dataesr/scientific-paragraphs-categorization huggingface dataset:
Altogether, this resource has participated to improve the reproducibility of research in the field of VH interactomics, allowing scientists to compare their own experimental dataset and the properties observed from the underlying reconstructed protein interaction networks to those published previously. Hence, the VirHostNet resource was cited more than 70 times since its publication in the 2009 NAR database issue (source Google Scholar, September 2014). We have also demonstrated the scientific interest and the quality of such data, by providing new clues on convergent strategies selected by viruses to hijack and evade from the host innate immune functions (autophagy, response to type I interferon) as well as the role of viruses in the development of complex infectious diseases  (2, (8) (9) (12) (13).
Despite the growing numbers of virus/virus (VV) and VH ppi dataset published in the last 5 years  (14) , only few other large-scale annotation efforts were reported so far, excepted those performed by Intact  (15) , VirusMint  (16, 17)  and a new resource, HPIDB  (18). In this issue, we present our last VirHostNet 2. 0 curation update that leads to a 5-fold enrichment of the VH ppi data available in VirHostNet 1. 0. Comparison to other resources shows that more than 80% of our annotations are unique to VirHostNet 2. 0, emphasizing the high sensitivity of our curation approach. An implementation of a PSIC-D584 Nucleic Acids Research, 2015, Vol. 43, Database issue QUIC web server  (19)  is provided to make this dataset available in a standard way. In the VirHostNet 2. 0, the model and the data were extended to non-human hosts and their associated viruses.
These data were complemented with annotations extracted from the eight external databases using the PSICQUIC EBI web service. The biological relevance of our combined VH ppi dataset was evaluated by using VH species relationships annotations provided by UniProt  (20)  and Viralzone  (21). Finally, we have also dramatically improved visual access to our unique resource, by developing a new web interface based on HMTL 5 technologies. This new development relies on a system-level modeling framework that significantly increases the interactivity between graph and tabular representation of VH ppi.
Biocuration and integration of VH ppi data The VirHostNet 2. 0 ppi data (September 2014) were obtained from multiple and complementary sources. First, intraspecies (VV and HH) and interspecies (VH) ppi were manually annotated from the scientific literature using the PSI-MI TAB 2. 5 format  (22). Hence, each ppi is manually annotated according to the spoke model, by using the accession numbers of the two protein partners (UniProt or Refseq primary accession number are used), the PSI-MI method accession number describing the experiment and the PubMed publication id (PMID). This approach has the advantage to rapidly increase the sensitivity of the VH and VV ppi dataset as compared to more accurate annotation strategies (PSI-MI TAB 2. 6 and 2. 7), without loosing any information concerning the redundancy of experiments.
Moreover, this solution will provide a solid foundation for the next runs of annotation in the richer PSI-MI TAB 2. 6 or 2. 7 formats. All the VirHostNet annotations are made publicly available at EBI, throughout a PSICQUIC web service  (19). These data were next completed (update will follow the UniProt release frequency) with ppi data extracted from eight high-confidence external databases (including IntAct, Mint, DIP, InnateDB, BIND, UniProt, HPIDb) by using the EBI PSICQUIC web service. To overcome the heterogeneity of gene/protein accession numbers, all protein entries are first mapped onto UniProt (or NCBI/Refseq if not available) primary accession numbers by using the UniProt and NCBI cross-references. In the VirHostNet release 2. 0, we also take into account viral protein domain interactions by using domain definition provided by UniProt and Ref-Seq (mature peptide).
An up-to-date VH ppi dataset extended to non-human hosts Our new round of biocuration results in a 5-fold enrichment according to the previous release 1. 0 of VirHostNet. Over 30 000 ppi are now annotated, with a total of 16 000 VH, 2 500 VV and 12 000 host/host (HH) ppi (Figure  1a , Supplementary Table  S1 ). A comparison to the data stored in known available databases reveals that more than 80% of annotated interactions are unique to the VirHostNet 2. 0 resource, confirming the relatively high sensitivity of our fast annotation approach. It is interesting to note that 50 and 42% of VH ppi data were obtained by TAP and Y2H experiment, respectively (Figure  1b ). Moreover, more than 33% of the VirHostNet 2. 0 ppi are supported by at least two independent experiments, suggesting the high quality of those interactions (Figure  1c ).
