A Tailorable Collaborative Learning System That Combines OGSA Grid Services and IMS-LD Scripting

This paper presents Gridcole, a new collaborative learning system that can be easily tailored by educators in order to support their own CSCL scenarios, using computing services provided by third parties in the form of OGSA grid services. Educators employ scripts in order to describe the sequence of learning activities and required tools, with standard IMS-LD notation. Thus, through the integration of coarse-grained tools, that may even offer supercomputing capabilities or use specific hardware resources, educators do not depend on software developers to easily configure a suitable environment in order to support a broad range of collaborative scenarios. An example of a learning scenario for a Computer Architecture course is described to illustrate the capabilities of Gridcole

IoT predictive application for DC motor control using radio frequency links

International audienceThe Internet of things (IoT) becomes a new solution for the future industry. It aims to provide an intelligent environment to control systems in real time. We propose an embedded predictive application in the Wireless Networked Control Systems (W-NCS) to control a DC motor via RF links, under the presence of packet losses and limited size constraint of transmitted packets in the communication channel. To design an IoT predictive application in W-NCS, a Model Predictive Control (MPC) method is proposed. This strategy is developed and implemented in the embedded device by using the RIOT OS which granted maintenance costs of IoT products and offers a real-time support in the control of the systems. The proposed approach is tested in a wireless environment with an intention to be applied to tackle the problem of communication. The practical experiment results obtained demonstrate the effectiveness of the Networked Predictive Control (NPC) approach based on the W-NCS structure for this kind of problems

Actin-Based Motility of Intracellular Microbial Pathogens

A diverse group of intracellular microorganisms, including Listeria monocytogenes, Shigella spp., Rickettsia spp., and vaccinia virus, utilize actin-based motility to move within and spread between mammalian host cells. These organisms have in common a pathogenic life cycle that involves a stage within the cytoplasm of mammalian host cells. Within the cytoplasm of host cells, these organisms activate components of the cellular actin assembly machinery to induce the formation of actin tails on the microbial surface. The assembly of these actin tails provides force that propels the organisms through the cell cytoplasm to the cell periphery or into adjacent cells. Each of these organisms utilizes preexisting mammalian pathways of actin rearrangement to induce its own actin-based motility. Particularly remarkable is that while all of these microbes use the same or overlapping pathways, each intercepts the pathway at a different step. In addition, the microbial molecules involved are each distinctly different from the others. Taken together, these observations suggest that each of these microbes separately and convergently evolved a mechanism to utilize the cellular actin assembly machinery. The current understanding of the molecular mechanisms of microbial actin-based motility is the subject of this review

A Novel 8-Predictors Signature to Predict Complicated Disease Course in Pediatric-onset Crohn’s Disease: A Population-based Study

International audienceBackground The identification of patients at high risk of a disabling disease course would be invaluable in guiding initial therapy in Crohn’s disease (CD). Our objective was to evaluate a combination of clinical, serological, and genetic factors to predict complicated disease course in pediatric-onset CD. Methods Data for pediatric-onset CD patients, diagnosed before 17 years of age between 1988 and 2004 and followed more than 5 years, were extracted from the population-based EPIMAD registry. The main outcome was defined by the occurrence of complicated behavior (stricturing or penetrating) and/or intestinal resection within the 5 years following diagnosis. Lasso logistic regression models were used to build a predictive model based on clinical data at diagnosis, serological data (ASCA, pANCA, anti-OmpC, anti-Cbir1, anti-Fla2, anti-Flax), and 369 candidate single nucleotide polymorphisms. Results In total, 156 children with an inflammatory (B1) disease at diagnosis were included. Among them, 35% (n = 54) progressed to a complicated behavior or an intestinal resection within the 5 years following diagnosis. The best predictive model (PREDICT-EPIMAD) included the location at diagnosis, pANCA, and 6 single nucleotide polymorphisms. This model showed good discrimination and good calibration, with an area under the curve of 0.80 after correction for optimism bias (sensitivity, 79%, specificity, 74%, positive predictive value, 61%, negative predictive value, 87%). Decision curve analysis confirmed the clinical utility of the model. Conclusions A combination of clinical, serotypic, and genotypic variables can predict disease progression in this population-based pediatric-onset CD cohort. Independent validation is needed before it can be used in clinical practice