Inhibition of mTOR-kinase destabilizes MYCN and is a potential therapy for MYCN-dependent tumors.

MYC oncoproteins deliver a potent oncogenic stimulus in several human cancers, making them major targets for drug development, but efforts to deliver clinically practical therapeutics have not yet been realized. In childhood cancer, aberrant expression of MYC and MYCN genes delineates a group of aggressive tumours responsible for a major proportion of pediatric cancer deaths. We designed a chemical-genetic screen that identifies compounds capable of enhancing proteasomal elimination of MYCN oncoprotein. We isolated several classes of compound that selectively kill MYCN expressing cells and we focus on inhibitors of PI3K/mTOR pathway in this study. We show that PI3K/mTOR inhibitors selectively killed MYCN-expressing neuroblastoma tumor cells, and induced significant apoptosis of transgenic MYCN-driven neuroblastoma tumors concomitant with elimination of MYCN protein in vivo. Mechanistically, the ability of these compounds to degrade MYCN requires complete blockade of mTOR but not PI3 kinase activity and we highlight NVP-BEZ235 as a PI3K/mTOR inhibitor with an ideal activity profile. These data establish that MYCN expression is a marker indicative of likely clinical sensitivity to mTOR inhibition, and provide a rationale for the selection of clinical candidate MYCN-destabilizers likely to be useful for the treatment of MYCN-driven cancers

A decentralized fault detection and isolation scheme for spacecraft: bridging the gap between model-based fault detection and isolation research and practice

This paper introduces a decentralized fault diagnosis and isolation (FDI) architecture for spacecraft and applies it to the attitude determination and control system (ADCS) of a satellite. A system is decomposed into functional subsystems. The architecture is composed of local diagnosers for subsystems which work with local models. Fault ambiguities due to interactions between subsystems are resolved at a higher level by a supervisor, which combines the partial view of the local diagnosers and performs isolation on request. The architecture is hierarchically scalable. The structure of the ADCS is modeled as constraints and variables and used to demonstrate the decentralized architecture

A l'Heure des Statecharts et de

Cet article présente un projet développé au Département Génie Electrique et Informatique de l'Institut National des Sciences Appliquées de Toulouse (INSA) dont le but est la commande d'une montre digitale. La modélisation de la commande s'appuie sur les Statecharts et sa mise en œuvre repose sur l'utilisation d'un PC sous noyau temps-réel. Ce projet permet ainsi d'illustrer à la fois des enseignements de base en systèmes à événements discrets et des aspects plus avancés d'informatique industrielle

JePeIA : de la création à la mise en oeuvre d’un escape game à visée pédagogique sur l’Intelligence Artificielle

Depuis 2019, une équipe d’enseignants collabore avec Instant Science et l’Institut ANITI, soutenue par la région Occitanie, pour développer un escape game pédagogique sur l’intelligence artificielle. Le dispositif JEPEIA (JEu PEdagogique en Intelligence Artificielle) est actuellement déployé dans les lycées de la région et lors des manifestations des établissements partenaires. Cet article décrit le développement du dispositif et son utilisation. JEPEIA vise à former largement aux enjeux de l’intelligence artificielle, transmettre une vision réaliste et non fantasmée des possibilités et des limites de ces technologies. Il vise également à sensibiliser et susciter une réflexion sur les enjeux sociétaux associés au développement de l’IA. En comprenant mieux ce domaine, les élèves, étudiants, personnels et citoyens seront mieux préparés à anticiper les évolutions futures et à s’approprier les discussions émergentes sur ces sujets d’importance majeure

Decentralized diagnosis in a spacecraft attitude determination and control system

