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PubMed2003

Performance change of mammographic CAD schemes optimized with most-recent and prior image databases.

RATIONALE AND OBJECTIVES: The authors evaluated performance changes in the detection of masses on "current" (latest) and "prior" images by computer-aided diagnosis (CAD) schemes that had been optimized with databases of current and prior mammograms. MATERIALS AND METHODS: The authors selected 260 pairs of matched consecutive mammograms. Each current image depicted one or two verified masses. All prior images had been interpreted originally as negative or probably benign. A CAD scheme initially detected 261 mass regions and 465 false-positive regions on the current images, and 252 corresponding mass regions (early signs) and 471 false-positive regions on prior images. These regions were divided into two training and two testing databases. The current and prior training databases were used to optimize two CAD schemes with a genetic algorithm. These schemes were evaluated with two independent testing databases. RESULTS: The scheme optimized with current images produced areas under the receiver operating characteristic curve of (0.89 +/- 0.01 and 0.65 +/- 0.02 when tested with current images and prior images, respectively. The scheme optimized with prior images produced areas under the receiver operating characteristic curve of 0.81 +/- 0.02 and 0.71 +/- 0.02 when tested with current images and prior images, respectively. Performance changes for both current and prior testing databases were significant (P < .01) for the two schemes. CONCLUSION: CAD schemes trained with current images do not perform optimally in detecting masses depicted on prior images. To optimize CAD schemes for early detection, it may be important to include in the training database a large fraction of prior images originally reported as negative and later proven to be positive.

Algorithms↗

PubMed2002

Optimal neural network architecture selection: improvement in computerized detection of microcalcifications.

RATIONALE AND OBJECTIVES: The authors evaluated the effect of optimal neural network architecture selection on the performance of a computer-aided diagnostic system designed to detect microcalcification clusters on digitized mammograms. MATERIALS AND METHODS: The authors developed a computer program to detect microcalcification clusters automatically on digitized mammograms. Previously, they found that a properly selected and trained convolution neural network (CNN) could reduce false-positive (FP) findings and therefore improve the accuracy of microcalcification detection. In the current study, they evaluated the effectiveness of the CNN optimized with an automated optimization technique in improving the accuracy of the microcalcification detection program, comparing it with the manually selected CNN. An independent test data set was used, which included 472 mammograms selected from the University of South Florida public database and contained 253 biopsy-proved malignant clusters. RESULTS: At an FP rate of 0.7 cluster per image, the film-based sensitivity was 84.6% for the optimized CNN, compared with 77.2% for the manually selected CNN. For clusters imaged on both craniocaudal and mediolateral oblique views, a cluster could be considered detected when it was detected on one or both views. For this case-based approach, at an FP rate of 0.7 per image, the sensitivity was 93.3% for the optimized and 87.0% for the manually selected CNN. CONCLUSION: The classification of true and false signals is an important step in the microcalcification detection program. An optimized CNN can effectively reduce FP findings and improve the accuracy of the computer-aided detection system.

Breast Neoplasms↗

PubMed2000

High level secretion of recombinant human serum albumin by fed-batch fermentation of the methylotrophic yeast, Pichia pastoris, based on optimal methanol feeding strategy.

The dynamic programming method was applied to obtain the optimal specific growth rate, mu, in the fed-batch fermentation using the recombinant human serum albumin (rHSA)-producing yeast, Pichia pastoris. Based on the relationship between the specific production rate, varrho, and the specific growth rate, mu, a simple mathematical model describing the growth and rHSA production was constructed and used for calculations. Two constraints, final volume and maximum methanol feed rate, were adopted for calculations and the optimal mu resulted as follows. That is, mu was initially at the maximum value, mu(max), then decreased gradually. Finally, mu decreased to the mu(min) that gave a maximum varrho. The decline of mu was revealed to be caused by the constraint for maximum methanol feeding rate, F(max), and F(max) was constant until mu decreased to mu(min). We tried to realize the optimal mu in the fed-batch fermentation by manipulating the methanol feeding rate and obtained it. However, the observed varrho was differed from the expected one. The discrepancy between the expected varrho and observed varrho after the change of mu suggests the inapplicability of the relationship between mu and varrho to dynamic situations where mu changes. To confirm this, simulation and fed-batch fermentation runs were carried out at a methanol feeding rate that would cause a continuous change in mu. The rHSA production was simulated well, suggesting the applicability of the relationship between mu and varrho in such situations. Discontinuity in the change in methanol feeding rate of the optimal feed pattern at the time mu changed is considered to be the cause for the discrepancy between the expected and observed varrho. Therefore, a new methanol feeding strategy that could mimic the changes in mu and varrho of the optimal strategy without a discontinuity in the feeding rate was sought using a mathematical model of fermentation by trial and error. This modification in the methanol feeding rate resulted in a considerably improved varrho and 18% increase in total rHSA production compared with those obtained by the optimal strategy.

