Mast cell carboxypeptidase A, a secretory granule component

Mast cells are highly granulated cells of the immune system that upon stimulation release a number of inflammatory mediators including heparin and/or chondroitin sulphate (CS) proteoglycan (PG) and various heparin-binding proteases such as tryptase, chymase and carboxypeptidase A (CPA). Mast cell CPA is a zinc-metalloexopeptidase, cleaving substrates with carboxyl-terminal aliphatic or aromatic amino acids. In this thesis, the storage and activation of CPA was investigated, using bone marrow derived mast cells (BMMCs) from mice lacking heparin, either due to loss of the gene coding for the heparin biosynthesis enzyme, NDST-2, or the gene coding for the PG core protein serglycin (SG). We found that BMMCs from NDST-2-/- mice that thus lack heparin, but produce CS, lack the chymase, mMCP-5 and mature CPA. Interestingly, the pro-form, but not the active form of CPA could be detected in the heparin-deficient cells, indicating a role for heparin in the processing of CPA. Furthermore, we have shown that the cysteine proteases, cathepsins C and S, are not involved in processing CPA, but rather that lack of cathepsin C or S cause increased levels of CPA as well as mMCP-5. In addition, neither cathepsins B nor L influence CPA processing at all, but instead, an aspartic protease, cathepsin E, plays a role in processing pro-CPA. Further, these studies led to the novel finding that cathepsin E is located inside the mast cell granules, where it is stored in complex with heparin. The activation of mast cells, which ultimately leads to degranulation, has been studied in detail; however, the process where mast cell granules are formed has not gained as much attention. We addressed this issue by the use of BMMCs from mice lacking SG. Here, we present evidence that secretory granules are formed independently of SG PG but that SG mediates selective condensation of certain granule constituents, while others are independent of SG. Mast cell proteases are correctly sorted into the granules but are subsequently degraded, exocytosed or remain unprocessed when SG is absent. These results indicate a model in which selected granule constituents are sorted into granules by SG-mediated retention

Additive domain decomposition operator splittings -- convergence analyses in a dissipative framework

We analyze temporal approximation schemes based on overlapping domain decompositions. As such schemes enable computations on parallel and distributed hardware, they are commonly used when integrating large-scale parabolic systems. Our analysis is conducted by first casting the domain decomposition procedure into a variational framework based on weighted Sobolev spaces. The time integration of a parabolic system can then be interpreted as an operator splitting scheme applied to an abstract evolution equation governed by a maximal dissipative vector field. By utilizing this abstract setting, we derive an optimal temporal error analysis for the two most common choices of domain decomposition based integrators. Namely, alternating direction implicit schemes and additive splitting schemes of first and second order. For the standard first-order additive splitting scheme we also extend the error analysis to semilinear evolution equations, which may only have mild solutions.Comment: Please refer to the published article for the final version which also contains numerical experiments. Version 3 and 4: Only comments added. Version 2, page 2: Clarified statement on stability issues for ADI schemes with more than two operator

Efficiency of Lift Production in Flapping and Gliding Flight of Swifts

Many flying animals use both flapping and gliding flight as part of their routine behaviour. These two kinematic patterns impose conflicting requirements on wing design for aerodynamic efficiency and, in the absence of extreme morphing, wings cannot be optimised for both flight modes. In gliding flight, the wing experiences uniform incident flow and the optimal shape is a high aspect ratio wing with an elliptical planform. In flapping flight, on the other hand, the wing tip travels faster than the root, creating a spanwise velocity gradient. To compensate, the optimal wing shape should taper towards the tip (reducing the local chord) and/or twist from root to tip (reducing local angle of attack). We hypothesised that, if a bird is limited in its ability to morph its wings and adapt its wing shape to suit both flight modes, then a preference towards flapping flight optimization will be expected since this is the most energetically demanding flight mode. We tested this by studying a well-known flap-gliding species, the common swift, by measuring the wakes generated by two birds, one in gliding and one in flapping flight in a wind tunnel. We calculated span efficiency, the efficiency of lift production, and found that the flapping swift had consistently higher span efficiency than the gliding swift. This supports our hypothesis and suggests that even though swifts have been shown previously to increase their lift-to-drag ratio substantially when gliding, the wing morphology is tuned to be more aerodynamically efficient in generating lift during flapping. Since body drag can be assumed to be similar for both flapping and gliding, it follows that the higher total drag in flapping flight compared with gliding flight is primarily a consequence of an increase in wing profile drag due to the flapping motion, exceeding the reduction in induced drag

A full space-time convergence order analysis of operator splittings for linear dissipative evolution equations