The VH ppi were next flagged according to the information of VH species relationships provided by UniProt  (20)  and Viralzone  (21). This annotation step allows us to better control the true nature of annotated interspecies interactions and to clearly differentiate ppi related to biological VH relationships from more artificial ones. Hence, we show that more than 90% of the VirHostNet 2. 0 curated ppi corresponds to biological VH species relationships, suggesting the high specificity of the available data and annotations. Nevertheless, even when considering the combined dataset, we note a poor coverage of the VH interactions space (less than 2% of the 5958 VH relationships). Hence, at the viral 'species' rank (as defined by the NCBI taxonomy classification), only 107 known VH relationships are described by at least one molecular interaction.
This corresponds to 259 viral taxons when considering the diversity of viral strains for each viral species. Moreover, at least one molecular interaction experiment has been described for 11 distinct host species (Supplementary Table  S1 ), including five vertebrates, one invertebrate, three plants and one prokaryote, with a strong inspection bias toward virus/human ppi (99% of the ppi dataset). This result clearly reflects the research and the annotation efforts to identify the molecular basis of viral replication and pathogenesis in human. However, the number of non-yet annotated VH ppi is growing for non-human hosts  (23) , such as animals (Bos Taurus, Sus scrofa, Gallus gallus) and plants (Arabidopsis thaliana) of agronomical interest. Hence, to better anticipate this trend, we decided to extend the previous model of VirHostNet to all known viral hosts.
Hence, a set of graph metrics (degree, betweenness, closeness, transitivity of nodes), frequently used in the fields of interactomics to better characterize network properties, were precomputed with the igraph R package for each network (VH, HH, V2H pin) and stored into our knowledgebase. The graph visualization rendering and coloring scheme presented below is based on these three network models. Hence, visualization of the nodes and edges graph properties will.
A brand new VH ppi network viewer We have developed a new web-based VH ppi viewer (aka VirHostScape) based on Cytoscape web  (24) , jQuery and Bootstrap libraries. This web interface is available at http: //virhostnet. prabi. fr and allows users to rapidly query and visualize proteins and ppi data in the context of VH, HH and V2H pin. A wiki documentation and a short tutorial are provided on the website. The VirHostScape 'home' page is composed of (i) a 'quick-search' text area, (ii) a BLASTP  (25)  form and (iii) a taxonomic browser (see Figure  2  and the wiki for more details). The 'quick search' form allows--in 'simple search' mode--to retrieve direct partners of a protein query based on UniProt and RefSeq accession numbers. When selecting 'multiple search', the user can enter multiple protein accession numbers and visualize interactions among them.
The web interface provides also functionalities to visualize protein interactions between a list of proteins annotated (i) at a NCBI taxonomic rank, (ii) with a specific domain composition (Interpro), (iii) acting in a specific pathway (KEGG) or (iv) annotated from a publication referenced by its PMID. All search forms are tuned with autocomplete functionality. In addition, a 'BLAST' form allows the user to make homology search against a database containing all the sequences of the protein partners stored in VirHostNet 2. 0. This is a simple way to rapidly predict putative partners of an unknown protein sequence or a variant, based on the assumption of interology (i. e. the conservation of ppi between two protein partners and their homologs) (see the wiki page for more details and a case study).