Journal Article↗

PubMed2003

Explanatory optimization of protein mass spectrometry via genetic search.

Optimizing experimental conditions for the effective analysis of intact proteins by mass spectrometry is challenging, as many analytical factors influence the spectral quality, often in very different ways for different proteins and especially with complex protein mixtures. We show that genetic search methods are highly effective in this kind of optimization and that it was possible in 6 generations with a total of <500 experiments out of some 10(14) to find good combinations of experimental variables (electrospray ionization mass spectral settings) that would not have been detected by optimizing each variable alone (i.e., the search space is epistatic). Moreover, by inspecting the evolution of the variables to be optimized using genetic programming, we discovered an important relationship between two of the mass spectrometer settings that accounts for much of this success. Specifically, the conditions that were evolved included very low values of skimmer 1 voltage (the sample cone) and a skimmer 2 voltage (extraction cone) above a threshold that would nevertheless minimize the potential difference between the sample and extraction skimmers. The discovery of this relationship demonstrates the hypothesis-generating ability of genetic search in optimization processes where the size of the search space means that little or no a priori knowledge of the optimal conditions is available.

Animals↗

PubMed2007

Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics.

Metabolomics seeks to measure potentially all the metabolites in a biological sample, and consequently, we need to develop and optimize methods to increase significantly the number of metabolites we can detect. We extended the closed-loop (iterative, automated) optimization system that we had previously developed for one-dimensional GC-TOF-MS (O'Hagan, S.; Dunn, W. B.; Brown, M.; Knowles, J. D.; Kell, D. B. Anal. Chem. 2005, 77, 290-303) to comprehensive two-dimensional (GCxGC) chromatography. The heuristic approach used was a multiobjective version of the efficient global optimization algorithm. In just 300 automated runs, we improved the number of metabolites observable relative to those in 1D GC by some 3-fold. The optimized conditions allowed for the detection of over 4000 raw peaks, of which some 1800 were considered to be real metabolite peaks and not impurities or peaks with a signal/noise ratio of less than 5. A variety of computational methods served to explain the basis for the improvement. This closed-loop optimization strategy is a generic and powerful approach for the optimization of any analytical instrumentation.

Biomarkers↗

PubMedDate not supplied

Productivity improvement in xanthan gum fermentation using multiple substrate optimization.

A novel and more comprehensive formulation of the optimal control problem that reflects the operational requirements of a typical industrial fermentation has been proposed in this work. This formulation has been applied to a fed-batch bioreactor with three control variables, i.e., feed rates of carbon source, nitrogen source, and an oxygen source, to result in a 148.7% increase in product formation. Xanthan gum production using Xanthomonas campestris has been used as the model system for this optimization study, and the liquid-phase oxygen supply strategy has been used to supply oxygen to the fermentation. The formulated optimization problem has several constraints associated with it due to the nature of the system. A robust stochastic technique, differential evolution, has been used to solve this challenging optimization problem. The infinite dimensional optimization problem has been approximated to a finite dimensional one by control vector parametrization. The state constraints that are path constraints have been addressed by using penalty functions and by integrating them over the total duration to ensure a feasible solution. End point constraints on final working volume of the reactor and on the final residual concentrations of carbon and nitrogen sources have been included in the problem formulation. Further, the toxicity of the oxygen source, H(2)O(2), has been addressed by imposing a constraint on its maximum usable concentration. In addition, the initial volume of the bioreactor contents and feed concentrations have been handled as decision variables, which has enabled a well-grounded choice for their values from the optimization procedure; adhoc values are normally used in the industry. All results obtained by simulation have been validated experimentally with good agreements between experimental and simulated values.

Algorithms↗

PubMedDate not supplied

Optimization of batch and fed-batch bioreactors using simulated annealing.

