns as the number of basic patterns in the sequence it is supposed to push: this and the fact of doubling the value of the input parameter of Procedure Assumption in Algorithm 5 contribute in particular to keep the polynomiality of our solution. Thus, once the earlier agent completes the first part of Harvest(α), the later one has at least started the execution of Assumption(α) (and thus of the first part of Harvest(α)).
Conclusion The express view algorithm allows an important shortening of Intromedic capsule film reading time with a high sensitivity. Nowadays, very few informatics algorithms for accelerated reading have been validated, although almost all capsule systems propose such informatics tools. This includes the SBI (small-bowel blood indicator, Given Imaging/Medtronic), that proved to be of poor sensitivity and specificity  [12, 13] , and the Quick-view algorithm that was validated through a retrospective blinded double-reading trial published in 2012  [14]. Since that study, very few evaluations of informatics reading algorithms have been published, and no prospective evaluation  [15]. Developing such complex algorithms requires specifically large image databases that have been produced through international collaborations  [16].
Statistical analysis Sensitivity values were presented as a percentage (95 % confidence interval). McNemar's test was used to compare the express view and standard reading sensitivity and specificity results using the expert review as a gold standard. We used SPSS 12. 0 version software (SPSS Inc, Chicago, IL, United States) in a Windows XP (Microsoft, Seattle, WA, United States) environment. A P value < 0. 05 was considered to be statistically significant.
Is the express view algorithm now ready for clinical use? The theoretical sensitivity of more than 94 % is rather reassuring, although confirmation by an independent study would be helpful. In this setting, one could use the express view mode to make a first reading, that could be considered to be sufficient in those patients with significant findings at the end of this first reading. In those patients with a negative express view reading, a reading of the "complementary film" could be done, whenever available on the Intromedic software. On the other hand, simple patient surveillance followed by a complete reading in those with recurrent bleeding is an alternative. This approach may yield precious time saving for gastroenterologists.
Morphometric characters of fossil leaves such as size and shape are important and widely used sources for reconstructing palaeoenvironments. Various tools, including CLAMP or Leaf Margin Analysis, utilize leaf traits as input parameters for estimating palaeoclimate, mostly based on correlations between traits and climate parameters of extant plants. During the last few years, the scope of information extracted from the morphology of fossil leaves has been further expanded by including leaf economics, which describe correlations between functional leaf traits and ecological strategies. The amount and quality of available data are essential for a successful palaeoecological analysis utilizing leaf traits. Here, the database MORPHYLL is described. This database is devised to offer a web-based resource for fossil leaf trait data.
For this purpose, fossil leaves from various collections were digitized and morphometric traits extracted from leaf outlines. Besides metadata such as accession number, repository, fossil site or taxonomic information (for identified specimens), MORPHYLL offers queries for several morphometric parameters and derived ecophysiological traits (e. g. , leaf mass per area). Currently, the database contains data from about 6000 fossil leaves from sites in Central Europe, spanning almost the entire Paleogene and part of the early Neogene. The application potential of the database is demonstrated by conducting some exemplary analyses of leaf traits for the Paleocene, Eocene and Oligocene, with the results indicating changes of mean leaf traits through time. For example, the results show leaf mass per area to peak during the Eocene, which is in accordance with general climate development during the Paleogene.
For extant plants, the initiative "TRY" offers a large database containing traits of recent leaves and other plant organs  (Kattge et al. , 2011). Various differences exist between data collection of extant and fossil leaf traits. For example, while the amount and condition of material can be strictly defined for extant leaves, fossil assemblages are often heavily biased by taphonomic filters and preservation processes. Furthermore, the taxonomic affinities of fossil leaves are often ambiguous, unresolved, or their classifications are only referred to higher taxonomic ranks. Various traits are not directly accessible in fossil leaves (for instance, nitrogen content), or are difficult to measure or partially lacking (for instance, higher order venation). Considering the importance of fossil leaf traits in palaeoclimatology and palaeoecology, there is a high demand for available data.
Adding new data and techniques to increase the research potential of leaf traits is, therefore, of particular relevance  (Blonder and Enquist, 2014; Göldel et al. , 2015; Li et al. , 2016). Additionally, besides their value in palaeoclimatic and palaeoecologic studies, leaf traits are essential for taxonomy in palaeobotany  (Jones et al. , 1988; Boulter and Kvaček, 1989; Denk and Dillhoff, 2005; Walther and Kvaček, 2007; Kovar-Eder, 2016). This applies not only to qualitative characters, but also to morphometric parameters  (Thiébaut, 2002). Research approaches based on traits considerably benefit from internet resources making data-bases available for the public  (Dalitz and Homeier, 2004; Barclay et al. , 2007; Kattge et al. , 2011; Marine Species Traits editorial board, 2017). Databases focusing on fossil leaves and their traits, such as the Paleobotany Project  (Miller et al.
