Prophylactic ranitidine treatment in critically ill children - a population pharmacokinetic study

Aims - To characterize the population pharmacokinetics of ranitidine in critically ill children and to determine the influence of various clinical and demographic factors on its disposition. Methods - Data were collected prospectively from 78 paediatric patients (n = 248 plasma samples) who received oral or intravenous ranitidine for prophylaxis against stress ulcers, gastrointestinal bleeding or the treatment of gastro-oesophageal reflux. Plasma samples were analysed using high-performance liquid chromatography, and the data were subjected to population pharmacokinetic analysis using nonlinear mixed-effects modelling. Results - A one-compartment model best described the plasma concentration profile, with an exponential structure for interindividual errors and a proportional structure for intra-individual error. After backward stepwise elimination, the final model showed a significant decrease in objective function value (−12.618; P < 0.001) compared with the weight-corrected base model. Final parameter estimates for the population were 32.1 l h−1 for total clearance and 285 l for volume of distribution, both allometrically modelled for a 70 kg adult. Final estimates for absorption rate constant and bioavailability were 1.31 h−1 and 27.5%, respectively. No significant relationship was found between age and weight-corrected ranitidine pharmacokinetic parameters in the final model, with the covariate for cardiac failure or surgery being shown to reduce clearance significantly by a factor of 0.46. Conclusions - Currently, ranitidine dose recommendations are based on children's weights. However, our findings suggest that a dosing scheme that takes into consideration both weight and cardiac failure/surgery would be more appropriate in order to avoid administration of higher or more frequent doses than necessary.

Population pharmacokinetic and pharmacogenetic analysis of 6-mercaptopurine in paediatric patients with acute lymphoblastic leukaemia

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • The cytotoxic effects of 6-mercaptopurine (6-MP) were found to be due to drug-derived intracellular metabolites (mainly 6-thioguanine nucleotides and to some extent 6-methylmercaptopurine nucleotides) rather than the drug itself. • Current empirical dosing methods for oral 6-MP result in highly variable drug and metabolite concentrations and hence variability in treatment outcome. WHAT THIS STUDY ADDS • The first population pharmacokinetic model has been developed for 6-MP active metabolites in paediatric patients with acute lymphoblastic leukaemia and the potential demographic and genetically controlled factors that could lead to interpatient pharmacokinetic variability among this population have been assessed. • The model shows a large reduction in interindividual variability of pharmacokinetic parameters when body surface area and thiopurine methyltransferase polymorphism are incorporated into the model as covariates. • The developed model offers a more rational dosing approach for 6-MP than the traditional empirical method (based on body surface area) through combining it with pharmacogenetically guided dosing based on thiopurine methyltransferase genotype. AIMS - To investigate the population pharmacokinetics of 6-mercaptopurine (6-MP) active metabolites in paediatric patients with acute lymphoblastic leukaemia (ALL) and examine the effects of various genetic polymorphisms on the disposition of these metabolites. METHODS - Data were collected prospectively from 19 paediatric patients with ALL (n = 75 samples, 150 concentrations) who received 6-MP maintenance chemotherapy (titrated to a target dose of 75 mg m−2 day−1). All patients were genotyped for polymorphisms in three enzymes involved in 6-MP metabolism. Population pharmacokinetic analysis was performed with the nonlinear mixed effects modelling program (nonmem) to determine the population mean parameter estimate of clearance for the active metabolites. RESULTS - The developed model revealed considerable interindividual variability (IIV) in the clearance of 6-MP active metabolites [6-thioguanine nucleotides (6-TGNs) and 6-methylmercaptopurine nucleotides (6-mMPNs)]. Body surface area explained a significant part of 6-TGNs clearance IIV when incorporated in the model (IIV reduced from 69.9 to 29.3%). The most influential covariate examined, however, was thiopurine methyltransferase (TPMT) genotype, which resulted in the greatest reduction in the model's objective function (P < 0.005) when incorporated as a covariate affecting the fractional metabolic transformation of 6-MP into 6-TGNs. The other genetic covariates tested were not statistically significant and therefore were not included in the final model. CONCLUSIONS - The developed pharmacokinetic model (if successful at external validation) would offer a more rational dosing approach for 6-MP than the traditional empirical method since it combines the current practice of using body surface area in 6-MP dosing with a pharmacogenetically guided dosing based on TPMT genotype.

