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Results for “Optimization”

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At least 505 records · Page 28Linked to original sources

PubMed2002

Attenuation of pressure swings along the endotracheal tube is indicative of optimal distending pressure during high-frequency oscillatory ventilation in a model of acute lung injury.

We tested the hypothesis that during high-frequency oscillatory ventilation, the oscillatory pressure ratio (OPR) is minimal at the optimal mean airway pressure (Paw). OPR is defined as the ratio of pressure swings at the distal end and the proximal opening of the endotracheal tube. Optimal Paw was assumed to be the lowest Paw at which the physiological shunt fraction was below 0.1. Acute lung injury was produced by saline lung lavage of pigs who were then subjected to a stepwise increase of Paw to impose underinflation, optimal inflation, and overdistention (inflation phase), followed by a stepwise decrease of Paw (deflation phase). OPR reached a minimum of 0.10 +/- 0.01 at Paw = 31 +/- 4 cm H(2)O during the inflation phase and a minimum of 0.04 +/- 0.01 at Paw = 18 +/- 1 cm H(2)O during the deflation phase. Optimal Paw was 31 +/- 4 cm H(2)O on the inflation limb and 14 +/- 2 cm H(2)O on the deflation limb. Paw at the minimal OPR was not significantly different from the optimal Paw during the inflation phase, and slightly but significantly higher (4.1 +/- 1.6 cm H(2)O) during the deflation phase. In conclusion, a consistent relationship was found between OPR and Paw, with a minimum in all animals. The minimal OPR coincides fairly well with the Paw where oxygenation is optimal.

Animals↗

PubMed1995

The optimization of protein-solvent interactions: thermostability and the role of hydrophobic and electrostatic interactions.

Protein-solvent interactions were analyzed using an optimization parameter based on the ratio of the solvent-accessible area in the native and the unfolded protein structure. The calculations were performed for a set of 183 nonhomologous proteins with known three-dimensional structure available in the Protein Data Bank. The dependence of the total solvent-accessible surface area on the protein molecular mass was analyzed. It was shown that there is no difference between the monomeric and oligomeric proteins with respect to the solvent-accessible area. The results also suggested that for proteins with molecular mass above some critical mass, which is about 28 kDa, a formation of domain structure or subunit aggregation into oligomers is preferred rather than a further enlargement of a single domain structure. An analysis of the optimization of both protein-solvent and charge-charge interactions was performed for 14 proteins from thermophilic organisms. The comparison of the optimization parameters calculated for proteins from thermophiles and mesophiles showed that the former are generally characterized by a high degree of optimization of the hydrophobic interactions or, in cases where the optimization of the hydrophobic interactions is not sufficiently high, by highly optimized charge-charge interactions.

Bacterial Proteins↗

PubMed2002

Fisher information matrix for non-linear mixed-effects models: evaluation and application for optimal design of enoxaparin population pharmacokinetics.

We address the problem of the choice and the evaluation of designs in population pharmacokinetic studies that use non-linear mixed-effects models. Criteria, based on the Fisher information matrix, have been developed to optimize designs and adapted to such models. We optimize designs under different constraints and evaluate them for a population pharmacokinetics study, within a new phase III trial of enoxaparin, a low molecular weight heparin. To do this, we approximate the expression of the Fisher information matrix for non-linear mixed-effects models including the residual error variance as a parameter to be estimated. We use the Fedorov-Wynn algorithm to minimize the inverse of the determinant of this matrix as required by the D-optimality criterion. Two optimal designs, as well as a design defined by pharmacologists, are evaluated by the simulation of 30 replicated data sets with NONMEM; all designs involve 220 patients with four measurements per patient. We also evaluate the relevance of the standard errors of estimation given from the Fisher information matrix by comparison with those given by NONMEM. The three designs provide more precise population parameter estimates; the optimal design gives the best precision and offers a simple clinical implementation. The expected standard errors given by the information matrix are close to those obtained by NONMEM on the simulation. Moreover, the proposed criterion of D-optimality appears to be a good measure to compare designs for population studies.

Anticoagulants↗

PubMed2005

Optimal time-points in clinical trials with linearly divergent treatment effects.