In order for biobased industrial products to compete economically with petroleum-derived products, significant reduction in their processing cost is necessary. Since most bioprocesses are operated in batch or fed-batch mode, their optimization involves theoretical and computational challenges. Simulated annealing (SA), a stochastic optimization algorithm, is used in this study to solve a number of challenging optimization problems related to the design and operation of bioreactors. Two well-known case studies are considered in which the robustness and efficiency of the SA algorithm is demonstrated. More specifically, in the first case study it is shown that the global optimal solution located by SA achieves significant improved productivity when compared with the results of previous investigations. In the second case study a realistic objective function is considered where the economic performance of a bioprocess is optimized. SA exhibits impeccable performance and robustness and was able to locate the global optimal solution irrespective of the initial point selected.

Algorithms↗

PubMedDate not supplied

Fluorescence spectroscopy and multivariate spectral descriptor analysis for high-throughput multiparameter optimization of polymerization conditions of combinatorial 96-microreactor arrays.

Selection of optimum process conditions in combinatorial microreactors is essential if the combinatorial synthesis process is to be correlated with the synthesis process on a more conventional scale and the materials are to have the desired chemical properties. We have developed a new methodology for the high-throughput multiparameter optimization of polymerization reaction conditions in arrays of microreactors. Our strategy is based on the application of nondestructive spectroscopic techniques to measure chemical properties of polymers directly in individual microreactors followed by the multivariate spectral descriptor analysis for rapid determination of the optimal process conditions. We have demonstrated our strategy in the high-throughput multiparameter optimization of process conditions in thin-film melt polymerization reactions performed in 96-microreactor arrays for combinatorial screening of new polymerization catalysts. The combinatorial polymerization system was optimized for the best processing parameters using a set of input variables that included reactant parameters (relative amounts of starting components and catalyst loading) and processing variables (reaction time, reaction temperature, and inert gas flow rate). The measured output parameters were the chemical properties of materials and reproducibility of the material formation in replicate polymerizations in microreactors. Spatially resolved nondestructive evaluation of polymer formation was performed directly in individual microreactors and provided information about the spatial homogeneity of polymers in microreactors. It showed to be another powerful indicator of the reproducible polymerization process on the combinatorial scale. Although the methodology described here was implemented for high-throughput optimization of polymerization conditions, it is more general and can be further implemented for a variety of applications in which optimization of process parameters can be studied in situ or off-line using spectroscopic and other tools.

Journal Article↗

PubMed2006

Optimal charges in lead progression: a structure-based neuraminidase case study.

Collective experience in structure-based lead progression has found electrostatic interactions to be more difficult to optimize than shape-based ones. A major reason for this is that the net electrostatic contribution observed includes a significant nonintuitive desolvation component in addition to the more intuitive intermolecular interaction component. To investigate whether knowledge of the ligand optimal charge distribution can facilitate more intuitive design of electrostatic interactions, we took a series of small-molecule influenza neuraminidase inhibitors with known protein cocrystal structures and calculated the difference between the optimal and actual charge distributions. This difference from the electrostatic optimum correlates with the calculated electrostatic contribution to binding (r(2) = 0.94) despite small changes in binding modes caused by chemical substitutions, suggesting that the optimal charge distribution is a useful design goal. Furthermore, detailed suggestions for chemical modification generated by this approach are in many cases consistent with observed improvements in binding affinity, and the method appears to be useful despite discrete chemical constraints. Taken together, these results suggest that charge optimization is useful in facilitating generation of compound ideas in lead optimization. Our results also provide insight into design of neuraminidase inhibitors.

Binding Sites↗

PubMed1998

Comparative molecular field analysis of artemisinin derivatives: ab initio versus semiempirical optimized structures.

Based on the belief that structural optimization methods, producing structures more closely to the experimental ones, should give better, i.e. more relevant, steric fields and hence more predictive CoMFA models, comparative molecular field analyses of artemisinin derivatives were performed based on semiempirical AM1 and HF/3-21G optimized geometries. Using these optimized geometries, the CoMFA results derived from the HF/3-21G method are found to be usually but not drastically better than those from AM1. Additional calculations were performed to investigate the electrostatic field difference using the Gasteiger and Marsili charges, the electrostatic potential fit charges at the AM1 level, and the natural population analysis charges at the HF/3-21G level of theory. For the HF/3-21G optimized structures no difference in predictability was observed, whereas for AM1 optimized structures such differences were found. Interestingly, if ionic compounds are omitted, differences between the various HF/3-21G optimized structure models using these electrostatic fields were found.