A meshless method for an inverse two-phase one-dimensional nonlinear Stefan problem

We extend a meshless method of fundamental solutions recently proposed by the authors for the one-dimensional two-phase inverse linear Stefan problem, to the nonlinear case. In this latter situation the free surface is also considered unknown which is more realistic from the practical point of view. Building on the earlier work, the solution is approximated in each phase by a linear combination of fundamental solutions to the heat equation. The implementation and analysis are more complicated in the present situation since one needs to deal with a nonlinear minimization problem to identify the free surface. Furthermore, the inverse problem is ill-posed since small errors in the input measured data can cause large deviations in the desired solution. Therefore, regularization needs to be incorporated in the objective function which is minimized in order to obtain a stable solution. Numerical results are presented and discussed. © 2014 IMACS.

Why are aggregate equity payouts pro-cyclical?

We use two general equilibrium models to explain why changes in the external economic environment result in pro-cyclical aggregate dividend payout behavior. Both models that we consider endogenize low elasticity of investment. The first model incorporates capital adjustment costs, while the second one assumes that risk-averse managers maximize their own objective function rather than shareholder wealth. We show that, while both models generate pro-cyclical aggregate dividends, a feature consistent with the observed business-cycle pattern of payouts from well-diversified portfolios, the second model provides a more likely explanation for this effect. Our findings emphasize the importance of incorporating agency conflicts when considering the relationship between the external economic environment and the financial behavior of businesses.

On Solving the Maximum Betweenness Problem Using Genetic Algorithms

In this paper a genetic algorithm (GA) is applied on Maximum Betweennes Problem (MBP). The maximum of the objective function is obtained by finding a permutation which satisfies a maximal number of betweenness constraints. Every permutation considered is genetically coded with an integer representation. Standard operators are used in the GA. Instances in the experimental results are randomly generated. For smaller dimensions, optimal solutions of MBP are obtained by total enumeration. For those instances, the GA reached all optimal solutions except one. The GA also obtained results for larger instances of up to 50 elements and 1000 triples. The running time of execution and finding optimal results is quite short.

An Electromagnetism Metaheuristic for the Uncapacitated Multiple Allocation Hub Location Problem

In this article, the results achieved by applying an electromagnetism (EM) inspired metaheuristic to the uncapacitated multiple allocation hub location problem (UMAHLP) are discussed. An appropriate objective function which natively conform with the problem, 1-swap local search and scaling technique conduce to good overall performance.Computational tests demonstrate the reliability of this method, since the EM-inspired metaheuristic reaches all optimal/best known solutions for UMAHLP, except one, in a reasonable time.

Laser tracker position optimization

This article presents a laser tracker position optimization code based on the tracker uncertainty model developed by the National Physical Laboratory (NPL). The code is able to find the optimal tracker positions for generic measurements involving one or a network of many trackers, and an arbitrary set of targets. The optimization is performed using pattern search or optionally, genetic algorithm (GA) or particle swarm optimization (PSO). Different objective function weightings for the uncertainties of individual points, distance uncertainties between point pairs, and the angular uncertainties between three points can be defined. Constraints for tracker position limits and minimum measurement distances have also been implemented. Furthermore, position optimization taking into account of lines-of-sight (LOS) within complex CAD geometry have also been demonstrated. The code is simple to use and can be a valuable measurement planning tool.

Laser tracker position optimization

This article presents a laser tracker position optimization code based on the tracker uncertainty model developed by the National Physical Laboratory (NPL). The code is able to find the optimal tracker positions for generic measurements involving one or a network of many trackers, and an arbitrary set of targets. The optimization is performed using pattern search or optionally, genetic algorithm (GA) or particle swarm optimization (PSO). Different objective function weightings for the uncertainties of individual points, distance uncertainties between point pairs, and the angular uncertainties between three points can be defined. Constraints for tracker position limits and minimum measurement distances have also been implemented. Furthermore, position optimization taking into account of lines-of-sight (LOS) within complex CAD geometry have also been demonstrated. The code is simple to use and can be a valuable measurement planning tool.

Well-Behavior, Well-Posedness and Nonsmooth Analysis

AMS subject classification: 90C30, 90C33.

Surrogate-based analysis and optimization for the design of heat sinks with jet impingement

Heat sinks are widely used for cooling electronic devices and systems. Their thermal performance is usually determined by the material, shape, and size of the heat sink. With the assistance of computational fluid dynamics (CFD) and surrogate-based optimization, heat sinks can be designed and optimized to achieve a high level of performance. In this paper, the design and optimization of a plate-fin-type heat sink cooled by impingement jet is presented. The flow and thermal fields are simulated using the CFD simulation; the thermal resistance of the heat sink is then estimated. A Kriging surrogate model is developed to approximate the objective function (thermal resistance) as a function of design variables. Surrogate-based optimization is implemented by adaptively adding infill points based on an integrated strategy of the minimum value, the maximum mean square error approach, and the expected improvement approaches. The results show the influence of design variables on the thermal resistance and give the optimal heat sink with lowest thermal resistance for given jet impingement conditions.

