Manganese speciation in human cerebrospinal fluid using CZE coupled to inductively coupled plasma MS

The neurotoxic effects of manganese (Mn) at elevated concentrations are well known. This raises the question, which of the Mn species can cross neural barriers and appear in cerebrospinal fluid (CSF). CSF is the last matrix in a living human organism available for analysis before a compound reaches the brain cells and therefore it is assumed to reflect best the internal exposure of brain tissue to Mn species. A previously developed CE method was modified for separation of albumin, histidine, tyrosine, cystine, fumarate, malate, inorganic Mn, oxalacetate, alpha-keto-glutarate, nicotinamide-dinucleotide (NAD), citrate, adenosine, glutathione, and glutamine. These compounds are supposed in the literature to act as potential Mn carriers. In a first attempt, these compounds were analyzed by CZE-UV to check whether they are present in CSF. The CZE-UV method was simpler than the coupled CZE-inductively coupled plasma (ICP)-dynamic reaction cell (DRC)-MS method and it was therefore chosen to obtain a first overview information. In a second step, the coupled method (CZE-ICP-DRC-MS) was used to analyze, in detail, which of the compounds found in CSF by CZE-UV were actually bound to Mn. Finally, 13 Mn species were monitored in CSF samples, most of them being identified: Mn-histidine, Mn-fumarate, Mn-malate, inorganic Mn, Mn-oxalacetate, Mn-alpha-keto glutarate, Mn-carrying NAD, Mn-citrate and Mn-adenosine. By far the most abundant Mn species was Mn-citrate showing a concentration of 0.7 +/- 0.13 microg Mn/L. Interestingly, several other Mn species can be related to the citric acid cycle.     

A survey of recent developments in multiobjective optimization

Multiobjective Optimization (MO) has many applications in such fields as the Internet, finance, biomedicine, management science, game theory and engineering. However, solving MO problems is not an easy task. Searching for all Pareto optimal solutions is expensive and a time consuming process because there are usually exponentially large (or infinite) Pareto optimal solutions. Even for simple problems determining whether a point belongs to the Pareto set is -hard. In this paper, we discuss recent developments in MO. These include optimality conditions, applications, global optimization techniques, the new concept of epsilon Pareto optimal solution, and heuristics.     

Finding multiple roots of a box-constrained system of nonlinear equations with a biased random-key genetic algorithm

Several numerical methods for solving nonlinear systems of equations assume that derivative information is available. Furthermore, these approaches usually do not consider the problem of finding all solutions to a nonlinear system. Rather, most methods output a single solution. In this paper, we address the problem of finding all roots of a system of equations. Our method makes use of a biased random-key genetic algorithm (BRKGA). Given a nonlinear system, we construct a corresponding optimization problem, which we solve multiple times, making use of a BRKGA, with areas of repulsion around roots that have already been found. The heuristic makes no use of derivative information. We illustrate the approach on seven nonlinear equations systems with multiple roots from the literature.     

Robust generalized eigenvalue classifier with ellipsoidal uncertainty

Uncertainty is a concept associated with data acquisition and analysis, usually appearing in the form of noise or measure error, often due to some technological constraint. In supervised learning, uncertainty affects classification accuracy and yields low quality solutions. For this reason, it is essential to develop machine learning algorithms able to handle efficiently data with imprecision. In this paper we study this problem from a robust optimization perspective. We consider a supervised learning algorithm based on generalized eigenvalues and we provide a robust counterpart formulation and solution in case of ellipsoidal uncertainty sets. We demonstrate the performance of the proposed robust scheme on artificial and benchmark datasets from University of California Irvine (UCI) machine learning repository and we compare results against a robust implementation of Support Vector Machines.     

