Mogwaï: a Framework to Handle Complex Queries on Large Models

International audienceWhile Model Driven Engineering is gaining more industrial interest, scalability issues when managing large models have become a major problem in current modeling frameworks. Scalable model persistence has been achieved by using NoSQL backends for model storage, but existing modeling framework APIs have not evolved accordingly, limiting NoSQL query performance benefits. In this paper we present the Mogwaï a scalable and efficient model query framework based on a direct translation of OCL queries to Gremlin, a query language supported by several NoSQL databases. Generated Gremlin expressions are computed inside the database itself, bypassing limitations of existing framework APIs and improving overall performance, as confirmed by our experimental results showing an improvement of execution time up to a factor of 20 and a reduction of the memory overhead up to a factor of 75 for large models

Improving memory efficiency for processing large-scale models

International audienceScalability is a main obstacle for applying Model-Driven Engineering to reverse engineering, or to any other activity manipulating large models. Existing solutions to persist and query large models are currently ine cient and strongly linked to memory availability. In this paper, we propose a memory unload strategy for Neo4EMF, a persistence layer built on top of the Eclipse Modeling Framework and based on a Neo4j database backend. Our solution allows us to partially unload a model during the execution of a query by using a periodical dirty saving mechanism and transparent reloading. Our experiments show that this approach enables to query large models in a restricted amount of memory with an acceptable performance

Mutation of the Protein Kinase C Site in Borna Disease Virus Phosphoprotein Abrogates Viral Interference with Neuronal Signaling and Restores Normal Synaptic Activity

Understanding the pathogenesis of infection by neurotropic viruses represents a major challenge and may improve our knowledge of many human neurological diseases for which viruses are thought to play a role. Borna disease virus (BDV) represents an attractive model system to analyze the molecular mechanisms whereby a virus can persist in the central nervous system (CNS) and lead to altered brain function, in the absence of overt cytolysis or inflammation. Recently, we showed that BDV selectively impairs neuronal plasticity through interfering with protein kinase C (PKC)–dependent signaling in neurons. Here, we tested the hypothesis that BDV phosphoprotein (P) may serve as a PKC decoy substrate when expressed in neurons, resulting in an interference with PKC-dependent signaling and impaired neuronal activity. By using a recombinant BDV with mutated PKC phosphorylation site on P, we demonstrate the central role of this protein in BDV pathogenesis. We first showed that the kinetics of dissemination of this recombinant virus was strongly delayed, suggesting that phosphorylation of P by PKC is required for optimal viral spread in neurons. Moreover, neurons infected with this mutant virus exhibited a normal pattern of phosphorylation of the PKC endogenous substrates MARCKS and SNAP-25. Finally, activity-dependent modulation of synaptic activity was restored, as assessed by measuring calcium dynamics in response to depolarization and the electrical properties of neuronal networks grown on microelectrode arrays. Therefore, preventing P phosphorylation by PKC abolishes viral interference with neuronal activity in response to stimulation. Our findings illustrate a novel example of viral interference with a differentiated neuronal function, mainly through competition with the PKC signaling pathway. In addition, we provide the first evidence that a viral protein can specifically interfere with stimulus-induced synaptic plasticity in neurons

Neurons are MHC Class I-Dependent Targets for CD8 T Cells upon Neurotropic Viral Infection

Following infection of the central nervous system (CNS), the immune system is faced with the challenge of eliminating the pathogen without causing significant damage to neurons, which have limited capacities of renewal. In particular, it was thought that neurons were protected from direct attack by cytotoxic T lymphocytes (CTL) because they do not express major histocompatibility class I (MHC I) molecules, at least at steady state. To date, most of our current knowledge on the specifics of neuron-CTL interaction is based on studies artificially inducing MHC I expression on neurons, loading them with exogenous peptide and applying CTL clones or lines often differentiated in culture. Thus, much remains to be uncovered regarding the modalities of the interaction between infected neurons and antiviral CD8 T cells in the course of a natural disease. Here, we used the model of neuroinflammation caused by neurotropic Borna disease virus (BDV), in which virus-specific CTL have been demonstrated as the main immune effectors triggering disease. We tested the pathogenic properties of brain-isolated CD8 T cells against pure neuronal cultures infected with BDV. We observed that BDV infection of cortical neurons triggered a significant up regulation of MHC I molecules, rendering them susceptible to recognition by antiviral CTL, freshly isolated from the brains of acutely infected rats. Using real-time imaging, we analyzed the spatio-temporal relationships between neurons and CTL. Brain-isolated CTL exhibited a reduced mobility and established stable contacts with BDV-infected neurons, in an antigen- and MHC-dependent manner. This interaction induced rapid morphological changes of the neurons, without immediate killing or impairment of electrical activity. Early signs of neuronal apoptosis were detected only hours after this initial contact. Thus, our results show that infected neurons can be recognized efficiently by brain-isolated antiviral CD8 T cells and uncover the unusual modalities of CTL-induced neuronal damage

Genome-wide Analyses Identify KIF5A as a Novel ALS Gene

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.Peer reviewe

Efficient Persistence and Query Techniques for Very Large Models

Abstract While Model Driven Engineering is gaining more industrial interest, scalability issues when managing large models have become a major problem in current modeling frameworks. In particular, there is a need to store, query, and transform very large models in an efficient way. Several persistence solutions based on relational and NoSQL databases have been proposed to tackle these issues. However, existing solutions often rely on a single data store, which suits for a specific modeling activity, but may not be optimized for other scenarios. Furthermore, existing solutions often rely on low-level model handling API, limiting NoSQL query performance benefits. In this article, we first introduce NEOEMF, a multi-database model persistence framework able to store very large models in an efficient way according to specific modeling activities. Then, we present the MOGWAÏ query framework, able to compute complex OCL queries over very large models in an efficient way with a small memory footprint. All the presented work is fully open source and available online

Efficient Persistence and Query Techniques for Very Large Models

International audienceWhile Model Driven Engineering is gaining more industrial interest , scalability issues when managing large models have become a major problem in current modeling frameworks. In particular, there is a need to store, query, and transform very large models in an efficient way. Several persistence solutions based on relational and NoSQL databases have been proposed to tackle these issues. However , existing solutions often rely on a single data store, which suits for a specific modeling activity, but may not be optimized for other scenarios. Furthermore, existing solutions often rely on low-level model handling API, limiting NoSQL query performance benefits. In this article, we first introduce NEOEMF, a multi-database model persistence framework able to store very large models in an efficient way according to specific modeling activities. Then, we present the MOGWA¨IMOGWA¨I query framework, able to compute complex OCL queries over very large models in an efficient way with a small memory footprint. All the presented work is fully open source and available online