Solution of the Least Squares Method problem of pairwise comparison matrices

The aim of the paper is to present a new global optimization method for determining all the optima of the Least Squares Method (LSM) problem of pairwise comparison matrices. Such matrices are used, e.g., in the Analytic Hierarchy Process (AHP). Unlike some other distance minimizing methods, LSM is usually hard to solve because of the corresponding nonlinear and non-convex objective function. It is found that the optimization problem can be reduced to solve a system of polynomial equations. Homotopy method is applied which is an efficient technique for solving nonlinear systems. The paper ends by two numerical example having multiple global and local minima.

An improved framework for dynamic origin-destination (O-D) matrix estimation

This dissertation aims to improve the performance of existing assignment-based dynamic origin-destination (O-D) matrix estimation models to successfully apply Intelligent Transportation Systems (ITS) strategies for the purposes of traffic congestion relief and dynamic traffic assignment (DTA) in transportation network modeling. The methodology framework has two advantages over the existing assignment-based dynamic O-D matrix estimation models. First, it combines an initial O-D estimation model into the estimation process to provide a high confidence level of initial input for the dynamic O-D estimation model, which has the potential to improve the final estimation results and reduce the associated computation time. Second, the proposed methodology framework can automatically convert traffic volume deviation to traffic density deviation in the objective function under congested traffic conditions. Traffic density is a better indicator for traffic demand than traffic volume under congested traffic condition, thus the conversion can contribute to improving the estimation performance. The proposed method indicates a better performance than a typical assignment-based estimation model (Zhou et al., 2003) in several case studies. In the case study for I-95 in Miami-Dade County, Florida, the proposed method produces a good result in seven iterations, with a root mean square percentage error (RMSPE) of 0.010 for traffic volume and a RMSPE of 0.283 for speed. In contrast, Zhou's model requires 50 iterations to obtain a RMSPE of 0.023 for volume and a RMSPE of 0.285 for speed. In the case study for Jacksonville, Florida, the proposed method reaches a convergent solution in 16 iterations with a RMSPE of 0.045 for volume and a RMSPE of 0.110 for speed, while Zhou's model needs 10 iterations to obtain the best solution, with a RMSPE of 0.168 for volume and a RMSPE of 0.179 for speed. The successful application of the proposed methodology framework to real road networks demonstrates its ability to provide results both with satisfactory accuracy and within a reasonable time, thus establishing its potential usefulness to support dynamic traffic assignment modeling, ITS systems, and other strategies.

A quantitative approach to the organizational design problem

The span of control is the most discussed single concept in classical and modern management theory. In specifying conditions for organizational effectiveness, the span of control has generally been regarded as a critical factor. Existing research work has focused mainly on qualitative methods to analyze this concept, for example heuristic rules based on experiences and/or intuition. This research takes a quantitative approach to this problem and formulates it as a binary integer model, which is used as a tool to study the organizational design issue. This model considers a range of requirements affecting management and supervision of a given set of jobs in a company. These decision variables include allocation of jobs to workers, considering complexity and compatibility of each job with respect to workers, and the requirement of management for planning, execution, training, and control activities in a hierarchical organization. The objective of the model is minimal operations cost, which is the sum of supervision costs at each level of the hierarchy, and the costs of workers assigned to jobs. The model is intended for application in the make-to-order industries as a design tool. It could also be applied to make-to-stock companies as an evaluation tool, to assess the optimality of their current organizational structure. Extensive experiments were conducted to validate the model, to study its behavior, and to evaluate the impact of changing parameters with practical problems. This research proposes a meta-heuristic approach to solving large-size problems, based on the concept of greedy algorithms and the Meta-RaPS algorithm. The proposed heuristic was evaluated with two measures of performance: solution quality and computational speed. The quality is assessed by comparing the obtained objective function value to the one achieved by the optimal solution. The computational efficiency is assessed by comparing the computer time used by the proposed heuristic to the time taken by a commercial software system. Test results show the proposed heuristic procedure generates good solutions in a time-efficient manner.

