dataesr/scientific-paragraphs-categorization huggingface dataset:
In this context they are valuable as 'micropublications', acting as a landing-place for small bits and pieces of information on European species. This mostly concerns unpublished new distributional records, which are often direct or indirect spin-offs from the working on Fauna Europaea. Last but not least the Fauna Europaea data papers could also facilitate the publication of analyses based on the project, including small treatises (like master theses) or assessments carried-out by important stakeholders.
Acknowledgements The  Fauna Europaea  project bureau offers special thanks to the  Fauna Europaea Management, Steering and Advisory Committees , especially to  Prof. Philippe Bouchet ,  Prof. Daniel Goujet ,  Prof. Henrik Enghoff ,  Prof. Wieslaw Bogdanowicz ,  Prof. Alessandro Minelli ,  Anastasios Legakis ,  Miguel Alonso-Zarazaga ,  Trudy Brannan  and  Alfonso Nava Sanchez , for their commitment and guidance during the project phase of  Fauna Europaea. We are grateful for the many in-kind contributions from involved taxonomic institutes to  Fauna Europaea. Here , the successive CETAF chairs and the current director  Ana Casino Rubio  are thanked for their continuing help on profiling  Fauna Europaea  as a European Clearing House on taxonomic expertise.
CATdb (http://urgv. evry. inra. fr/CATdb) is a database providing a public access to a large collection of transcriptomic data, mainly for Arabidopsis but also for other plants. This resource has the rare advantage to contain several thousands of microarray experiments obtained with the same technical protocol and analyzed by the same statistical pipelines. In this paper, we present GEM2Net, a new module of CATdb that takes advantage of this homogeneous dataset to mine co-expression units and decipher Arabidopsis gene functions. GEM2Net explores 387 stress conditions organized into 18 biotic and abiotic stress categories. For each one, a model-based clustering is applied on expression differences to identify clusters of co-expressed genes. To characterize functions associated with these clusters, various resources are analyzed and integrated: Gene Ontology, subcellular localization of proteins, Hormone Families, Transcription Factor Families and a refined stress-related gene list associated to publications.
For the last 20 years, high-throughput technologies have made it possible to assess the behaviors of genes in a broader context and co-expression studies based on transcriptomic data are now considered to be a relevant approach to characterize and decipher the function of genes  (3) (4) (5) (6). CATdb  (7)  can now contribute to take up this challenge. Originally CATdb was developed to manage the Arabidopsis microarrays data generated by the URGV transcriptomic platform (http://www-urgv. versailles. inra. fr/microarray/). CATdb provides access to a large collection of transcriptomic data for A. thaliana, which were all obtained with the two-color CATMA (Complete Arabidopsis Transcriptome Micro Array) microarrays  (8). The design of the CATMA microarrays has been regularly updated and includes almost all the annotated genes of Arabidopsis, combining the official annotation of TAIR (The Arabidopsis Information Resource  (9) ) with gene predictions from the EuGene software tool  (10).
Thus, the 'home made' CATMA microarrays are distinct from other Arabidopsis microarrays, for example CATMA includes 5095 genes that are not targeted by the commonly used ATH1 GeneChip R microarray from Affymetrix and this specificity has been successfully exploited in the past to discover new genes  (11). The latest version of CATMA (CATMAv7) targets 35 656 genes. Finally, since its first release, the information system of CATdb has been upgraded to follow the technological developments of the platform. Currently, CATdb stores transcriptomic data for 20 distinct species obtained with four DNA chip technologies and totalizing 4838 CATMA, 1464 Affymetrix, 1208 NimbleGen and 48 Agilent microarrays. At present, the database includes 282 projects (231 for Arabidopsis and 51 for other species) and contains almost 10 000 hybridized samples (five times more than in 2008 for the first CATdb publication (1)), which are publicly accessible at http://urgv.