In repeated measures studies, equidistant time-points do not always yield efficient treatment effect estimators. In the present paper, the optimal allocation of time-points is calculated for a small number of repeated measures, different covariance structures and linearly divergent treatment effects. The gain in efficiency of the treatment effect estimator by using optimally allocated time-points instead of equidistant time-points or by adding optimally spaced measures (at the expense of patients) is then computed. The assumed covariance structure is crucial for the results. For a compound symmetric covariance structure, a large gain in efficiency is obtained by adding repeated measures at the end of the study. For a first-order auto-regressive covariance structure, highly efficient treatment effect estimators are obtained with only two repeated measures, i.e. at the start and at the end of the study. For a first-order auto-regressive covariance structure including measurement error, the gain in efficiency by adding optimally spaced measures depends on the covariance parameter values. The gain in efficiency is similar with or without a random intercept. For a fixed study budget, the commonly used design with more than two equally spaced measures was never optimal for the linear cost function and covariance structures that were used. If the covariance structure is unknown, the optimal design based on a first-order auto-regressive covariance structure with measurement error is preferable in terms of robustness against misspecification of the covariance structure. The numerical results are illustrated by two examples.

Clinical Trials as Topic↗

PubMed1999

Optimization of the cost and sensitivity of receptor- and enzyme-based assays.

In detecting receptor antagonists or enzyme inhibitors, there are three parameters that often affect the outcome in a predictable quantitative manner: concentrations of the receptors (enzyme), labeled ligand (substrate), and antagonist (inhibitor). The usual goal of assay optimization is to maximize the ability of the assay to detect low concentrations of the analyte. Another question of practical importance, especially in screening of large numbers of samples, would be minimization of the reagent cost. Although the mathematical theory of optimization of the receptor binding assay was developed a long time ago, the resulting formulas (in the general case of unequal affinities of ligand and competitor) were not well suited for practical use. The current availability of computational programs, such as Mathematica, makes possible an efficient solution, both for receptor- and enzyme-based assays. We use a graphical approach to assay optimization and apply it to the following problems: (1) optimization of assay sensitivity, (2) optimization of the reagent cost, and (3) analysis of the entire range of the parameter values since the mathematically optimal values may sometimes be impractical. The computation is extremely simple and the problem can sometimes be solved in several minutes.

Binding, Competitive↗

PubMed1999

Optimal stoichiometric designs of ATP-producing systems as determined by an evolutionary algorithm.

The design of metabolic pathways is thought to be the result of an optimization process such that the structure of contemporary metabolic routes maximizes a particular objective function. Recently, it has been shown that some essential stoichiometric properties of glycolysis can be explained on the basis of the requirement for a high ATP production rate. Because the number of stoichiometrically feasible designs increases strongly with the number of reactions involved, a systematic analysis of all the possibilities turns out to be inaccessible beyond a certain system size. We present, therefore, an alternative approach to compute in a more efficient way the optimal design of glycolysis interacting with an external ATP-consuming reaction. The algorithm is based on the laws of evolution by natural selection, and may be viewed as a particular version of evolutionary algorithms. The following conclusions are derived: (a) evolutionary algorithms are very useful search strategies in determining optimal stoichiometries of metabolic pathways. (b) Essential topological features of the glycolytic network may be explained on the basis of flux optimization. (c) There is a strong interrelation between the optimal stoichiometries and the thermodynamic and kinetic properties of the participating reactions. (d) Some subsequences of reactions in optimal pathways are strongly conserved at variation of system parameters, which may be understood by applying principles of metabolic control analysis.

Adenosine Triphosphatases↗

PubMed1990

Evolutionary optimization and neural network models of behavior.

One of the main challenges to the adaptionist program in general and the use of optimization models in behavioral and evolutionary ecology, in particular, is that organisms are so constrained by ontogeny and phylogeny that they may not be able to attain optimal solutions, however those are defined. This paper responds to the challenge through the comparison of optimality and neural network models for the behavior of an individual polychaete worm. The evolutionary optimization model is used to compute behaviors (movement in and out of a tube) that maximize a measure of Darwinian fitness based on individual survival and reproduction. The neural network involves motor, sensory, energetic reserve and clock neuronal groups. Ontogeny of the neural network is the change of connections of a single individual in response to its experiences in the environment. Evolution of the neural network is the natural selection of initial values of connections between groups and learning rules for changing connections. Taken together, these can be viewed as "design parameters". The best neural networks have fitnesses between 85% and 99% of the fitness of the evolutionary optimization model. More complicated models for polychaete worms are discussed. Formulation of a neural network model for host acceptance decisions by tephritid fruit flies leads to predictions about the neurobiology of the flies. The general conclusion is that neural networks appear to be sufficiently rich and plastic that even weak evolution of design parameters may be sufficient for organisms to achieve behaviors that give fitnesses close to the evolutionary optimal fitness, particularly if the behaviors are relatively simple.

Animals↗

PubMed1981

Optimal sampling times for pharmacokinetic experiments.

