Depletion of the ubiquitin-binding adaptor molecule SQSTM1/p62 from macrophages harboring cftr ΔF508 mutation improves the delivery of Burkholderia cenocepacia to the autophagic machinery

Cystic fibrosis is the most common inherited lethal disease in Caucasians. It is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), of which the cftr ?F508 mutation is the most common. ?F508 macrophages are intrinsically defective in autophagy because of the sequestration of essential autophagy molecules within unprocessed CFTR aggregates. Defective autophagy allows Burkholderia cenocepacia (B. cepacia) to survive and replicate in ?F508 macrophages. Infection by B. cepacia poses a great risk to cystic fibrosis patients because it causes accelerated lung inflammation and, in some cases, a lethal necrotizing pneumonia. Autophagy is a cell survival mechanism whereby an autophagosome engulfs non-functional organelles and delivers them to the lysosome for degradation. The ubiquitin binding adaptor protein SQSTM1/p62 is required for the delivery of several ubiquitinated cargos to the autophagosome. In WT macrophages, p62 depletion and overexpression lead to increased and decreased bacterial intracellular survival, respectively. In contrast, depletion of p62 in ?F508 macrophages results in decreased bacterial survival, whereas overexpression of p62 leads to increased B. cepacia intracellular growth. Interestingly, the depletion of p62 from ?F508 macrophages results in the release of the autophagy molecule beclin1 (BECN1) from the mutant CFTR aggregates and allows its redistribution and recruitment to the B. cepacia vacuole, mediating the acquisition of the autophagy marker LC3 and bacterial clearance via autophagy. These data demonstrate that p62 differentially dictates the fate of B. cepacia infection in WT and ?F508 macrophages.

Introducing Automated Management Through Iteratively Increased Automation and Indicators

Introducing automation into a managed environment includes significant initial overhead and abstraction, creating a disconnect between the administrator and the system. In order to facilitate the transition to automated management, this paper proposes an approach whereby automation increases gradually, gathering data from the task deployment process. This stored data is analysed to determine the task outcome status and can then be used for comparison against future deployments of the same task and alerting the administrator to deviations from the expected outcome. Using a machinelearning
approach, the automation tool can learn from the administrator's reaction to task failures and eventually react to faults autonomously.

Modelling lay-up automation and production rate interaction on the cost of large stiffened panel components

This paper presents an integrated design and costing method for large stiffened panels for the purpose of investigating the influence and interaction of lay-up technology and production rate on manufacturing cost. A series of wing cover panels (≈586kg, 19·9m2) have been sized with realistic requirements considering manual and automated lay-up routes. The integrated method has enabled the quantification of component unit cost sensitivity to changes in annual production rate and employed equipment maximum deposition rate. Moreover the results demonstrate the interconnected relationship between lay-up process and panel design, and unit cost. The optimum unit cost solution when using automated lay-up is a combination of the minimum deposition rate and minimum number of lay-up machines to meet the required production rate. However, the location of the optimum unit cost, at the boundaries between the number of lay-up machines required, can make unit cost very sensitive to small changes in component design, production rate, and equipment maximum deposition rate. - See more at: http://aerosociety.com/News/Publications/Aero-Journal/Online/1941/Modelling-layup-automation-and-production-rate-interaction-on-the-cost-of-large-stiffened-panel-components#sthash.0fLuu9iG.dpuf

Significance Driven Computation on Next-Generation Unreliable Platforms

In this paper, we propose a design paradigm for energy efficient and variation-aware operation of next-generation multicore heterogeneous platforms. The main idea behind the proposed approach lies on the observation that not all operations are equally important in shaping the output quality of various applications and of the overall system. Based on such an observation, we suggest that all levels of the software design stack, including the programming model, compiler, operating system (OS) and run-time system should identify the critical tasks and ensure correct operation of such tasks by assigning them to dynamically adjusted reliable cores/units. Specifically, based on error rates and operating conditions identified by a sense-and-adapt (SeA) unit, the OS selects and sets the right mode of operation of the overall system. The run-time system identifies the critical/less-critical tasks based on special directives and schedules them to the appropriate units that are dynamically adjusted for highly-accurate/approximate operation by tuning their voltage/frequency. Units that execute less significant operations can operate at voltages less than what is required for correct operation and consume less power, if required, since such tasks do not need to be always exact as opposed to the critical ones. Such scheme can lead to energy efficient and reliable operation, while reducing the design cost and overheads of conventional circuit/micro-architecture level techniques.