It also provides a useful taxonomy summary of the protein interaction network (the number of viral and host proteins, the number of VH, VV, HH ppi). We also propose a rich 'option panel' that helps users to colorize, resize and filter graph nodes (proteins) and edges (ppi) according to multiple and complex criteria, including (i) taxonomy, (ii) molecular interaction experiments (number of independent experiments, independent experimental methods, publications or independent database supporting the interaction) and (iii) graph properties of nodes and edges pre-computed in the three graph models (see section above). The pin generated by Cy- toscape web libraries is automatically rendered using a default coloring scheme (nodes and their borders are colored according to UniProt division, edges are colored according to their types: red--for confirmed VH interspecies and VV intraspecies interactions, blue--for HH confirmed ppi, black for other artificial interspecies interactions) and resizing rules (the size of nodes is proportional to the degree in the V2H pin; the width of edges is proportional to the number of independent experiments supporting the interaction).
CONCLUSION VirHostNet 2. 0 is to our knowledge, one of the most comprehensive datasets of VH ppi data available today. The overall quality of VH ppi annotations was improved a step further by integrating VH species relationships provided by UniProt and Viralzone. All these annotated ppi data are made available to the worldwide community in PSI-MI TAB 2. 5 throughout a PSICQUIC dedicated web service. We hope this unique resource will continue to serve as a gold standard in the interpretation of VH interactome data. Our new HTML 5. 0 web interface, VirHostScape allows fast reconstruction and visualization of VH protein interaction networks and will facilitate reproducible research. Our BLASTP web service will help Virologists to rapidly infer novel VH ppi from viral protein sequences or new variants.
The communication between modules is made possible by client-server style architecture. A module providing data is the server that listens to clients that request a subscription. When data on the server is changed, the new values are sent to all clients that have registered their interest to this message. The clients can then use the new data to perform the task of the module (e. g. refresh the display). The system is asynchronous and data driven. If there is no new data, no new message will be generated and no action performed by any software module. In ARCS, every application is described as a set of threads. Basically, a finite state machine,which states represent a specific configuration of the application's data flow controls each thread. Such a configuration is called a sheet and contains configuration values for components as well as a list of signal/slot connections.
Each change of state in the state machine results in a change of global configuration of components and hence reconfigures connection between components, that is to say the dataflow. A simple example would be an application with one automaton (one thread), with a given number of sheets (eg. each sheet representing a given scenario). Here, the state machine's role would be to switch from a scenario to another. From the technical point of view, ARCS is written in C++ and is based on Qt Library, which already implements the signal/slot concept. ARCS supports XML and JavaScript as scripting languages to describe applications behavior. The framework also supports distributed components via a specific middleware and webservices in standard formats (XML, JSON). The performance of distributed applications based on ARCS has been assessed with a methodology dedicated to AR distributed systems  [19].
4. 1. System architecture The system is constituted of five main types of sites (network nodes) that are: • ROV site (usually in the sea / pool); • ARCS module (Mixed Reality application site); • 3D repository site where are stored 3D content (e. g: fauna and flora models); • Web server which hosts the web application; • User site (can be several users on different sites). Figure  1  illustrates the relationships and data streams exchanged between the five sites. Except the ROV site, all the other sites may be regrouped depending on the application scenario. The architecture has been built this way to offer more flexibility. Basically, the system works as follows. The user controls the robot via the web user interface. The web GUI allows high level commands that are composed of several instructions transmitted to the ROV.
The robot equipped with a set of sensors (including two cameras, but only one can is used in our case) sends a continuous data stream to the ARCS main application, which creates the augmented scene and sends out the final output data (video stream and other information) that is then available to the user who can build a custom view of the scene. The internal architecture (see figure  3 ) of the Web Application is divided into three parts. The internal architecture involves as well: a web page written with HTML5, CSS 3 and JavaScript (JS), a PHP script in order to communicate with the ROV using Modbus TCP protocol and a PHP controller. Modbus is a serial communications protocol published in 1979. The controller is designed to manage all data streams between modules: • Robot instructions, which are high level commands, created by users by clicking on the interfaces buttons (e.