Uptake measurements of acetic acid on ice and nitric acid-doped thin ice films over upper troposphere/lower stratosphere temperatures

The adsorption of gaseous acetic acid (CH(3)C(O)OH) on thin ice films and on ice doped with nitric acid (1.96 and 7.69 wt %) was investigated over upper troposphere and lower stratosphere (UT/LS) temperatures (198-208 K), and at low gas concentrations. Experiments were performed in a Knudsen flow reactor coupled to a quadrupole mass spectrometer. The initial uptake coefficients, γ(0), on thin ice films or HNO(3)-doped ice films were measured at low surface coverage. In all cases, γ(0) showed an inverse temperature dependence, and for pure thin ice films, it was given by the expression γ(0)(T) = (4.73 ± 1.13) × 10(-17) exp[(6496 ± 1798)/T]; the quoted errors are the 2σ precision of the linear fit, and the estimated systematic uncertainties are included in the pre-exponential factor. The inverse temperature dependence suggests that the adsorption process occurs via the formation of an intermediate precursor state. Uptakes were well represented by the Langmuir adsorption model, and the saturation surface coverage, N(max), on pure thin ice films was (2.11 ± 0.16) × 10(14) molecules cm(-2), independent of temperature in the range 198-206 K. Light nitration (1.96 and 7.69 wt %) of ice films resulted in more efficient CH(3)C(O)OH uptakes and larger N(max) values that may be attributed to in-bulk diffusion or change in nature of the gas-ice surface interaction. Finally, it was estimated that the rate of adsorption of acetic acid on high-density cirrus clouds in the UT/LS is fast, and this is reflected in the short atmospheric lifetimes (2-8 min) of acetic acid; however, the extent of this uptake is minor resulting in at most a 5% removal of acetic acid in UT/LS cirrus clouds.     

Parallel hybrid heuristics for the permutation flow shop problem

This paper addresses the Permutation Flowshop Problem with minimization of makespan, which is denoted by Fm|prmu|C _max. In the permutational scenario, the sequence of jobs has to remain the same in all machines. The Flowshop Problem (FSP) is known to be NP-hard when more than three machines are considered. Thus, for medium and large scale instances, high-quality heuristics are needed to find good solutions in reasonable time. We propose and analyse parallel hybrid search methods that fully use the computational power of current multi-core machines. The parallel methods combine a memetic algorithm (MA) and several iterated greedy algorithms (IG) running concurrently. Two test scenarios were included, with short and long CPU times. The tests were conducted on the set of benchmark instances introduced by Taillard (Eur. J. Oper. Res. 64:278?C285, 1993), commonly used to assess the performance of new methods. Results indicate that the use of the MA to manage a pool of solutions is highly effective, allowing the improvement of the best known upper bound for one of the instances.     

Optimal persistent disturbance attenuation control for linear hybrid systems

In this paper, the disturbance attenuation properties for a class of linear hybrid systems are investigated, and a hybrid optimal persistent disturbance attenuation control problem is studied. First, a procedure is developed to determine the minimal l^∞$l^{\infty }$ induced gain of linear hybrid systems. However, for general hybrid systems, the termination of the procedure is not guaranteed. Then, the decidability issues are discussed. The termination of the procedure in a finite number of steps is shown for a subclass of hybrid systems with simplified discrete event dynamics, called switched linear systems. Finally, the optimal persistent disturbance attenuation controller synthesis problem is studied. It is shown that the optimal performance level can be achieved by a piecewise linear state feedback control law, and a systematic approach is proposed to design such feedback control.     

Convergence analysis of a global optimization algorithm using stochastic differential equations

We establish the convergence of a stochastic global optimization algorithm for general non-convex, smooth functions. The algorithm follows the trajectory of an appropriately defined stochastic differential equation (SDE). In order to achieve feasibility of the trajectory we introduce information from the Lagrange multipliers into the SDE. The analysis is performed in two steps. We first give a characterization of a probability measure (Π) that is defined on the set of global minima of the problem. We then study the transition density associated with the augmented diffusion process and show that its weak limit is given by Π.