Minimum-order stable recursive filter design via the genetic algorithm approach

In this paper, the minimum-order stable recursive filter design problem is proposed and investigated. This problem is playing an important role in pipeline implementation sin signal processing. Here, the existence of a high-order stable recursive filter is proved theoretically, in which the upper bound for the highest order of stable filters is given. Then the minimum-order stable linear predictor is obtained via solving an optimization problem. In this paper, the popular genetic algorithm approach is adopted since it is a heuristic probabilistic optimization technique and has been widely used in engineering designs. Finally, an illustrative example is sued to show the effectiveness of the proposed algorithm.

Modelling life history strategies with capture-recapture data: Evolutionary demography of the water skink Eulamprus tympanum

Matrix population models, elasticity analysis and loop analysis can potentially provide powerful techniques for the analysis of life histories. Data from a capture-recapture study on a population of southern highland water skinks (Eulamprus tympanum) were used to construct a matrix population model. Errors in elasticities were calculated by using the parametric bootstrap technique. Elasticity and loop analyses were then conducted to identify the life history stages most important to fitness. The same techniques were used to investigate the relative importance of fast versus slow growth, and rapid versus delayed reproduction. Mature water skinks were long-lived, but there was high immature mortality. The most sensitive life history stage was the subadult stage. It is suggested that life history evolution in E. tympanum may be strongly affected by predation, particularly by birds. Because our population declined over the study, slow growth and delayed reproduction were the optimal life history strategies over this period. Although the techniques of evolutionary demography provide a powerful approach for the analysis of life histories, there are formidable logistical obstacles in gathering enough high-quality data for robust estimates of the critical parameters.

Determining complicated winding patterns for shim coils using stream functions and the target-field method

In a magnetic resonance imaging equipment, gradient and shim coils are needed to produce a spatially varying magnetic field throughout the sample being imaged. Such coils consist of turns of wire wound on the surface of a cylindrical tube. Shim coils in particular, must sometimes be designed to produce complicated magnetic fields to correct for impurities. Streamline patterns for shim coils are much more complicated than those for gradient coils, In this work we present a detailed analysis of streamline methods and their application to shim coil design, A method is presented for determining the winding patterns to generate these complicated fields. (C) 2002 John Wiley & Sons, Inc.

Methods for prediction of peptide binding to MHC molecules: A comparative study

Background: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. Materials and Methods: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. Results: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. Conclusion: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.

Predicting energy requirements in the clinical setting: Are current methods evidence based?

Estimating energy requirements is necessary in clinical practice when indirect calorimetry is impractical. This paper systematically reviews current methods for estimating energy requirements. Conclusions include: there is discrepancy between the characteristics of populations upon which predictive equations are based and current populations; tools are not well understood, and patient care can be compromised by inappropriate application of the tools. Data comparing tools and methods are presented and issues for practitioners are discussed. (C) 2003 International Life Sciences Institute.

Formal methods within a totally functional approach to programming

Taking functional programming to its extremities in search of simplicity still requires integration with other development (e.g. formal) methods. Induction is the key to deriving and verifying functional programs, but can be simplified through packaging proofs with functions, particularly folds, on data (structures). Totally Functional Programming avoids the complexities of interpretation by directly representing data (structures) as platonic combinators - the functions characteristic to the data. The link between the two simplifications is that platonic combinators are a kind of partially-applied fold, which means that platonic combinators inherit fold-theoretic properties, but with some apparent simplifications due to the platonic combinator representation. However, despite observable behaviour within functional programming that suggests that TFP is widely-applicable, significant work remains before TFP as such could be widely adopted.

Assessing cost-effectiveness - Mental health: introduction to the study and methods

Objective: The Assessing Cost-Effectiveness - Mental Health (ACE-MH) study aims to assess from a health sector perspective, whether there are options for change that could improve the effectiveness and efficiency of Australia's current mental health services by directing available resources toward 'best practice' cost-effective services. Method: The use of standardized evaluation methods addresses the reservations expressed by many economists about the simplistic use of League Tables based on economic studies confounded by differences in methods, context and setting. The cost-effectiveness ratio for each intervention is calculated using economic and epidemiological data. This includes systematic reviews and randomised controlled trials for efficacy, the Australian Surveys of Mental Health and Wellbeing for current practice and a combination of trials and longitudinal studies for adherence. The cost-effectiveness ratios are presented as cost (A$) per disability-adjusted life year (DALY) saved with a 95% uncertainty interval based on Monte Carlo simulation modelling. An assessment of interventions on 'second filter' criteria ('equity', 'strength of evidence', 'feasibility' and 'acceptability to stakeholders') allows broader concepts of 'benefit' to be taken into account, as well as factors that might influence policy judgements in addition to cost-effectiveness ratios. Conclusions: The main limitation of the study is in the translation of the effect size from trials into a change in the DALY disability weight, which required the use of newly developed methods. While comparisons within disorders are valid, comparisons across disorders should be made with caution. A series of articles is planned to present the results.