g. go forward, turn left, etc. ). The PHP Modbus script will convert those high level commands to Modbus commands (e. g. : ROV Address + Read/Write code + First memory registry code + Number of bytes + Data to send + CRC16); • Robot data, which are sent by the ROV (e. g. sensors status, error, etc. ). A specific module called Robot Computer Interface (RCI) permits to link the ROV to the network (see figure  3 ). It is composed of four components: • Power manager; • Battery; • Control and video module: convert LAN/WLAN data into RS485 data; • Video demodulation: convert PAL/NTSC video signal into a numeric video signal. The web application can also receive data from ARCS. This is a specific data stream which contains data computed by ARCS application in order to allow a local augmentation if many users are viewing the live video (e.
4. 2. Web application user interface The Web Application is written in PHP, HTML5, JavaScript and CSS3. The user interface (see figure  4  below) is a human machine interface in order to send commands to the ROV (teleoperation), supervise sensors data from ROV (virtual dashboard) or enable a user to interact with other features (chat with other divers or get more information via web services, such as weather, maps, geo-localized pictures, historical information about the area…) in the web page.
4. 3. Multimodal application user interface The user interface of the multimodal application also permits to send commands to the ROV (teleoperation), supervise sensors data from ROV (virtual dashboard) using natural gestures. Users control a virtual ROV using the markers placed on the flystick device (Figure  5 ). Those markers are tracked with the infrared cameras placed on both sides of the screen. This system tracks the user's positions, so we can reproduce it on the ROV. Hence, by manipulating a virtual ROV, the operator is controlling the real one. We also use the metaphor of pulling a rope. Indeed, during his/her movements, the user must hold the trigger of the fly stick pressed. When he/she releases it, the ROV stays in its last position and the user is free to move. At the next displacement, the ROV starts from its last position and not from the user's current position.
5. ARCS-based module Built following the standard ARCS application pattern which itself is based on the generic Augmented Reality application pattern introduced by Asa McWilliams  [17] , the application is thought support multiple distant users. The architecture of the middleware that has been designed for ARCS can be found in  [16]. The model of the application is data driven. Video stream coming from the ROV implies that for each new frame processed by the video stream reader, the tracking component computes visible 3D entities and their position (3D registration). ARCS offers a framework to support several 3D rendering engines and already supports OGRE 3D and OpenInventor. Depending on the chosen rendering engine, the corresponding component creates then the final representation blending the raw input with 3D contents taken from a potentially distant database.
8. 2. Mixed Reality with ARCS Real-time scene augmentation has been performed following three custom scenarios. First is contextual scene augmenting (figure  11 ), that only consists in adding virtual entities for immersive and decorative goals (ex. Adding specific species of fish depending on environment or adding virtual funny fish in a swimming pool, etc. ). The second test is based on marker tracking (ex. Display information on a marked zone or object such as a pipe or a wreck). In our example (figure  12 ), those markers are set on buoys. Applications such as collaborative treasure hunting game and virtual fish encyclopedia have been developed (Figure  13 ). This virtual encyclopedia provides 3D models of fish, and information from a database such as name, natural environment and zones of the world where it can be found.
Other information such as maximum size, maximum depth, etc. are available in the database and can also be displayed. One can easily build new applications with very little effort of development since ARCS offers great reusability of components and parametrisation ability. The third and last one is based on natural features tracking (ex. Automatic fish classification). A preliminary test has been made with the SURF  [18]  (Speeded-Up Robust Features) component of ARCS (which is based on OpenSURF library), but a more complex application is being built based on several descriptors.
The user can also define specific characteristics of the fish to detect (color, size…) as shown in figure  13. Then, for classification and identification purposes, each region of interest is described with a features vector including oments of the shape, color information extracted from the color histogram and texture The shape descriptors are usual blob descriptors (centroid, area, elongation, and statistical moments). Color descriptors are also based on statistical moments. For texture descriptors, several options have been studied. Haar-like features being very sensitive to rotation, we decided to combine statistical properties with spectral and structural texture analysis methods. The feature vector is then matched with the features vectors in the known fish database (the application has been tested only with 3 different species at the moment).