Animals↗

PubMed1999

Protein structure prediction in a 210-type lattice model: parameter optimization in the genetic algorithm using orthogonal array.

We have applied the orthogonal array method to optimize the parameters in the genetic algorithm of the protein folding problem. Our study employed a 210-type lattice model to describe proteins, where the orientation of a residue relative to its neighboring residue is described by two angles. The statistical analysis and graphic representation show that the two angles characterize protein conformations effectively. Our energy function includes a repulsive energy, an energy for the secondary structure preference, and a pairwise contact potential. We used orthogonal array to optimize the parameters of the population, mating factor, mutation factor, and selection factor in the genetic algorithm. By designing an orthogonal set of trials with representative combinations of these parameters, we efficiently determined the optimal set of parameters through a hierarchical search. The optimal parameters were obtained from the protein crambin and applied to the structure prediction of cytochrome B562. The results indicate that the genetic algorithm with the optimal parameters reduces the computing time to reach a converged energy compared to nonoptimal parameters. It also has less chance to be trapped in a local energy minimum, and predicts a protein structure which is closer to the experimental one. Our method may also be applicable to many other optimization problems in computational biology.

Algorithms↗

PubMed2004

A simple geometrical pattern for the branching distribution of the bronchial tree, useful to estimate optimality departures.

The design of the bronchial tree has largely been proposed as a model of optimal design from a physical-functional perspective. However, the distributive function of the airway may be more related to a geometrical than a physical problem. The bronchial tree must distribute a three dimensional volume of inspired air on a two dimensional alveolar surface, included in a limited volume. It is thus valid to ask whether an optimal bronchial tree from a physical perspective is also optimum from a geometrical point of view. In this paper we generate a simple geometric model for the branching pattern of the bronchial tree, deducing relationships that permit estimation of the departures from the geometrical optimum of each bifurcation. We also, for comparative purposes, estimate the departures from the physical optimum. From the geometrical assumptions: i) a symmetrical dichotomic fractal design, ii) with minimum volume and iii) maximum dispersion of the terminal points; and several simulations we suggest that the optimality is characterized by a bifurcation angle theta approximately 60 degrees and a length reduction scale gamma = (1/2)(1/3) = 0.7937. We propose distances from the physical and geometrical optimality defined as Euclidean distances from the expected optima. We show how the advanced relationships and the distances can be used to estimate departures from the optimality in bronchographs of four species. We found lower physical and geometrical departures in the distal zone than those of the proximal zones, as well as lower physical than geometrical departures from optimality.

Animals↗

PubMed2004

Optimization of ex vivo CT- and MR- imaging of atherosclerotic vessel wall changes.

OBJECTIVE: To optimize a methodology for ex vivo imaging of atherosclerotic vessel wall changes using multidetector-row computed tomography (MDCT) and multi-contrast magnetic resonance imaging (MRI). METHODS: In phantom studies and studies on intact ex vivo porcine and human hearts, various filling mixtures of MDCT and MRI contrast agents have been evaluated, to enable filling and distension of the coronary arteries for optimal visualization of atherosclerotic vessel wall changes with both techniques. Various proportions of methyl cellulose, iodine-containing CT contrast agent and paramagnetic MR contrast agent containing iron-oxide particles have been tested. Imaging parameters have been optimized for high resolution plaque imaging using a four detector-row CT scanner and a 1.5 T MR system. RESULTS: Phantom studies and studies on ex vivo porcine and human hearts demonstrated optimal proportion of methyl cellulose and CT contrast agent to be 98% vs. 2%, and 75% vs. 25% of methyl cellulose vs. MR contrast agent, respectively. These proportions provided optimal opacification of the vessel lumen in the MDCT images with 250 Hounsfield Units, and good signal suppression within the vessel lumen in the MR images, resembling in vivo imaging techniques. After retrospective matching with histopathology, atherosclerotic lesions of the human ex vivo specimens could be identified on MRI and MDCT images. CONCLUSION: Using an optimized mixture of methyl cellulose, MDCT and MRI contrast agents, visualization of atherosclerotic vessel wall changes is feasible, and applicable to various ex vivo models.

Aged↗

PubMed1997

Distinguishing optimism from pessimism in older adults: is it more important to be optimistic or not to be pessimistic?

