Vaccinia protein C16 blocks innate immune sensing of DNA by binding the Ku complex

VACV gene C16L encodes a 37-kDa protein that is highly conserved in orthopoxviruses and functions as an immunomodulator. Intranasal infection of mice with a virus lacking C16L (vΔC16) induced less weight loss, fewer signs of illness and increased infiltration of leukocytes to the lungs compared with wild-type virus. To understand C16’s mechanism of action, tandem affinity purification and mass spectrometry were used to identify C16 binding partners. This revealed that Ku70, Ku80 and PHD2 interact with C16 in cells. Ku70 and Ku80 constitute the Ku heterodimer, a well characterised DNA repair complex. MEFs lacking Ku, or the other component of the DNA-dependent protein kinase (DNA-PK) complex, the catalytic subunit of DNA-PK (DNA-PKcs), were shown to be deficient in the upregulation of IRF-3-dependent genes such as Cxcl10, Il6 and Ifnb in response to transfection of DNA, but not poly (I:C). Furthermore, following infection of MEFs with VACV strain MVA the activation of Cxcl10 or Il6 transcription was dependent on DNA-PK. Therefore, DNA-PK is a DNA sensor capable of detecting poxvirus DNA and activating IRF-3-dependent innate immunity. C16 inhibited the binding of Ku to DNA, and therefore inhibited DNA-mediated induction of Cxcl10 and Il-6 in MEFs. The role of C16 in vivo was also examined: infection with vΔC16 led to increased production of Cxcl10 and Il-6 following intranasal infection of mice compared with wild-type virus. C16 is therefore an inhibitor of DNA-PK-mediated DNA sensing and innate immune activation. C16 was also shown to bind to PHD2, an enzyme involved in regulation of hypoxic signalling. VACV was found to activate the transcription of hypoxia-related genes, and C16 expression in cells was also capable of doing this. The role of hypoxic signalling in VACV infection remains poorly understood

Myocardial Architecture and Patient Variability in Clinical Patterns of Atrial Fibrillation

Atrial fibrillation (AF) increases the risk of stroke by a factor of four to five and is the most common abnormal heart rhythm. The progression of AF with age, from short self-terminating episodes to persistence, varies between individuals and is poorly understood. An inability to understand and predict variation in AF progression has resulted in less patient-specific therapy. Likewise, it has been a challenge to relate the microstructural features of heart muscle tissue (myocardial architecture) with the emergent temporal clinical patterns of AF. We use a simple model of activation wavefront propagation on an anisotropic structure, mimicking heart muscle tissue, to show how variation in AF behaviour arises naturally from microstructural differences between individuals. We show that the stochastic nature of progressive transversal uncoupling of muscle strands (e.g., due to fibrosis or gap junctional remodelling), as occurs with age, results in variability in AF episode onset time, frequency, duration, burden and progression between individuals. This is consistent with clinical observations. The uncoupling of muscle strands can cause critical architectural patterns in the myocardium. These critical patterns anchor micro-re-entrant wavefronts and thereby trigger AF. It is the number of local critical patterns of uncoupling as opposed to global uncoupling that determines AF progression. This insight may eventually lead to patient specific therapy when it becomes possible to observe the cellular structure of a patient's heart.Comment: 5 pages, 4 figures. For supplementary materials please contact Kishan A. Manani at [email protected]

A Data Driven Modeling Approach for Store Distributed Load and Trajectory Prediction