A score is then computed and if it satisfies a minimal threshold, the detected fish is identified as a member of a specific species. This application is still in development and it still needs implementation of some of the feature descriptors and validation with a dataset of decent size. The final goal is to identify fish and augment the scene with in real time with information about the identified fish using any distant tablet or even smartphone with access to Internet. Within this project, a special device has also been developed to visualise augmented reality contents underwater  [20].
Using hierarchical Bayesian modeling, we both infer population structure and identify outlier loci that are candidates for local adaptation. As outlier loci, the hierarchical factor model searches for loci that are atypically related to population structure as measured by the latent factors. In a model of population divergence, we show that the factor model can achieve a 2-fold or more reduction of false discovery rate compared to the software BayeScan or compared to a F ST approach. We analyze the data of the Human Genome Diversity Panel to provide an example of how factor models can be used to detect local adaptation with a large number of SNPs. The Bayesian factor model is implemented in the open-source PCAdapt software.
Each outlier locus can be related to one of the K factors because outlier loci should be atypically explained by one of the K factors. To measure for each locus the strength of evidence for outlyingness, we compute the Bayes factor of the outlier model against the non-outlier model. If a locus is considered as an outlier, the factor with which there is an atypical correlation is found by computing the posterior probabilities of each of the K components of the outlier mixture model. To account for Linkage Disequilibrium (LD), we additionally consider a Potts Model that encourages outlier loci to be clustered in the genome (see METHODS for details). When fitting the factor model with K = 3 to data simulated under the scenario of population divergence depicted in Figure  1.
Analysis of human SNP data The HGDP dataset contains 644, 199 SNPs, after removal of the SNPs on the sex chromosomes and on the mitochondrion, which have been typed for 1, 043 individuals coming from 53 different populations  (Li et al. 2008). First, we fit the factor model with different values of K. By contrast with the two previous examples, there is no value of K at which the MSE stops to decrease (Figure  S3 ). By looking at the different factors (Figure  5  and Figure  S6 ), we decide to consider a model that captures genetic differentiation between but not within continents. Using this criterion, we consider a factor model with K = 4 since larger values of K would reveal genetic difference within continents (Figure  S6 ). The first factor mostly contrasts African from Asiatic and Native American individuals and the second factor mainly discriminates African from Middle-Eastern and Western Eurasian individuals.
• For the first factor, although we consider ten different SNPs, only two genomic regions are found. One of the two genomic regions is located on chromosome 10 and downstream of the oncogene CYP26A1 whose expression is enhanced in sunlight-damaged human skin  (Osanai and Lee 2011; Mallick et al. 2013). The other SNPs were found in the SM6 gene which is implicated in the structural maintenance of chromosome protein 6 and which has already been picked as a candidate for selection in another scan with the HGDP sample  (Hao et al. 2013). For all the ten SNPs, we investigate the worldwide repartition of allele frequencies with the ALFRED database  (Rajeevan et al. 2012). East Asiatic and Native American populations have allele frequencies that are different from the rest of the sample (Table  S2 ) as can be predicted when looking at the geographic repartition of the first factor (Figure  5 ).
Discussion Based on a Bayesian factor model, we provide a new approach for performing genome scan of local adaption. The hierarchical factor model considers as outliers the SNPs that are atypically related to population structure. A set of K latent factors measure population structure and they can adequately describe clustering of individuals into populations (Figure  1 ), isolation-by-distance patterns and gradients of selection (Figure  4 ). Compared to the software BayeScan or to genome scans based on F ST , the factor model does not assume a particular model of population structure. In a model of population divergence, we show that removing the assumptions of the F -model considerably reduces the false discovery rate. To explain why the factor model generates less false discoveries, we introduce the notions of mechanistic and phenomenological models  (Hilborn and Mangel 1997).