Confirmatory factor analysis revealed that the Life Orientation Test (LOT) consisted of separate Optimism and Pessimism factors among middle-aged and older adults. Although the two factors were significantly negatively correlated among individuals facing a profound life challenge (i.e., caregiving), they were only weakly correlated among noncaregivers. Caregivers also expressed less optimism than noncaregivers and showed a trend toward greater pessimism, suggesting that life stress may affect these dispositions. Pessimism, not optimism, uniquely predicted subsequent psychological and physical health; however, optimism and pessimism were equally predictive for stressed and nonstressed samples. By exploring optimism and pessimism separately, researchers may better determine whether the beneficial effects of optimism result from thinking optimistically, avoiding pessimistic thinking, or a combination of the two.

Adaptation, Psychological↗

PubMed2004

Optimism, perceived risk of breast cancer, and cancer worry among a community-based sample of women.

Optimism is frequently inversely associated with distress; however, less is known about the mechanisms underlying this relationship. Optimism can be defined as having a generalized positive expectancy about the future. The authors hypothesized that a specific expectancy might mediate the relationship between optimism and distress. That model was tested using perceived risk of breast cancer as a specific measure of expectancy and cancer worry as a measure of distress in a community-based sample of 1,366 women. It was hypothesized that optimism would be inversely associated with cancer worry and that perceived risk of breast cancer would mediate the relationship between optimism and cancer worry. Multiple regression analyses revealed that perceived risk partially mediated the relationship between optimism and cancer worry.

Adult↗

PubMed2002

Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.

Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.

Acetic Acid↗

PubMed2004

Optimal neural population coding of an auditory spatial cue.

A sound, depending on the position of its source, can take more time to reach one ear than the other. This interaural (between the ears) time difference (ITD) provides a major cue for determining the source location. Many auditory neurons are sensitive to ITDs, but the means by which such neurons represent ITD is a contentious issue. Recent studies question whether the classical general model (the Jeffress model) applies across species. Here we show that ITD coding strategies of different species can be explained by a unifying principle: that the ITDs an animal naturally encounters should be coded with maximal accuracy. Using statistical techniques and a stochastic neural model, we demonstrate that the optimal coding strategy for ITD depends critically on head size and sound frequency. For small head sizes and/or low-frequency sounds, the optimal coding strategy tends towards two distinct sub-populations tuned to ITDs outside the range created by the head. This is consistent with recent observations in small mammals. For large head sizes and/or high frequencies, the optimal strategy is a homogeneous distribution of ITD tunings within the range created by the head. This is consistent with observations in the barn owl. For humans, the optimal strategy to code ITDs from an acoustically measured distribution depends on frequency; above 400 Hz a homogeneous distribution is optimal, and below 400 Hz distinct sub-populations are optimal.

Acoustic Stimulation↗

PubMed2003

Optimization of submerged-culture conditions for mycelial growth and exo-biopolymer production by Auricularia polytricha (wood ears fungus) using the methods of uniform design and regression analysis.

This paper is concerned with the optimization of submerged culture conditions for mycelial growth and exo-biopolymer production by Auricularia polytricha by one-factor-at-a-time and uniform design (UD) methods. First, the one-factor-at-a-time method was adopted to investigate the effects of environmental factors (i.e., initial pH and temperature) and variables of medium components (i.e., carbon, nitrogen and mineral sources) on mycelial growth and exo-biopolymer production. Sucrose, yeast extract, and K2HPO4 were identified to be the most suitable carbon, nitrogen, and mineral sources, respectively. The optimal temperature and initial pH for mycelial growth and exo-biopolymer production were found to be 25 degrees C and 5.0, respectively. Subsequently, the concentrations of sucrose, yeast extract, and K2HPO4 were optimized using the UD method. The optimal concentrations for the enhanced production were determined as 6% (w/v) sucrose, 2.5% (w/v) yeast extract, and 0.3% (w/v) K2HPO4 for mycelial yield, and 6% (w/v) sucrose, 1.28% (w/v) yeast extract, and 0.3% (w/v) K2HPO4 for exo-biopolymer production, respectively. Subsequent experiments confirmed the validity of the models. This optimization strategy in shake-flask culture led to a mycelial yield of 6.14 g/l, and exo-biopolymer production of 2.12 g/l, respectively, which were considerably higher than those obtained in the preliminary studies. By using the optimized medium, the maximum concentrations of mycelial biomass and exo-biopolymer in a 5 litre stirred-tank bioreactor indicated 35.3 g/l and 3.1 g/l, respectively.

Basidiomycota↗