Observing Proteins as Single Molecules Encapsulated in Surface‐Tethered Polymeric Nanocontainers

Protein unfolding inside immobilized polymerosomes : One of the most interesting properties of polymeric vesicles is their remarkable stability against extreme temperatures and osmotic stress, and their longevity even under harsh environmental conditions. We have demonstrated, in an application on protein folding, that surface‐tethered polymerosomes are suitable for performing time‐resolved single molecule studies with encapsulated proteins, as illustrated here.

     

Go Brown: Inner-Disciplinary Conjectures

The relationship of society to its excrement is telling at both a cultural and social level. The notion of green architecture with its emphasis on the naturally pure has so far eschewed waste as a primary generative material. Alexandros Tsamis here invokes a more inclusive notion of the environment in which by-product becomes intrinsic to any project. Copyright © 2010 John Wiley & Sons, Ltd.     

Simulation-based robust design of mass dampers for response mitigation of tension leg platforms

A robust stochastic design framework is discussed for design of mass dampers. The focus is on applications for the mitigation of the coupled heave and pitch response of Tension Leg Platforms under stochastic sea excitation. The framework presented fully addresses the complex relationship between the coupled dynamics of the platform, the stochastic excitation and the vibration of the dampers. Model parameters that have some level of uncertainty are probabilistically described. In this probabilistic setting, the system reliability is adopted as the design objective. Stochastic simulation is considered for evaluation of the system model response and the overall reliability performance. This way, all nonlinear characteristics of the structural response and environmental excitation are explicitly incorporated into their respective models. An efficient algorithm is discussed for performing the challenging stochastic design optimization. The ideas are illustrated in an application involving a tension leg platform with closely spaced frequencies for the heave and pitch degrees of freedom.     

Cyclooxygenase Inhibition Alters Proliferative,Migratory, and Invasive Properties of Human Glioblastoma Cells In Vitro

Prostaglandin E₂ (PGE₂) is known to increase glioblastoma (GBM) cell proliferation and migration while cyclooxygenase (COX) inhibition decreases proliferation and migration. The present study investigated the effects of COX inhibitors and PGE₂ receptor antagonists on GBM cell biology. Cells were grown with inhibitors and dose response, viable cell counting, flow cytometry, cell migration, gene expression, Western blotting, and gelatin zymography studies were performed. The stimulatory effects of PGE₂ and the inhibitory effects of ibuprofen (IBP) were confirmed in GBM cells. The EP2 and EP4 receptors were identified as important mediators of the actions of PGE₂ in GBM cells. The concomitant inhibition of EP2 and EP4 caused a significant decrease in cell migration which was not reverted by exogenous PGE₂. In T98G cells exogenous PGE₂ increased latent MMP2 gelatinolytic activity. The inhibition of COX1 or COX2 caused significant alterations in MMP2 expression and gelatinolytic activity in GBM cells. These findings provide further evidence for the importance of PGE₂ signalling through the EP2 and the EP4 receptor in the control of GBM cell biology. They also support the hypothesis that a relationship exists between COX1 and MMP2 in GBM cells which merits further investigation as a novel therapeutic target for drug development.     

Biomimetic Omnidirectional Antireflective Glass via Direct Ultrafast Laser Nanostructuring

Here, a single‐step, biomimetic approach for the realization of omnidirectional transparent antireflective glass is reported. In particular, it is shown that circularly polarized ultrashort laser pulses produce self‐organized nanopillar structures on fused silica (SiO₂). The laser‐induced nanostructures are selectively textured on the glass surface in order to mimic the spatial randomness, pillar‐like morphology, as well as the remarkable antireflection properties found on the wings of the glasswing butterfly, Greta oto, and various Cicada species. The artificial structures exhibit impressive antireflective properties, both in the visible and infrared frequency ranges, which are remarkably stable over time. Accordingly, the laser‐processed glass surfaces show reflectivity smaller than 1% for various angles of incidence in the visible spectrum for s–p linearly polarized configurations. However, in the near‐infrared spectrum, the laser‐textured glass shows higher transmittance compared to the pristine. It is envisaged that the current results will revolutionize the technology of antireflective transparent surfaces and impact numerous applications from glass displays to optoelectronic devices.     

A superconvergent hybridisable discontinuous Galerkin method for linear elasticity

The first superconvergent hybridisable discontinuous Galerkin method for linear elastic problems capable of using the same degree of approximation for both the primal and mixed variables is presented. The key feature of the method is the strong imposition of the symmetry of the stress tensor by means of the well known and extensively used Voigt notation, circumventing the use of complex mathematical concepts to enforce the symmetry of the stress tensor either weakly or strongly. A novel procedure to construct element by element a superconvergent postprocessed displacement is proposed. Contrary to other hybridisable discontinuous Galerkin formulations, the methodology proposed here is able to produce a superconvergent displacement field for low‐order approximations. The resulting method is robust and locking‐free in the nearly incompressible limit. An extensive set of numerical examples is utilised to provide evidence of the optimality of the method and its superconvergent properties in two and three dimensions and for different element types.     

Fermions in Two Dimensions: Scattering and Many-Body Properties

Ultracold atomic Fermi gases in two dimensions (2D) are an increasingly popular topic of research. The interaction strength between spin-up and spin-down particles in two-component Fermi gases can be tuned in experiments, allowing for a strongly interacting regime where the gas properties are yet to be fully understood. We have probed this regime for 2D Fermi gases by performing T = 0 ab initio diffusion Monte Carlo calculations. The many-body dynamics are largely dependent on the two-body interactions; therefore, we start with an in-depth look at scattering theory in 2D. We show the partial-wave expansion and its relation to the scattering length and effective range. Then, we discuss our numerical methods for determining these scattering parameters. We close out this discussion by illustrating the details of bound states in 2D. Transitioning to the many-body system, we use variationally optimized wave functions to calculate ground-state properties of the gas over a range of interaction strengths. We show results for the energy per particle and parametrize an equation of state. We then proceed to determine the chemical potential for the strongly interacting gas.     

Probabilistic movement primitives under unknown system dynamics

Physical interaction requires robots to accurately follow kinematic trajectories while modulating the interaction forces to accomplish tasks and to be safe to the environment. However, current approaches rely on accurate physical models or iterative learning approaches. We present a versatile approach for physical interaction tasks, based on Movement Primitives (MPs) that can learn physical interaction tasks solely by demonstrations, without explicitly modeling the robot or the environment. We base our approach on the Probabilistic Movement Primitives (ProMPs), which utilizes the variance of the demonstrations to provide better generalization of the encoded skill, combine skills, and derive a controller that follows exactly the encoded trajectory distribution. However, the ProMP controller requires the system dynamics to be known. We present a reformulation of the ProMPs that allows accurate reproduction of the skill without modeling the system dynamics and, further, we extent our approach to incorporate external sensors, as for example, force/torque sensors. Our approach learns physical interaction tasks solely from demonstrations and online adapts the movement to force–torque sensor input. We derive a variable-stiffness controller in closed form that reproduces the trajectory distribution and the interaction forces present in the demonstrations. We evaluate our approach in simulated and real-robot tasks.