evry. inra. fr/CATdb. For each project, a complete description going from the experimental design to the detailed features of the samples, the normalized intensity ratios and the results of differential expression analysis are available. The 4613 distinct samples represent 28 organs of the plant harvested at 132 development stages. Out of all samples, 1657 are extracted from plants treated with one of the 186 factors described in CATdb. Hence, the CATdb content increases regularly and includes a large range of experiments around the transcriptomic data for various plants. In this article, we present a new module of CATdb called GEM2Net. Its main objective is to provide a global and comprehensive overview of the co-expression units of genes responding to a panel of stress stimuli. Our approach relies on a model-based clustering method applied to a carefully selected set of CATMA experiments dealing with the transcriptome of Arabidopsis under various stress conditions.
Compared to other Arabidopsis resources like Genevestigator  (12) , the Stanford Microarray Database (13), GeneMania  (14) , MapMan  (15)  or ATTED-II  (16, 17) , GEM2Net contains complementary and original features. As pointed out by Horan et al. (18) , the results of such studies might suffer from the heterogeneous origins of the data. Moreover, in these latter resources, the co-expression is measured by computing the correlation between all pairs of gene across microarray datasets. In contrast our model-based clustering approach is meant to detect groups of genes and not only pairs. Besides, the discovery of new genes involved in plant response to biotic and abiotic stresses constitutes an important challenge in plant biology with relevance to agriculture and ecology since it could represent a potential starting point for plant breeding.
To date, few databases related to plant stresses have been developed: the Stress-Responsive Transcription Factor Database  (19) , Arabidopsis Stress Responsive Gene Database  (20)  or Plant Stress Gene Database  (21). These databases provide access to a list of curated stress genes extracted from literature, but this quality of information is incompatible with a global analysis. At a genome scale, Lan et al. (22)  predicted new stress response genes by combining machine learning methods on genes with known functions and described by transcriptome data. In this article, we developed the main original features of GEM2Net: (i) the global analysis of a homogeneous and dedicated transcriptomic dataset, (ii) the use of a modelbased clustering approach to study gene co-expression and (iii) a set of bioinformatic developments and tools to integrate, analyze and visualize the meta-data that characterize each gene co-expression unit.
Gene set enrichment tests The enrichments of clusters in GO Slim terms, subcellular localization terms, orphan genes, transcription factors (TFs), hormones or stress-triggered genes in literature were assessed using a hypergeometric test to compare the number of genes in each cluster associated to the studied meta-data to its expected value in the genome (34 042 genes). Overrepresentation was declared statistically significant when the P-value was lower than 0. 01. In the case of proteinprotein interactions (PPI), we used a permutation approach to assess the significance of the number of PPI in a cluster. In brief, for every cluster, we counted the number of PPI and denoted this number k. Then we randomly sampled 1000 clusters of the same size and counted the number of PPI in each of those random clusters.
Database and web implementation CATdb was implemented using the PostgreSQL (v9. 1. 2) RDBMS. Attention was brought to the normalization process during conception, whereas pertinent indexes were created to improve performances with tables that need to be intensively queried. A Web interface for accessing GEM2Net content was developed with the PHP (release 5. 3. 3) language to benefit from its integration as a module of Apache HTTP server (https://httpd. apache. org/). Objectoriented programming concepts were applied to take advantage of the easy maintainability qualities in development of a complex interface. Dynamic rendering was added using Javascript libraries (Jquery, Json) and functions, in order to facilitate the navigation between pages and then improve the user experience. The Cytoscape Web software tool (v1.
Transcriptomic dataset To provide a global insight into the plant response to an environmental change or biological attack, a set of CATMA microarrays (387 dye-swaps) dedicated to biotic or abiotic stresses were extracted from CATdb. Their normalized expression differences were the starting point of this metaanalysis project. All these data have the rare advantage to have been generated with the same technical protocols and the same statistical pipelines including the normalization and the differential analysis (refer to Gagnot et al. (1)  for details). Overall, GEM2Net explores 18 stress categories (Figure  1 ) describing nine biotic and nine abiotic stresses. To define the set of genes to be considered in each stress category, some criteria were taken into account: (i) only genes for which a probe with a good specificity and without missing values were mined, (ii) raw P-values of these genes for each comparison were adjusted to control a global FWER (corrective term equals the number of genes by 387) and only genes for which at least one of the adjusted Pvalues was lower than 0.
