Search

Background Telomerase reverse transcriptase promoter (pTERT) mutation is a dominant altered telomere maintenance mechanism in primary glioblastomas (GBMs). Objective The aim of this study was to correlate pTERT mutations with clinico-histological features and other molecular markers (p53 protein-expression, ATRX protein-expression, IDH mutations, EGFR gene amplification and MGMT methylation) in adult GBMs. Materials and methods Evaluated for histological patterns, p53 and ATRX protein expression by immunohistochemistry (IHC), IDH mutations by IHC followed by sequencing in IHC negative cases, EGFR gene amplification by fluorescence in situ hybridization, MGMT promoter methylation by methylation-specific PCR and pTERT mutation by sequencing. Results A total of 155 adult supratentorial GBMs [age-range 20-80 years] formed study cohort. 15.6% were IDH1R132 mutated, none were IDH2R172 mutated and 27% were EGFR amplified. 43% were MGMT methylated and were more common with IDH-mutation (mIDH) than EGFR amplification. 90% of mIDH (but no EGFR amplified) cases showed ATRX-loss. 43.5% were pTERT mutated (C228T was the commonest type) and were mutually exclusive with ATRX-loss. 14% of mIDH and 42% of EGFR amplified cases showed pTERT mutation, the latter was more commonly pMGMT unmethylated (63.6%). Conclusions 43.5% of the GBMs showed pTERT mutation (C228T was commonest; 72%). pTERT mutations were mutually exclusive with ATRX protein loss, more commonly associated with IDH wild type and EGFR amplified GBMs.

International audience; Addressee detection is an important challenge to tackle in order to improve dialogical interactions between humans and agents. This detection, essential for turn-taking models, is a hard task in multiparty conditions. Rule based as well as statistical approaches have been explored. Statistical approaches, particularly deep learning approaches, require a huge amount of data to train. However, smart feature selection can help improve addressee detection on small datasets, particularly if multimodal information is available. In this article, we propose a statistical approach based on smart feature selection that exploits contextual and multimodal information for addressee detection. The results show that our model outperforms an existing baseline.

In the last decades, the industry has profoundly integrated the use of digital resources in their production process. However, these assets are rarely re-used for the training of the users, operators and technicians that have to interact with these objects. Furthermore, although training and learning environments are classical applications of virtual reality, the design of these environments is generally ad hoc, i.e. dedicated to specific operations on specific objects, hence requiring the intervention of programmers whenever a modification of the pedagogical scenario is required. In this article, we propose a methodology to design adaptable virtual environments, by separating the role of the different protagonists that play a part in the creation of learning environments. In particular, its goal is to allow the teachers to implement different scenarios according to the level of the trainees and to the pedagogical objectives without the intervention of computer scientists. An example of adaptable wind turbine environment is shown, with three different learning situations: simulator, safety training and preventive maintenance training.

Virtual agents are a real asset in collaborative virtual environment for training CVET as they can replace missing team members. Collaboration between such agents and users, however, is generally limited. We present here a whole integrated model of CVET focusing on the abstraction of the real or virtual nature of the actor to define a homogenous collaboration model. First, we define a new collaborative model of interaction. This model notably allows to abstract the real or virtual nature of a teammate. Moreover, we propose a new role exchange approach so that actors can swap their roles during training. The model also permits the use of physically based objects and characters animation to increase the realism of the world. Second, we design a new communicative agent model, which aims at improving collaboration with other actors using dialog to coordinate their actions and to share their knowledge. Finally, we evaluated the proposed model to estimate the resulting benefits for the users and we show that this is integrated in existing CVET applications. Copyright © 2014 John Wiley & Sons, Ltd.

International audience; Coordination is an essential ingredient for human-agent teamwork. It requires team members to share knowledge to establish common grounding and mutual awareness among them. This paper proposes a be-havioral architecture C 2 BDI that enhances the knowledge sharing using natural language communication between team members. Collaborative conversation protocols and resource allocation mechanism have been defined that provide proactive behavior to agents for coordination. This architecture has been applied to a real scenario in a collaborative virtual environment for learning. The solution enables users to coordinate with other team members.

In Collaborative Virtual Environments (VEs) for Training, users have to learn how to perform a collaborative task and also how to coordinate with teammates’ activities. Efficient coordination requires teammates to exchange information about their beliefs, goals and plans. The collaborative-conversational BDI agent (C2BDI) endows virtual agents with first, deliberative capabilities about the interdependency of their activities, and second, with task-oriented conversational capabilities that support multiparty spoken dialogues helping them to coordinate their activities with teammates [2]. This proposed solution has been used in two virtual reality applications: a real training scenario [1] and an application dedicated to scientific experiments [2]. The main motivations of this last was to control the characteristics of the collective activity and to be more extensible.

Many Virtual Reality (VR) applications, such as Virtual Learning Environments or Interactive Virtual Tours, are based on a rich semantic description of the environment and tasks that users have to perform. These applications are built upon Virtual Environments (VEs) in which artificial agents act autonomously while interacting in realtime with users. Semantic modelling of a VR environment makes it possible the knowledge-driven access from the description of VEs that simplifies the development of VR applications. It eases the development of these types of applications. Semantic modelling should provide a consistent representation of the following aspects: 1) The simulated world, its structure and the behavior of its entities, 2) Interactions and tasks, that users and agents can perform in the environment, 3) Knowledge items, that autonomous agents can use for decision-making or for communication with users. This paper presents MASCARET, a model-based approach, for the design of semantic VR environments. This approach is based on the Unified Modeling Language (UML). In this approach, UML is used to provide a knowledge-driven access to the semantic contents of the VE and not for code generation, as in classical software development process. Interests of a UML-based approach are that its metamodel covers different views of the semantic modelling: ontology, structure, behaviors, interactions, activities. It is also an extensible language that can be specialized to provide formal operational semantics. We also present how MASCARET can be used to develop content-rich interactive applications that can be deployed over various VR platforms. Finally, we discuss the benefits of such a metamodel-based approach and show how the multi-layer semantic model can be used in different VR applications, in which adaptive behaviors of artificial agents acting within complex environments have to be simulated.

International audience; Recent cultural heritage applications have been based on rich-content virtual environment (VE), in which virtual humans can communicate with visitors and other agents using natural language (NL). The conceptualisation of these dialogues are dependent on the contents of the application. Hence, we propose to use the semantic modelling of the VE and the agents' activities for the conceptualisation of the dialogue. Meta-level semantic information are used as arguments in NLU/NLG rules. The advantage of this approach is that the dialogue rules are independent from the contents of the application and have clear semantics. We applied these principles to develop Brest'Coz, an interactive virtual tour for the learning of shipbuilding techniques used in France in early 18 th century.