A sequential estimation procedure is presented which uses optimal sampling times to estimate the parameters of a model from data obtained from a group of subjects. This optimal sampling sequential estimation procedure utilizes parameter estimates from previous subjects in the group to determine the optimal sampling times for the next subject. Parameter estimates obtained from the optimal sampling procedure are compared to those obtained from a conventional sampling scheme by using Monte Carlo simulations which include noise terms for both assay error and intersubject variability. The results of these numerical experiments, for the two examples considered here, show that the parameter estimates obtained from data collected at optimal sampling times have significantly less variability than those generated using the conventional sampling procedure. We conclude that optimal sampling and preexperiment simulation may be useful tools for designing informative pharmacokinetic experiments.

Animals↗

PubMed1995

Rapid optimization of electroporation conditions for plant cells, protoplasts, and pollen.

The optimization of electroporation conditions for maximal uptake of DNA during direct gene transfer experiments is critical to achieve high levels of gene expression in transformed plant cells. Two stains, trypan blue and fluorescein diacetate, have been applied to optimize electroporation conditions for three plant cell types, using different square wave and exponential wave electroporation devices. The different cell types included protoplasts from tobacco, a stable mixotrophic suspension cell culture from soybean with intact cell walls, and germinating pollen from alfalfa and tobacco. Successful electroporation of each of these cell types was obtained, even in the presence of an intact cell wall when conditions were optimized for the electroporation pulse. The optimal field strength for each of these cells differs, protoplasts having the lowest optimal pulse field strength, followed by suspension cells and finally germinating pollen requiring the strongest electroporation pulse. A rapid procedure is described for optimizing electroporation parameters using different types of cells from different plant sources.

Electroporation↗

PubMed2000

Optimism and depression as predictors of physical and mental health functioning: the Normative Aging Study.

Dispositional optimism has been linked in previous studies to better health outcomes. We sought to examine the independent associations of dispositional optimism and depressive symptoms with physical and mental functioning in a cohort of healthy middle-aged and older men. The study was conducted among 659 subjects in the Veterans Administration (VA) Normative Aging Study. Dispositional optimism and depressive symptomatology were measured in 1991 and 1990, respectively, by the Life Orientation Test and the Center for Epidemiologic Studies--Depression Scale (CES-D). The dependent variables, functioning and well-being, were measured in 1992 by the Medical Outcomes Study Short-Form Health Survey (SF-36). In multivariate regression models, optimism was associated with higher levels of general health perceptions, vitality, and mental health, and lower levels of bodily pain, but not to physical functioning, social functioning, or role limitations due to physical or emotional problems. Depressive symptomatology was associated with reduced levels of functioning across all SF-36 domains. The findings for optimism and depression were statistically significant after mutual adjustment in multivariate regression models. Optimism and depression are independent predictors of functional status among aging men.

Aged↗

PubMed1996

Optimal surface electrode positioning for reliable train of four muscle relaxation monitoring.

In the clinic, a major problem in train of four (TOF) muscle relaxation monitoring is incorrect placement of stimulation and recording electrodes, frequently resulting in incorrect estimates of the patient's degree of relaxation or in abandonment of relaxation monitoring. The aim of this study was to arrive at recommendations that describe how to find optimal positions for the electrodes, where 'optimal' is taken in the sense that small deviations from these positions introduce no or only a small decline in the accuracy of the computed degree of muscle relaxation. This study, which employed the Relaxograph as the stimulation and measuring device, established that incorrect positioning is a real problem that frequently occurs; that the correctness of positioning is not guaranteed when the calibration of the Relaxograph succeeds; that the inadequacy of the electrode position is sometimes discovered for the first time when relaxation deepens; that positioning errors can be discovered by analysing the shape of the evoked compound action potential (ECAP), not only upon calibration but also when relaxation deepens; that a set of optimal electrode positions can be found; and that recommendations of how to find these optimal positions could help clinicians to place the electrodes in such a way, that reliable relaxation monitoring was possible in 100% of the investigated cases. In a first test in 30 adult patients, we surveyed how clinicians routinely positioned electrodes and found that in 14 of the 30 cases positioning was unsuccessful. In a second test in 10 patients, we tested a variety of electrode positions in order to discover 'optimal' stimulation, recording and ground electrode sites. In a third test in 10 patients, electrodes were positioned at these 'optimal' sites; stimulation and recording at these sites was successful in all 10 cases.

Adult↗

PubMed1993

Optimization of Butterworth filter for brain SPECT imaging.