Co-expression analysis To directly identify co-expression units of several genes, the co-expression analysis was performed with a model-based clustering approach. The idea is that there exist unknown subpopulations that are observable through the expression differences. Model-based clustering aims at identifying this underlying structure in modeling the unknown distribution of transcription profiles by a mixture of parametric distributions, each one representing a subpopulation of genes. Since observations are the expression differences, a subpopulation represents a cluster of genes having the same dynamic of response across all the comparisons. Practically, multidimensional Gaussian mixtures of 2-100 subpopulations with unequal proportions were estimated with the MIX-MOD software  (23). Covariance matrices were constrained so that their volumes were differing and their orientation and shape were equal.
This threshold was fixed for each analysis so that as many genes as possible were classified, under the constraint that the proportion of misclassified genes is controlled at a level of 5%. This classification rule is called Multi-class False Discovery Rate (MFDR)  (24)  and is an extension of the previously described BFDR  (25). Following this procedure, a total of 681 clusters equal to co-expression units were identified for the 18 stress categories. According to the MFDR rule, the percentage of classified genes with high confidence ranged from 20 to 67% and out of the 17 264 analyzed genes, only 8% of them (1469 genes) were never classified with high confidence. An example of a co-expression profile is presented in Supplementary Figure  S1  and all the co-expression profiles are accessible in GEM2Net module.
The quality of a cluster can be evaluated through the size of the boxplots: a boxplot with a reasonably small size means that all the genes have the same dynamic of response. Although the biological replicates are coherent at the level of the whole set of genes, the clustering method highlights that some biological comparisons may behave differently for a given subset of genes. Aubourg et al. (11)  have already observed this phenomenon. On the top of the profile, the percentage of genes differentially expressed is indicated for each comparison. It can be useful to identify the comparisons that are relevant for the cluster under study. Comparison of the number of genes between Reference (all Arabidopsis genes) and GEM2Net dataset for the following meta-data: Orphan genes; BP stress gathers two terms of Biological Process from GO ('response to stress' and 'response to abiotic or biotic stress'); Bibliostress lists the stress-responsive genes with related bibliography extracted from GO; TF is a list of genes characterized as TFs in the Regulators project; Hormone is a list of genes having a link with hormone response as annotated in the AHD2.
From this classification, we extracted a list of genes known to be involved in stress response and having an associated publication (we named this list Bibliostress). Afterward, the meta-data were refined by considering databases specialized in families of Arabidopsis genes for both significant functional groups involved in stress, i. e. the TFs from the Regulators project  (30)  and the hormone-related genes from the Arabidopsis Hormone Database (AHD 2. 0)  (31). Whatever the source of an- notation chosen, a cluster was qualified as 'enriched for an annotation' if the result of a hypergeometric test was significant for this cluster (P-value < 0. 01). The main steps of the analyses workflow are described in Figure  2. A global view of the numbers of annotated genes regarding a series of meta-data is summarized in Table  1 , and highlights some characteristics of the GEM2Net dataset compared to the Arabidopsis genome (the Reference).
Out of the 5105 orphan genes from the Reference, 2165 (42%) were found to be impacted by stress in the GEM2Net dataset; this was close to the expected value of 50% in view of the proportion in all genes (17 264/34 042). Moreover, an equivalent distribution of orphan genes was found in biotic, abiotic and the individual 18 stress categories (Supplementary Table  S2 ). The stability of the number of orphan genes points out a regular distribution of the knowledge level throughout stress categories, even if the number of experiments was not equivalent. In regard to the gene annotation related to stress, an expected enrichment is found for the GEM2Net dataset with 23% of genes annotated with GO terms linked to stress (BP stress column) against 15% in the Reference set, and 12% against 7. 5% for the Bibliostress meta-data.