Low Satellite DNA Variability in Natural Populations of Drosophila antonietae Involved in Different Evolutionary Events

Drosophila antonietae is a cactophilic species that is found in the mesophilic forest of the Parana`-Paraguay river basin and in the dunes of the South Atlantic coast of Brazil. Although the genetic structure of the Parana`-Paraguay river basin populations has already been established, the relationship between these populations and those on the Atlantic coast is controversial. In this study, we compared 33 repetitive units of pBuM-2 satellite DNA isolated from individuals from 8 populations of D. antonietae in these geographic regions, including some populations found within a contact zone with the closely related D. serido. The pBuM-2 sequences showed low interpopulational variability. This result was interpreted as a consequence of both gene flow among the populations and unequal crossing over promoting homogenization of the tandem arrays. The results presented here, together with those of previous studies, highlight the use of pBuM-2 for solving taxonomic conflicts within the D. buzzatii species cluster.

Functional and Evolutionary Insights from the Genomes of Three Parasitoid Nasonia Species

We report here genome sequences and comparative analyses of three closely related parasitoid wasps: Nasonia vitripennis, N. giraulti, and N. longicornis. Parasitoids are important regulators of arthropod populations, including major agricultural pests and disease vectors, and Nasonia is an emerging genetic model, particularly for evolutionary and developmental genetics. Key findings include the identification of a functional DNA methylation tool kit; hymenopteran-specific genes including diverse venoms; lateral gene transfers among Pox viruses, Wolbachia, and Nasonia; and the rapid evolution of genes involved in nuclear-mitochondrial interactions that are implicated in speciation. Newly developed genome resources advance Nasonia for genetic research, accelerate mapping and cloning of quantitative trait loci, and will ultimately provide tools and knowledge for further increasing the utility of parasitoids as pest insect-control agents.

Solid-phase microextraction using poly(pyrrole) film and liquid chromatography with UV detection for analysis of antidepressants in plasma samples

Poly(pyrrole) (PPY) coating was prepared on a stainless-steel (SS) wire for solid-phase microextraction (SPME) by electrochemical deposition (cyclic voltammetric). The PPY was evaluated by analyzing new-generation antidepressants (mirtazapine, citalopram, paroxetine, duloxetine, fluoxetine, and sertraline) in plasma sample by SPME and liquid chromatography with UV detection (LC-UV). The effect of electrolyte Solution (lithium perchlorate or tetrabutylammonium perchlorate) and the number of cycles (50, 100 or 200) applied during the polymerization process on the SPME performance was evaluated. Important factors in the optimization of SPME efficiency such as extraction time, temperature, pH, influence of plasma proteins on sorption mechanisms, and desorption conditions are discussed. The SPME-PPY/LC method showed to be linear in concentrations ranging from the limit of quantification (LOQ) to 1200 ng mL(-1). The LOQ values range from 16 to 25 ng mL-1. The inter-day precision of the SPME-PPY/LC method presented coefficient of variation (CV) lower than 15%. Based on analytical validation results, the SPME-PPY/LC methodology showed to be adequate for antidepressant analysis, from therapeutic to toxic levels. In order to evaluate the proposed method for clinical use, the SPME-PPY/LC method was applied to the analysis of plasma samples from elderly depressed patients. (c) 2009 Elsevier B.V. All rights reserved,

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

In-tube solid-phase microextraction coupled to liquid chromatography (in-tube SPME/LC) analysis of nontricyclic antidepressants in human plasma

A sensitive, selective, and reproducible in-tube solid-phase microextraction and liquid chromatographic (in-tube SPME/LC-UV) method for simultaneous determination of mirrazapine, citalopram, paroxetine, duloxetine, fluoxetine, and sertraline in human plasma was developed, validated and further applied to the analysis of plasma samples from elderly patients undergoing therapy with antidepressants. Important factors in the optimization of in-tube SPME efficiency are discussed, including the sample draw/eject volume, draw/eject cycle number, draw/eject flow-rate, sample pH, and influence of plasma proteins. The quantification limits of the in-tube SPME/LC method varied between 20 and 50 ng/mL, with a coefficient of variation lower than 10%. The response of the in-tube SPME/LC method for most of the drugs was linear over a dynamic range from 50 to 500 ng/mL, with correlation coefficients higher than 0.9985. The in-tube SPME/LC can be successfully used to analyze plasma samples from ageing patients undergoing therapy with nontricyclic antidepressants. (c) 2007 Elsevier B.V. All rights reserved.

Microextraction in packed sorbent for analysis of antidepressants in human plasma by liquid chromatography and spectrophotometric detection

A sensitive and reproducible method by microextraction packed sorbent and liquid chromatography with UV detection (MEPS/LC-UV) is described for the determination of new generation antidepressants (sertraline, mirtazapine, fluoxetine, citalopram and paroxetine) in human plasma samples. The MEPS variables, such as sample volume, pH, number of extraction cycles (draw-eject), and desorption conditions (solvent and solvent volume of elution) influenced the MEPS/LC efficiency significantly. Important factors in the optimization of MEPS efficiency, as well as washing steps and carryover effect are discussed. The analyses were carried out using small sample volumes (400 mu L.), and in a short time period (3 min for the entire sample preparation step). The MEPS/LC-UV method was shown to be linear at concentrations ranging from the limit of quantification (LOQ) to 1000 ng mL(-1). The LOQ values ranged from 10 to 25 ng mL(-1). The inter-day precision of the method presented coefficient of the variation ranging from 1.3% to 8.7%. On the basis of analytical validation, it is shown that the MEPS/LC-UV methodology is adequate for antidepressant analysis, from therapeutic to toxic levels. In order to evaluate the proposed method for clinical use, the MEPS/LC-UV method was applied to analysis of plasma samples from elderly depressed patients. (C) 2010 Elsevier B.V. All rights reserved.