International audienceIn model-based diagnosis (MBD), structural models can provide useful information for fault diagnosis and fault-tolerant control design. In particular, they are known for supporting the design of analytical redundancy relations (ARRs) which are widely used to generate residuals for diagnosis. On the other hand, systems are increasingly complex whereby it is necessary to develop decentralized architectures to perform the diagnosis task. Decentralized diagnosis is of interest for on-board systems as a way to reduce computational costs or for large geographically distributed systems that require to minimizing data transfer. Decentralized solutions allow proper separation of industrial knowledge, provided that inputs and outputs are clearly defined. This paper builds on the results of [1] and proposes an optimized approach for decentralized fault-focused residual generation. It also introduce the concept of Fault-Driven Minimal Structurally-Overdetermined set (FMSO) ensuring minimal redundancy. The method decreases communication cost involved in decentralization with respect to the algorithm proposed in [1] while still maintaining the same isolation properties as the centralized approach as well as the isolation on request capability. 1. Introduction With increasing complexity of industrial processes, the requirement for reliability, availability and security is growing significantly. Fault detection and isolation (FDI) are becoming a major issue in industry. The structural approach constitutes a general framework to provide information when the system becomes complex. The main aim of the structural approach application is to identify the subsets of equations which include redundancy. The system structure analysis, originally developed for the decomposition of large systems of equations for their hierarchical resolution, was adopted by the Fault Detection and Isolation (FDI) community [2, 3]. Structural concepts are used for analysis of system monitor ability using the concept of complete matching on a graph. Decentralized diagnosis has received considerable attention to deal with distributed systems or with systems that may be too large to be diagnosed by one centralized site. In the same way, the decentralized solution allows proper separation of industrial knowledge, provided that inputs and outputs are clearly defined

Process decomposition and test selection for distributed fault diagnosis

International audienceDecomposing is one way to gain efficiency when dealing with large scale systems. In addition, the breakdown into subsystems may be mandatory to reflect some geographic or confidentiality constraints. In this context, the selection of diagnostic tests must comply with decomposition and it is desired to minimize the number of subsystem intercon-nections while still guaranteeing maximal diagnosability. On the other hand, it should be noticed that there is often some flexibility in the way to decompose a system. By placing itself in the context of structural analysis, this paper provides a solution to the double overlinked problem of choosing the decomposition of the system by leveraging existing flexibility and of selecting the set of diagnostic tests so as to minimize subsystem interconnections while maximizing diagnosability

Targeting MYCN in Neuroblastoma by BET Bromodomain Inhibition

Bromodomain inhibition comprises a promising therapeutic strategy in cancer, particularly for hematologic malignancies. To date, however, genomic biomarkers to direct clinical translation have been lacking. We conducted a cell-based screen of genetically-defined cancer cell lines using a prototypical inhibitor of BET bromodomains. Integration of genetic features with chemosensitivity data revealed a robust correlation between MYCN amplification and sensitivity to bromodomain inhibition. We characterized the mechanistic and translational significance of this finding in neuroblastoma, a childhood cancer with frequent amplification of MYCN. Genome-wide expression analysis demonstrated downregulation of the MYCN transcriptional program accompanied by suppression of MYCN transcription. Functionally, bromodomain-mediated inhibition of MYCN impaired growth and induced apoptosis in neuroblastoma. BRD4 knock-down phenocopied these effects, establishing BET bromodomains as transcriptional regulators of MYCN. BET inhibition conferred a significant survival advantage in three in vivo neuroblastoma models, providing a compelling rationale for developing BET bromodomain inhibitors in patients with neuroblastoma

Targeting MYCN in Neuroblastoma by BET Bromodomain Inhibition

Bromodomain inhibition comprises a promising therapeutic strategy in cancer, particularly for hematologic malignancies. To date, however, genomic biomarkers to direct clinical translation have been lacking. We conducted a cell-based screen of genetically defined cancer cell lines using a prototypical inhibitor of BET bromodomains. Integration of genetic features with chemosensitivity data revealed a robust correlation between MYCN amplification and sensitivity to bromodomain inhibition. We characterized the mechanistic and translational significance of this finding in neuroblastoma, a childhood cancer with frequent amplification of MYCN. Genome-wide expression analysis showed downregulation of the MYCN transcriptional program accompanied by suppression of MYCN transcription. Functionally, bromodomain-mediated inhibition of MYCN impaired growth and induced apoptosis in neuroblastoma. BRD4 knockdown phenocopied these effects, establishing BET bromodomains as transcriptional regulators of MYCN. BET inhibition conferred a significant survival advantage in 3 in vivo neuroblastoma models, providing a compelling rationale for developing BET bromodomain inhibitors in patients with neuroblastoma