Visualization of meta-data The GEM2Net Web interface (http://urgv. evry. inra. fr/ GEM2NET) allows users to query the database by stress category or to submit a list of genes of interest to retrieve the stress categories they are implicated in. It is possible to answer questions like 'are my genes of interest involved in the same co-expression unit?' by exploring graphically the results of meta-data analyses. To identify at a glance and summarize the potential functional biases of each cluster, GEM2Net proposes an original representation and interactive visualization, using pie charts and graphs. For each stress category, cluster annotation analyses are divided into several tabs, one per meta-data type, which allow the or- ganization of complex and abundant results. In such tabs, a central panel displays every cluster as a pie chart whose diameter is directly related to the total number of genes in the cluster.
The complete list of 213 genes of cluster 49 appears in a table sorted by gene roles in the selected meta-data type and highlights their implication in other biases by colored circles. GEM2Net integrates protein interactomes and targets of TF data from external resources (Supplementary Table  S1 ), so gene clusters can be viewed as interactive biological networks (Figure  5 ), thanks to the embedded Cytoscape Web software tool  (33). Adding gene annotation, e. g. TFs families, GO terms or choosing experimentally confirmed PPI, makes it easier to predict regulatory networks of biological relevance and to identify new functional partners. There-fore, by combining gene annotation corresponding to relevant meta-analyses with the available interaction networks in GEM2Net, it is possible to gather clues to infer the biological functions of some orphan genes in a co-expression unit.
DISCUSSION The new CATdb module, GEM2Net, was developed to summarize the transcriptomic responses of Arabidopsis to various stress conditions. The goal is to provide new information that will give new insights into plant stress responses and orphan genes involved in these responses when they are crossed with other experimental data or knowledge. The major outputs of GEM2Net are (i) the classification of several CATdb projects in biotic and abiotic stress categories, (ii) a global co-expression analysis using a modelbased clustering approach that is not so often used in genomic analyses, (iii) a visualization system that summarizes rapidly the cluster annotation enrichments in terms of Gene Ontology, genes cross-referenced in stress-related bibliography, hormone and TF families, (iv) the gene interaction networks constructed with protein interactome data or TF-target interactions that are involved in each cluster.
To progress further, we plan to integrate other types of metadata, such as the cis-regulatory motifs detected in promoters of genes belonging to a same cluster  (34). This may give the possibility to associate a cis-regulatory motif to a par-D1016 Nucleic Acids Research, 2015, Vol. 43, Database issue within the cluster 49 are represented with dark blue lines, using the Cytoscape Web software tool. Functional annotation is superimposed on nodes by selecting the corresponding checkbox above, hormone families (in blue) and orphans (in red) here. On the right frame, filters on GO categories can be applied to the network to view only nodes of the selected annotation. In addition, a 'Targets of Transcription Factors' option is available to display this type of interaction in the same network. ticular stress stimulus, thus providing a valuable resource to complete interactions between TFs and their targets.
To refine the co-expression units, we would update data with new transcriptome comparisons in each stress category. Thanks to the model-based clustering approach, it does not necessitate restarting completely the co-expression analysis. For instance, it was recently applied to investigate the roles of MAP kinases in Arabidopsis immune response to a microbial stress  (35). Therefore, GEM2Net is an ongoing project and new meta-analyses will be released in the future to share the results with a large scientific community. To conclude, GEM2Net aims at taking advantage of using controlled data associated to consistent meta-data to define relevant co-expression clusters, to improve their annotation and, thus to enhance the predictive power of assigning the right functions to orphan genes. Besides, since the plant responses to abiotic and biotic stresses are regulated by complex signaling networks and are associated with massive changes in gene expression, the GEM2Net results also allow building new hypotheses that might be the starting points for future biological projects.
