Discovery of functionally selective C5aR2 ligands: novel modulators of C5a signalling.

The complement cascade is comprised of a highly sophisticated network of innate immune proteins that are activated in response to invading pathogens or tissue injury. The complement activation peptide, C5a, binds two seven transmembrane receptors, namely the C5a receptor 1 (C5aR1) and C5a receptor 2 (C5aR2, or C5L2). C5aR2 is a non-G-protein-signalling receptor whose biological role remains controversial. Some of this controversy arises owing to the lack of selective ligands for C5aR2. In this study, a library of 61 peptides based on the C-terminus of C5a was assayed for the ability to selectively modulate C5aR2 function. Two ligands (P32 and P59) were identified as functionally selective C5aR2 ligands, exhibiting selective recruitment of β-arrestin 2 via C5aR2, partial inhibition of C5a-induced ERK1/2 activation and lipopolysaccharide-stimulated interleukin-6 release from human monocyte-derived macrophages. Importantly, neither ligand could induce ERK1/2 activation or inhibit C5a-induced ERK1/2 activation via C5aR1 directly. Finally, P32 inhibited C5a-mediated neutrophil mobilisation in wild-type, but not C5aR2(-/-) mice. These functionally selective ligands for C5aR2 are novel tools that can selectively modulate C5a activity in vitro and in vivo, and thus will be valuable tools to interrogate C5aR2 function.Immunology and Cell Biology advance online publication, 17 May 2016; doi:10.1038/icb.2016.43

Solving Global Optimization Problems Using MANGO

Traditional approaches for solving global optimization problems generally rely on a single algorithm. The algorithm may be hybrid or applied in parallel. Contrary to traditional approaches, this paper proposes to form teams of algorithms to tackle global optimization problems. Each algorithm is embodied and ran by a software agent. Agents exist in a multiagent system and communicate over Our proposed MultiAgent ENvironment for Global Optimization (MANGO). Through Communication and cooperation, the agents complement each other in tasks that they cannot do on their own. This paper gives a formal description of MANGO and Outlines design principles for developing agents to execute Oil MANGO. Our case study shows the effectiveness of multiagent teams in solving global optimization problems

Feasibility and dominance rules in the electromagnetism-like algorithm for constrained global optimization

This paper presents the use of a constraint-handling technique, known as feasibility and dominance rules, in a electromagnetismlike (ELM) mechanism for solving constrained global optimization problems. Since the original ELM algorithm is specifically designed for solving bound constrained problems, only the inequality and equality constraints violation together with the objective function value are used to select points and to progress towards feasibility and optimality. Numerical experiments are presented, including a comparison with other methods recently reported in the literature

Parallelization of the discrete gradient method of non-smooth optimization and its applications

We investigate parallelization and performance of the discrete gradient method of nonsmooth optimization. This derivative free method is shown to be an effective optimization tool, able to skip many shallow local minima of nonconvex nondifferentiable objective functions. Although this is a sequential iterative method, we were able to parallelize critical steps of the algorithm, and this lead to a significant improvement in performance on multiprocessor computer clusters. We applied this method to a difficult polyatomic clusters problem in computational chemistry, and found this method to outperform other algorithms. <br /

Branch-and-lift algorithm for deterministic global optimization in nonlinear optimal control

This paper presents a branch-and-lift algorithm for solving optimal control problems with smooth nonlinear dynamics and potentially nonconvex objective and constraint functionals to guaranteed global optimality. This algorithm features a direct sequential method and builds upon a generic, spatial branch-and-bound algorithm. A new operation, called lifting, is introduced, which refines the control parameterization via a Gram-Schmidt orthogonalization process, while simultaneously eliminating control subregions that are either infeasible or that provably cannot contain any global optima. Conditions are given under which the image of the control parameterization error in the state space contracts exponentially as the parameterization order is increased, thereby making the lifting operation efficient. A computational technique based on ellipsoidal calculus is also developed that satisfies these conditions. The practical applicability of branch-and-lift is illustrated in a numerical example. © 2013 Springer Science+Business Media New York

Multilocal programming and applications

Preprint versionMultilocal programming aims to identify all local minimizers of unconstrained or constrained nonlinear optimization problems. The multilocal programming theory relies on global optimization strategies combined with simple ideas that are inspired in deflection or stretching techniques to avoid convergence to the already detected local minimizers. The most used methods to solve this type of problems are based on stochastic procedures and a population of solutions. In general, population-based methods are computationally expensive but rather reliable in identifying all local solutions. In this chapter, a review on recent techniques for multilocal programming is presented. Some real-world multilocal programming problems based on chemical engineering process design applications are described.Fundação para a Ciência e a Tecnologia (FCT

Constrained dogleg methods for nonlinear systems with simple bounds

We focus on the numerical solution of medium scale bound-constrained systems of nonlinear equations. In this context, we consider an affine-scaling trust region approach that allows a great flexibility in choosing the scaling matrix used to handle the bounds. The method is based on a dogleg procedure tailored for constrained problems and so, it is named Constrained Dogleg method. It generates only strictly feasible iterates. Global and locally fast convergence is ensured under standard assumptions. The method has been implemented in the Matlab solver CoDoSol that supports several diagonal scalings in both spherical and elliptical trust region frameworks. We give a brief account of CoDoSol and report on the computational experience performed on a number of representative test problem

Development and calibration of a currency trading strategy using global optimization

We have developed a new financial indicator—called the Interest Rate Differentials Adjusted for Volatility (IRDAV) measure—to assist investors in currency markets. On a monthly basis, we rank currency pairs according to this measure and then select a basket of pairs with the highest IRDAV values. Under positive market conditions, an IRDAV based investment strategy (buying a currency with high interest rate and simultaneously selling a currency with low interest rate, after adjusting for volatility of the currency pairs in question) can generate significant returns. However, when the markets turn for the worse and crisis situations evolve, investors exit such money-making strategies suddenly, and—as a result—significant losses can occur. In an effort to minimize these potential losses, we also propose an aggregated Risk Metric that estimates the total risk by looking at various financial indicators across different markets. These risk indicators are used to get timely signals of evolving crises and to flip the strategy from long to short in a timely fashion, to prevent losses and make further gains even during crisis periods. Since our proprietary model is implemented in Excel as a highly nonlinear “black box” computational procedure, we use suitable global optimization methodology and software—the Lipschitz Global Optimizer solver suite linked to Excel—to maximize the performance of the currency basket, based on our selection of key decision variables. After the introduction of the new currency trading model and its implementation, we present numerical results based on actual market data. Our results clearly show the advantages of using global optimization based parameter settings, compared to the typically used “expert estimates” of the key model parameters.post-prin