Degradação fotocatalítica oxidativa do fenol utilizando carvão obtido da pirólise de diferentes biomassas

The modern industrial progress has been contaminating water with phenolic compounds. These are toxic and carcinogenic substances and it is essential to reduce its concentration in water to a tolerable one, determined by CONAMA, in order to protect the living organisms. In this context, this work focuses on the treatment and characterization of catalysts derived from the bio-coal, by-product of biomass pyrolysis (avelós and wood dust) as well as its evaluation in the phenol photocatalytic degradation reaction. Assays were carried out in a slurry bed reactor, which enables instantaneous measurements of temperature, pH and dissolved oxygen. The experiments were performed in the following operating conditions: temperature of 50 °C, oxygen flow equals to 410 mL min-1 , volume of reagent solution equals to 3.2 L, 400 W UV lamp, at 1 atm pressure, with a 2 hours run. The parameters evaluated were the pH (3.0, 6.9 and 10.7), initial concentration of commercial phenol (250, 500 and 1000 ppm), catalyst concentration (0, 1, 2, and 3 g L-1 ), nature of the catalyst (activated avelós carbon washed with dichloromethane, CAADCM, and CMADCM, activated dust wood carbon washed with dichloromethane). The results of XRF, XRD and BET confirmed the presence of iron and potassium in satisfactory amounts to the CAADCM catalyst and on a reduced amount to CMADCM catalyst, and also the surface area increase of the materials after a chemical and physical activation. The phenol degradation curves indicate that pH has a significant effect on the phenol conversion, showing better results for lowers pH. The optimum concentration of catalyst is observed equals to 1 g L-1 , and the increase of the initial phenol concentration exerts a negative influence in the reaction execution. It was also observed positive effect of the presence of iron and potassium in the catalyst structure: betters conversions were observed for tests conducted with the catalyst CAADCM compared to CMADCM catalyst under the same conditions. The higher conversion was achieved for the test carried out at acid pH (3.0) with an initial concentration of phenol at 250 ppm catalyst in the presence of CAADCM at 1 g L-1 . The liquid samples taken every 15 minutes were analyzed by liquid chromatography identifying and quantifying hydroquinone, p-benzoquinone, catechol and maleic acid. Finally, a reaction mechanism is proposed, cogitating the phenol is transformed into the homogeneous phase and the others react on the catalyst surface. Applying the model of Langmuir-Hinshelwood along with a mass balance it was obtained a system of differential equations that were solved using the Runge-Kutta 4th order method associated with a optimization routine called SWARM (particle swarm) aiming to minimize the least square objective function for obtaining the kinetic and adsorption parameters. Related to the kinetic rate constant, it was obtained a magnitude of 10-3 for the phenol degradation, 10-4 to 10-2 for forming the acids, 10-6 to 10-9 for the mineralization of quinones (hydroquinone, p-benzoquinone and catechol), 10-3 to 10-2 for the mineralization of acids.

On Provable Algorithms for Determination of Continuous Protein Interdomain Motions from Residual Dipolar Couplings

Dynamics of biomolecules over various spatial and time scales are essential for biological functions such as molecular recognition, catalysis and signaling. However, reconstruction of biomolecular dynamics from experimental observables requires the determination of a conformational probability distribution. Unfortunately, these distributions cannot be fully constrained by the limited information from experiments, making the problem an ill-posed one in the terminology of Hadamard. The ill-posed nature of the problem comes from the fact that it has no unique solution. Multiple or even an infinite number of solutions may exist. To avoid the ill-posed nature, the problem needs to be regularized by making assumptions, which inevitably introduce biases into the result.

Here, I present two continuous probability density function approaches to solve an important inverse problem called the RDC trigonometric moment problem. By focusing on interdomain orientations we reduced the problem to determination of a distribution on the 3D rotational space from residual dipolar couplings (RDCs). We derived an analytical equation that relates alignment tensors of adjacent domains, which serves as the foundation of the two methods. In the first approach, the ill-posed nature of the problem was avoided by introducing a continuous distribution model, which enjoys a smoothness assumption. To find the optimal solution for the distribution, we also designed an efficient branch-and-bound algorithm that exploits the mathematical structure of the analytical solutions. The algorithm is guaranteed to find the distribution that best satisfies the analytical relationship. We observed good performance of the method when tested under various levels of experimental noise and when applied to two protein systems. The second approach avoids the use of any model by employing maximum entropy principles. This 'model-free' approach delivers the least biased result which presents our state of knowledge. In this approach, the solution is an exponential function of Lagrange multipliers. To determine the multipliers, a convex objective function is constructed. Consequently, the maximum entropy solution can be found easily by gradient descent methods. Both algorithms can be applied to biomolecular RDC data in general, including data from RNA and DNA molecules.