ACKNOWLEDGEMENTS The authors thank  G. Celeux  ( INRIA ),  C. Maugis-Rabusseau  ( INSA Toulouse ),  S. Huet  ( INRA ),  C. Keribin  ( University of Paris-Sud ),  N. Verzelen  ( INRA ) and  C. Giraud  ( University of Paris-Sud ) for helpful discussions within the working group SONATAStat. The authors thank  Philippe Grevet  for computer system administration and PostgreSQL management at URGV.
Laughter Installations Previous laughter detection and response systems have generally used a limited human-avatar interaction. Fukushima et al. [15]  built a system that enhanced users' laughter activity during video watching. It comprised small dolls that shook and played prerecorded laughter sounds in response to users' laughter. AVLaughterCycle aimed to create an engaging laughter-driven interaction loop between a human and the agent  [52]. The system detected and responded to human laughs in real time by recording the user's laugh and choosing an acoustically similar laugh from an audiovisual laughter database. The Laugh Machine project endowed a virtual agent with the ability to laugh with a user as a fellow audience member watching a funny video  [53, 33]. The agent was capable of detecting the participant's laughs and laughing in response to both the detected behaviour or to pre-annotated humorous content of the stimulus movie.
Innovation: Multimodality As already explained, the Laugh When You're Winning project builds upon the Laugh Machine project that was carried out during eNTERFACE'12. The major innovations with regards to Laugh Machine or other installations are the following (these innovations will be further detailed in Sections 6 and 7): -The laughter detection module has been extended to take multimodal decisions: estimations of the likelihoods of Smile, Speaking and Laughter likelihoods result from analyses of audio, facial and body movements, while in Laugh Machine there was simply a laughter/no-laughter detection based on audio only; in addition, the intensity estimation module has been improved (a neural network was trained under Weka). -Several modules exchange information in real time for detection and analysis of laughter: the master process is Social Signal Interpretation (SSI) but some computations are outsourced to Eyesweb and Weka.
Social Signal Interpretation (SSI) The recognition component has to be equipped with certain sensors to capture multimodal signals. First, the raw sensor data is collected, synchronized and buffered for further processing. Then the individual streams are filtered, e. g. to remove noise, and transformed into a compact representation by extracting a set of feature values from the time-and frequency space. In this way the parameterized signal can be classified by either comparing it to some threshold or applying a more sophisticated classification scheme. The latter usually requires a training phase where the classifier is tuned using pre-annotated sample data. The collection of training data is thus another task of the recognition component. Often, activity detection is required in the first place in order to identify interesting segments, which are subject to a deeper analysis.
Finally, a meaningful interpretation of the detected events is only possible in the context of past events and events from other modalities. For instance, detecting several laughter events within a short time frame increases the probability that the user is in fact laughing. On the other hand, if we detect that the user is talking right now we would decrease the confidence for a detected smile. The different tasks the recognition component undertakes are visualized in Figure  1. The Social Signal Interpretation (SSI) software  [54]  developed at Augsburg University suits all mentioned tasks and was therefore used as a general framework to implement the recognition component. SSI provides wrappers for a large range of commercial sensors, such as web/dv cameras and multi-channel ASIO audio devices, or the Microsoft Kinect, but other sensors can be easily plugged to the ystem thanks to a patch-based architecture.
It also contains processing modules to filter and/or extract features from the recording signals. In addition, it includes several classifiers (K-nearest Neighbor, Support Vector Machines, Hidden Markov Models, etc. ) and fusion capabilities to take unified decisions from several channels. In this project, SSI was used to synchronize the data acqusition from all the involved sensors and computers, estimate users' states (laughing, speaking or smiling) from audio (see Laugh Machine project  [33] ) and face (see Section 6. 1, as well as fusing the estimations of users' states coming from the different modalities: audio, face anlysis and body analysis ((outsourced to Eyesweb, see Section 6. 2).