A method has been described to optimize the cutoff frequency of the Butterworth filter for brain SPECT imaging. Since a computer simulation study has demonstrated that separation between an object signal and the random noise in projection images in a spatial-frequency domain is influenced by the total number of counts, the cutoff frequency of the Butterworth filter should be optimized for individual subjects according to total counts in a study. To reveal the relationship between the optimal cutoff frequencies and total counts in brain SPECT study, we used a normal volunteer and 99mTc hexamethyl-propyleneamine oxime (HMPAO) to obtain projection sets with different total counts. High quality images were created from a projection set with an acquisition time of 300-seconds per projection. The filter was optimized by calculating mean square errors from high quality images visually inspecting filtered reconstructed images. Dependence between total counts and optimal cutoff frequencies was clearly demonstrated in a nonogram. Using this nomogram, the optimal cutoff frequency for each study can be estimated from total counts, maximizing visual image quality. The results suggest that the cutoff frequency of Butterworth filter should be determined by referring to total counts in each study.

Brain↗

PubMed2005

Automatic generation of a plan optimization volume for tangential field breast cancer radiation therapy.

BACKGROUND AND PURPOSE: Dose homogeneity is one of the objectives during computer planning of postoperative radiotherapy of the conserved breast. For three-dimensional (3-D) optimization of the dose distribution using serial CT scan images, suitable volumes have to be delineated. The purpose of this study was to develop a computer-generated delineation of a plan optimization volume (POV) and an irradiated volume (IV) and to automate their use in a fast dose homogeneity optimization engine. PATIENTS AND METHODS: Simulation was performed according to our standard procedure which involves the positioning of a lead collar around the palpable breast to facilitate the definition of gantry angle, collimator angle and field aperture for tangential wedged photon beams. In a change to the standard procedure an anterolateral radiograph was taken with its axis orthogonal to the central plane of the two tangential half-beams. Images from a serial CT scan were acquired in treatment position, and the geometric data of the three simulated beams were used by a computer program to generate the POV and IV. For each patient, weights of wedged and unwedged beams were optimized by either human heuristics using only the central slice (2-D), the whole set of CT slices (3-D), or by a computer algorithm using the POV, IV and lung volume with constrained matrix inversion (CMI) as optimization method. The resulting dose distributions were compared. RESULTS: The total planning procedure took, on average, 44 min of which < 7 min were needed for human interactions, compared to about 52 min for the standard planning at Ghent University Hospital, Belgium. The simulation time is increased by 2-3 min. The method provides 3-D information of the dose distribution. Dose homogeneity and minimum dose inside the POV and maximum dose inside the IV were not significantly different for the three optimization techniques. CONCLUSION: This automated planning method is capable of replacing the contouring of the clinical target volume as well as the trial-and-error procedure of assigning weights of wedged and unwedged beams by an experienced planner.

Algorithms↗

PubMed2003

Function of brainstem neurons in optimal control of respiratory mechanics.

An optimization control procedure is developed to describe the function of the human respiratory controller in determination of the respiratory frequency, the expiratory reserve volume, and the physiological dead space volume at all levels of human activity. The required level of alveolar ventilation is considered to have been determined based on the inputs from the peripheral and central chemoreceptors. The proposed procedure describes the mechanical control of breathing in which the excitation signals are adjusted and transferred from the neuron pools in the brainstem to the respiratory muscles to control the rate and depth of breathing. The criterion of minimum average respiratory work rate is used to find the optimal characteristics of respiration. The respiratory frequency, physiologic dead space volume, and expiratory reserve volume are used simultaneously as the optimization variables to minimize the average respiratory work rate. The optimization procedure has been applied by using different airflow patterns at various levels of ventilation. The theoretical results of the study have been compared with the experimental data in exercise taken from the literature. The results show a close agreement between the experimentally measured data and the theoretical values found by the optimization control procedure. The findings attest to the validity of the minimum average work rate criterion and the proposed multivariable optimization procedure compared with other procedures suggested in the literature in control of respiratory mechanics.

Algorithms↗

PubMed2006

Symbols as self-emergent entities in an optimization process of feature extraction and predictions.