, 2005)  are rare. Here, we introduce the database "MORPHYLL" containing morphological leaf trait data obtained from fossil angiosperm leaves (http:// www. morphyll. naturkundemuseum-bw. de). The traits comprise directly measured quantitative (= continuous) parameters such as leaf lamina area and perimeter as well as qualitative (= categorical) parameters, such as presence or absence of leaf teeth or lobation. Furthermore, various indirectly obtained traits are included, such as LM A or leaf circularity  (Huff et al. , 2003). The term "leaf shape" will be used here for both qualitative and quantitative numeric leaf traits. In this context, leaf morphometric analysis is understood as the statistical study of leaf shape and their covariation with other, e. g. , environmental, variables  (Claude, 2008).
In contrast to most other existing databases, "MOR-PHYLL" offers a leaf trait based vegetation analysis tool independent of the taxonomic (phylogenetic) relationship of the leaf assemblages. The data are based on digital images obtained from the fossil leaf specimens, with subsequent transfer of the leaf outline into GIS shapefile, which is a well-established geospatial vector data format for geographic information systems (GIS). Morphometric data were extracted by spatial SQL-queries applied to GIS shapefiles. Both images and GIS shapefiles are stored in MORPHYLL, together with the extracted morphometric data and various metadata (specimen accession number, fossil site name with stratigraphic position, location of repository/ collection, etc. ). Choosing GIS as a tool for morphometric analysis offers various advantages such as flexibility, transparency of image processing, and easy re-analysis for further morphometric parameters.
Fossil Leaf Collections More than 20 collections of fossil leaf floras from eight European museums in five countries are currently included in the database (Table  1 ). The fossil leaves, with a stratigraphic range extending from the Paleocene to the Miocene, originate from Central European sites (Figure  1 , Table  1 ). According to  Mai (1995) , the floras from France, Belgium, Germany and the Czech Republic belong to the Atlantic Boreal Bioprovince, whereas those from Austria are assigned to the Transeuropean Paratethys Bioprovince. Only angiosperm leaves are currently considered in the database. The fossil assemblages are derived from various depositional environments, including fluviatile (e. g. , Svatava, Schleenhain, Borna-Ost/Bockwitz), volcano related settings (e. g. , Menat, Markvartice, Enspel), as well as allochthonous assemblages from marine envi-ronments (e.
g. , Rauenberg and the various Austrian sites). Thus, a wide range of habitats and depositional environments are represented in the database. The number of included fossil leaves per site/ collection varies thus far from a few (e. g. , Rott) up to several hundred specimens (e. g. , Kučlín, Enspel), depending on the collection size of integrated repositories. Some fossil sites such as Gelinden and Sézanne are represented in the database with different leaf collections stored at different museums (Table  1 ). Thus, MORPHYLL serves as a database merging dispersed fossil leaf collections stored at different European natural history museums. The taxonomic affiliations of the fossil specimens as provided in the database correspond to information from the inventory catalogues of the museums, from labels or from publications.
A complete revision of all leaves included in the database is beyond the scope of this internet resource. Collections which have not been recently taxonomically revised are included because various applications of leaf traits, such as CLAMP, do not rely on taxonomy. Furthermore, considering only recently revised collections would have severely limited the amount of data available. New taxonomic revisions will, however, be considered in the database as soon as they are published. Furthermore, revisions will also be facilitated by information provided by the database of the Muséum National d'Histoire Naturelle, Paris (https://science. mnhn. fr/institution/mnhn/collection/f/item/ search) including images of these collections. The data for the Geiseltal locality represents a special case since the 1090 specimens stored at the Museum für Naturkunde Berlin belong to a single taxon only, Polyspora hallensis RUFFLE (formerly Symplocos hallensis)  (Barthel and Rüffle, 1976).
Selection of Specimens To improve the overall quality of the data, only those leaves were included in the database, which show a preservation of at least about 60% of the FIGURE 1. Map with fossil sites which are currently included in the database. The numbers indicate the sites, as provided in the leftmost column in Table  1. lamina, because reconstruction of complete leaf outlines from very small leaf fragments can induce substantial errors. Leaf fragments, however, may reveal valuable morphological information independent of lamina outline, such as venation type or vein density. Thus, the database also contains some leaf specimens showing only a small subset of all potential leaf traits. Furthermore, leaf specimens showing very poor contrast with the surrounding sediment were excluded since no unequivocal image processing was possible.
In cases where both compression and impression of a single specimen are preserved, each is listed in the database as a single entry. The reason for this is that in many cases the compression and the impression differ with respect to identifiable traits. The central entity, i. e. , the basic database unit, is represented by a single leaf or leaflet. Thus, fossil leaves consisting of multiple leaflets (compound leaves) are split up into sub-entities -each represented in the database by its own accession number according to the collection catalogue of the hosting institution.
Image Acquisition and Processing The data were gathered by conducting several successive steps, starting with: (1) obtaining digital images from the specimens, followed by (  2 ) image processing, (3) leaf outline extraction, (4) calculation of leaf morphometric parameters and (5) data storage. Wherever possible, all compiled data and the database itself were generated by using free open source software (see list below). Images were preferentially produced by scanning the fossil specimen using a high resolution CanoScan-5600F characterised by a high depth of field. If the preservation status of the fossils did not allow for this procedure, depending on the situation onsite, fossil leaves were photographed by using a copystand with an attached Nikon-D5100 digital camera. For photographed leaves, a scale bar was mounted in the focal plane of the leaf lamina to exactly calibrate their size.