The Douglas--Rachford and Peaceman--Rachford splitting methods are common choices for temporal discretizations of evolution equations. In this paper we combine these methods with spatial discretizations fulfilling some easily verifiable criteria. In the setting of linear dissipative evolution equations we prove optimal convergence orders, simultaneously in time and space. We apply our abstract results to dimension splitting of a 2D diffusion problem, where a finite element method is used for spatial discretization. To conclude, the convergence results are illustrated with numerical experiments

Efficient simulations of tubulin-driven axonal growth

This work concerns efficient and reliable numerical simulations of the dynamic behaviour of a moving-boundary model for tubulin-driven axonal growth. The model is nonlinear and consists of a coupled set of a partial differential equation (PDE) and two ordinary differential equations. The PDE is defined on a computational domain with a moving boundary, which is part of the solution. Numerical simulations based on standard explicit time-stepping methods are too time consuming due to the small time steps required for numerical stability. On the other hand standard implicit schemes are too complex due to the nonlinear equations that needs to be solved in each step. Instead, we propose to use the Peaceman--Rachford splitting scheme combined with temporal and spatial scalings of the model. Simulations based on this scheme have shown to be efficient, accurate, and reliable which makes it possible to evaluate the model, e.g.\ its dependency on biological and physical model parameters. These evaluations show among other things that the initial axon growth is very fast, that the active transport is the dominant reason over diffusion for the growth velocity, and that the polymerization rate in the growth cone does not affect the final axon length.Comment: Authors' accepted version, (post refereeing). The final publication (in Journal of Computational Neuroscience) is available at Springer via http://dx.doi.org/10.1007/s10827-016-0604-

Spatial and Physical Splittings of Semilinear Parabolic Problems

Splitting methods are widely used temporal approximation schemes for parabolic partial differential equations (PDEs). These schemes may be very efficient when a problem can be naturally decomposed into multiple parts. In this thesis, splitting methods are analysed when applied to spatial splittings (partitions of the computational domain) and physical splittings (separations of physical processes) of semilinear parabolic problems. The thesis is organized into three major themes: optimal convergence order analysis, spatial splittings and a physical splitting application.In view of the first theme, temporal semi-discretizations based on splitting methods are considered. An analysis is performed which yields convergence without order under weak regularity assumptions on the solution, and convergence orders ranging up to classical for progressively more regular solutions. The analysis is performed in the framework of maximal dissipative operators, which includes a large number of parabolic problems. The temporal results are also combined with convergence studies of spatial discretizations to prove simultaneous space–time convergence orders for full discretizations.For the second theme, two spatial splitting formulations are considered. For dimension splittings each part of the formulation represents the evolution in one spatial dimension only. Thereby, multidimensional problems can be reduced to families of one-dimensional problems. For domain decomposition splittings each part represents a problem on only a smaller subdomain of the full domain of the PDE. The results of the first theme are applied to prove optimal convergence orders for splitting schemes used in conjunction with these two splitting formulations. The last theme concerns the evaluation of a physical splitting procedure in an interdisciplinary application. A model for axonal growth out of nerve cells is considered. This model features several challenges to be addressed by a successful numerical method. It consists of a linear PDE coupled to nonlinear ordinary differential equations via a moving boundary, which is part of the solution. The biological model parameters imply a wide range of scales, both in time and space. Based on a physical splitting, a tailored scheme for this model is constructed. Its robustness and efficiency are then verified by numerical experiments

Event-Based Control and Estimation with Stochastic Disturbances

This thesis deals with event-based control and estimation strategies, motivated by certain bottlenecks in the control loop. Two kinds of implementation constraints are considered: closing one or several control loops over a data network, and sensors that report measurements only as intervals (e.g. with quantization). The proposed strategies depend critically on _events_, when a data packet is sent or when a change in the measurement signal is received. The value of events is that they communicate new information about stochastic process disturbances. A data network in the control loop imposes constraints on the event timing, modelled as a minimum time between packets. A thresholdbased control strategy is suggested and shown to be optimal for firstorder systems with impulse control. Different ways to find the optimal threshold are investigated for single and multiple control loops sharing one network. The major gain compared to linear time invariant (LTI) control is with a single loop a greatly reduced communication rate, which with multiple loops can be traded for a similarly reduced regulation error. With the bottleneck that sensors report only intervals, both the theoretical and practical control problems become more complex. We focus on the estimation problem, where the optimal solution is known but untractable. Two simplifications are explored to find a realistic state estimator: reformulation to a mixed stochastic/worst case scenario and joint maximum a posteriori estimation. The latter approach is simplified and evaluated experimentally on a moving cart with quantized position measurements controlled by a low-end microcontroller. The examples considered demonstrate that event-based control considerably outperforms LTI control, when the bottleneck addressed is a genuine performance constraint on the latter