In the mammalian cortex the early sensory processing can be characterized as feature extraction resulting in local and analogue low-level representations. As a direct consequence, these map directly to the environment, but interpretation under natural conditions is ambiguous. In contrast, high-level representations for cognitive processing, e.g. language, require symbolic representations characterized by expression and syntax. The representations are binary, structured and disambiguated. However, do these fundamental functional distinctions translate into a fundamental distinction of the respective brain areas and their anatomical and physiological properties? Here we argue that the distinction between early sensory processing and higher cognitive functions may not be based on structural differences of cortical areas; instead similar learning principles acting on input signals with different statistics give rise to the observed variations of function. Firstly, we give an account of present research describing neuronal properties at early stages of sensory systems as a consequence of an optimization process over the set of natural stimuli. Secondly, addressing a stage following early visual processing we suggest to extend the unsupervised learning scheme by including predictive processes. These contain the widely used objective of temporal coherence as a special case and are a powerful approach to resolve ambiguities. Furthermore, in combination with a prior on the bandwidth of information exchange between units it leads to a condensation of information. Thirdly, as a crucial step, not only are predictive units optimized, but the selectivity of the feature extractors are adapted to allow optimal predictability. Thus, over and beyond making useful predictions, we propose that the predictability of a stimulus be in itself a selection criterion for further processing. In a hierarchical system the combined optimization process leads to entities that represent condensed pieces of knowledge and that are not analogue anymore. Instead, these entities work as arguments in a framework of transformations that realize predictions. Thus, the criteria of predictability and condensation in an optimization of sensory representations relate directly to the two defining properties of symbols of expression and syntax. In this paper, we sketch an unsupervised learning process that gradually transforms analogue local representations into discrete binary representations by means of four hypotheses. We propose that in this optimization process acting in a hierarchical system, entities emerge at, higher levels that fulfil the criteria defining symbols, instantiating qualitatively different representations at similarly structured low and high levels.

Animals↗

PubMed2004

Genetic algorithms with filters for optimal control problems in fed-batch bioreactors.

When using a genetic algorithm (GA) to solve optimal control problems that can arise in a fed-batch bioreactor, the most obvious direct approach is to rely on a finite dimensional discretization of the optimal control problem into a nonlinear programming problem. Usually only the control function is discretized, and the continuous control function is approximated by a series of piecewise constant functions. Even though the piecewise discretized controls that the GA produces for the optimal control problem may give good performances, the control policies often show very high activity and differ considerably from those obtained using a continuous optimization strategy. The present study introduces a few filters into a real-coded genetic algorithm as additional operators and investigates the smoothing capabilities of the filters employed. It is observed that inclusion of a filter significantly smoothens the optimal control profile and often encourages the convergence of the algorithm. The applicability of the technique is illustrated by solving two previously reported optimal control problems in fed-batch bioreactors that are known to have singular arcs.

Algorithms↗

PubMed2000

Optimization method for 3D bracing correction of scoliosis using a finite element model.

Scoliosis is a complex three-dimensional deformity of the spine and rib cage frequently treated by brace. Although bracing produces significant correction in the frontal plane, it generally reduces the normal sagittal plane curvatures and has limited effect in the transverse plane. The goal of this study is to develop a new optimization approach using a finite element model of the spine and rib cage in order to find optimal correction patterns. The objective function to be minimized took account of coronal and sagittal offsets from a normal spine at the thoracic and lumbar apices as well as the rib hump. Two different optimization studies were performed using the finite element model, which was personalized to the geometry of 20 different scoliotic patients. The first study took into account only the thoracic deformity, while the second considered both the thoracic and lumbar deformities. The optimization produced an average of 56% and 51% reduction of the objective function respectively in the two studies. Optimal forces were mostly located on the convex side of the curve. This study demonstrates the feasibility of using an optimization approach with a finite element model of the trunk to analyze the biomechanics of bracing, and may be useful in the design of new and more effective braces.

Adolescent↗

PubMed2002

Multivariate optimization of polymerase chain reaction for microbial community analysis.

A multivariate regression, partial least square (PLS) approach was used to optimize a polymerase chain reaction (PCR) method for mixed communities. This approach, in contrast to univariate ones, provided information on the relative influence of the different factors to be optimized, as well as the interactions between factors. Models that predicted the outcome of further optimization were constructed from the initial experiments and verified experimentally. The models constructed were able to predict the outcome of a second set of experiments with high accuracy. PCR-amplification of DNA from environmental samples is often the first step in microbial community fingerprinting. Inhibitors and low cell numbers in the samples can cause problems with yield, for which compensation is normally made by increasing the number of cycles in the PCR-amplification reaction. Increasing the number of cycles, however, can cause other problems such as heteroduplex formation and increased bias. To avoid these problems the effects of different times of denaturing, annealing, and extension on yield were investigated for 2 different samples, one that consisted of a mixture of 9 laboratory strains, and one that represented the microbial community from the surface of the red alga Delisea pulchra. The multivariate approach showed, in addition to the successful optimization of yield, that the different factors affected the PCR depending on sample type. Annealing time had the largest effect on yield for the mixture of laboratory strains, whereas extension time was most important for the D. pulchra community. We suggest that multivariate optimization is a useful tool for PCR optimization and can be used irrespectively of the particular factors that are being investigated.

Journal Article↗