The task of achieving successful store separation from aircraft and spacecraft has historically been and continues to be, a critical issue for the aerospace industry. Whether it be from store-on-store wake interactions, store-parent body interactions or free stream turbulence, a failed case of store separation poses a serious risk to aircraft operators. Cases of failed store separation do not simply imply missing an intended target, but also bring the risk of collision with, and destruction of, the parent body vehicle. Given this risk, numerous well-tested procedures have been developed to help analyze store separation within the safe confines of wind tunnels. However, due to increased complexity in store separation configurations, such as rotorcraft and cavity-based separation, there is a growing desire to incorporate computational fluid dynamics (CFD) into the early stages of the store separation analysis. A viable method for achieving this objective is available through data-driven surrogate modeling of store distributed loads. This dissertation investigates the practicality of applying various data-driven modeling techniques to the field of store separation. These modeling methods will be applied to four demonstration scenarios: reduced order modeling of a moving store, design optimization, supersonic store separation, and rotorcraft store separation. For the first demonstration scenario, results are presented for three sub-tasks. In the first sub-task proper orthogonal decomposition (POD), dynamic mode decomposition (DMD), and convolutional neural networks (CNN) were compared for their capability to replicate distributed pressure loads of a pitching up prolate spheroid. Results indicated that POD was the most efficient approach for surrogate model generation. For the second sub-task, a POD-based surrogate model was derived from CFD simulations of an oscillating prolate spheroid subject to varying reduced frequency and amplitude of oscillation. The obtained surrogate model was shown to provide high-fidelity predictions for new combinations of reduced frequency and amplitude with a maximum percent error of integrated loads of less than 3\%. Therefore, it was demonstrated that the surrogate model was capable of predicting accurately at intermediate states. Further analysis showed a similar surrogate model could be generated to provide accurate store trajectory modeling under subsonic, transonic, and supersonic conditions. In the second demonstration scenario, a POD-based surrogate model is derived from a series of CFD simulations of isolated rotors in hover and forward flight. The derived surrogate models for hover and forward flight were shown to provide integrated load predictions within 1% of direct CFD simulation. Additionally, results indicated that computational expense could be reduced from 20 hours on 440 CPUs to less than a second on a single CPU. Given the reduction of cost and high fidelity of the surrogate model, the derived model was leveraged to optimize the twist and taper ratio of the rotor such that the efficiency of the rotor was maximized. For the third demonstration scenario, a POD and CNN surrogate model was derived for fixed-wing based supersonic store separation. Results demonstrated that both models were capable of providing high-fidelity predictions of the store\u27s distributed loads and subsequent trajectory. For the final demonstration scenario, a POD-based surrogate model was derived for the case of a store launching from a rotorcraft. The surrogate model was derived from three CFD simulations while varying ejection force. This surrogate model was then validated against CFD simulation of a new store ejection force. Results indicated that while the surrogate model struggled to provide detailed predictions of store distributed loads, mean load variations could be modeled well at a massively reduced computational cost. For each rotorcraft store separation CFD simulation, the computational cost required 10 days of simulation time across 880. While using the surrogate model, comparable predictions could be produced in under a minute on a single core. Overall findings from this study indicate that massive CFD generated data-sets can be efficiently leveraged to create meaningful surrogate models capable of being deployed to highly iterative design tasks relevant to store separation. Through further improvements, similar surrogate models can be combined with a control strategy to achieve trajectory optimization and control

DMD and POD Modal Analysis for Store Separation

Store separation from aircraft and spacecraft has historically been a critical and in some cases fatal issue for the aerospace industry. Given the severity of the issue much effort has been spent on the development of processes to identify failure flight conditions for store separation. The processes currently used for identifying potential failure conditions however are both resource intensive and iterative processes. A potential remedy to reducing resource use and improve turn around time in this process is the implementation of a mode based reduced order model (ROM) for modeling store separation. The objective of this study was to first identify the leading modes that can best be used to model a store separating from an aircraft. To obtain these modes, two algorithms were used; Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD). The computational fluid dynamic (CFD) solver Ansys Fluent was employed to obtain flow field data around a representative vehicle and store. Preliminary validation of the numerical results was initially preformed and the results showed good comparison of surface pressures and free-stream vorticity. The validated data-set was then used to identify which modal method, POD or DMD, better resolves the known dominate structures of the flow field. The results of this analysis showed the superiority of POD in identifying both free-stream and surface pressure structures. A final representative case of store separation was obtained at a flight speed of mach 0.8. POD was then used to obtain leading modes that were used to reconstruct a ROM of the flow field. This ROM was successful in predicting the store’s trajectory both inside and out of the training flight profile