A spectral-based cusum test of evolutionary change

We develop a test of evolutionary change that incorporates a null hypothesis of homogeneity, which encompasses time invariance in the variance and autocovariance structure of residuals from estimated econometric relationships. The test framework is based on examining whether shifts in spectral decomposition between two frames of data are significant. Rejection of the null hypothesis will point not only to weak nonstationarity but to shifts in the structure of the second-order moments of the limiting distribution of the random process. This would indicate that the second-order properties of any underlying attractor set has changed in a statistically significant way, pointing to the presence of evolutionary change. A demonstration of the test's applicability to a real-world macroeconomic problem is accomplished by applying the test to the Australian Building Society Deposits (ABSD) model.

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.

Pt-RuO(2) electrodes prepared by thermal decomposition of polymeric precursors as catalysts for direct methanol fuel cell applications

The electrocatalytic activity of Pt and RuO(2) mixed electrodes of different compositions towards methanol oxidation was investigated. The catalysts were prepared by thermal decomposition of polymeric precursors and characterized by energy dispersive X-ray, scanning electronic microscopy, X-ray diffraction and cyclic voltammetry. This preparation method allowed obtaining uniform films with controlled stoichiometry and high surface area. Cyclic voltammetry experiments in the presence of methanol showed that mixed electrodes decreased the potential peak of methanol oxidation by approximately 100 mV (RHE) when compared to the electrode containing only Pt. In addition, voltammetric experiments indicated that the Pt(0.6)Ru(0.4)O(y) electrode led to higher oxidation current densities at lower potentials. Chronoamperometry experiments confirmed the contribution of RuO(2) to the catalytic activity as well as the better performance of the Pt(0.6)Ru(0.4)O(y) electrode composition. Formic acid and CO(2) were identified as being the reaction products formed in the electrolysis performed at 400 and 600 mV. The relative formation of CO(2) was favored in the electrolysis performed at 400 mV (RHE) with the Pt(0.6)Ru(0.4)O(y) electrode. The presence of RuO(2) in Pt-Ru-based electrodes is important for improving the catalytic activity towards methanol electrooxidation. Moreover, the thermal decomposition of polymeric precursors seems to be a promising route for the production of catalysts applicable to DMFC. (C) 2009 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved.

Application of Ti/RuO(2)-Ta(2)O(5) electrodes in the electrooxidation of ethanol and derivants: Reactivity versus electrocatalytic efficiency

The influence of the preparation method on the performance of RuO(2)-Ta(2)O(5) electrodes was evaluated toward the ethanol oxidation reaction (EOR). Freshly prepared RuO(2)-Ta(2)O(5) thin films containing between 30 and 80 at.% Ru were prepared by two different methods: the modified Pechini-Adams method (DPP) and standard thermal decomposition (STD). Electrochemical investigation of the electrode containing RuO(2)-Ta(2)O(5) thin films was conducted as a function of electrode composition in a 0.5-mol dm(-3) H(2)SO(4) solution, in the presence and absence of ethanol and its derivants (acetaldehyde and acetic acid). At a low ethanol concentration (5 mmol dm(-3)), ethanol oxidation leads to high yields of acetic acid and CO(2). On the other hand, an increase in ethanol concentration (15-1000 mmol dm(-3)) favors acetaldehyde formation, so acetic acid and CO(2) production is hindered, in this case. Electrodes prepared by DPP provide higher current efficiency than STD electrodes for all the investigated ethanol concentrations. This may be explained by the increase in electrode area obtained with the DPP preparation method compared with STD. (c) 2008 Elsevier Ltd. 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.

Morphological and electrochemical investigation of RuO(2)-Ta(2)O(5) oxide films prepared by the Pechini-Adams method

Preparation methods can profoundly affect the structural and electrochemical properties of electrocatalytic coatings. In this investigation, RuO(2)-Ta(2)O(5) thin films containing between 10 and 90 at.% Ru were prepared by the Pechini-Adams method. These coatings were electrochemically and physically characterized by cyclic voltammetry, scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD). The composition and morphology of the oxide were investigated before and after accelerated life tests (ALT) by EDX and SEM. SEM results indicate typical mud-flat-cracking morphology for the majority of the films. High resolution SEMs reveal that pure oxide phases exhibit nanoporosity while binary compositions display a very compact structure. EDX analyses reveal considerable amounts of Ru in the coating even after total deactivation. XRD indicated a rutile-type structure for RuO(2) and orthorhombic structure for Ta(2)O(5). XPS data demonstrate that the binding energy of Ta is affected by Ru addition in the thin films, but the binding energy of Ru is not likewise influenced by Ta. The stability of the electrodes was evaluated by ALT performed at 750 mA cm(-2) in 80 degrees C 0.5 mol dm(-3) H(2)SO(4). The performance of electrodes prepared by the Pechini-Adams method is 100% better than that of electrodes prepared by standard thermal decomposition.

HPLC-FLD methods to quantify chloroaluminum phthalocyanine in nanoparticles, plasma and tissue: application in pharmacokinetic and biodistribution studies

Analytical and bioanalytical methods of high-performance liquid chromatography with fluorescence detection (HPLC-FLD) were developed and validated for the determination of chloroaluminum phthalocyanine in different formulations of polymeric nanocapsules, plasma and livers of mice. Plasma and homogenized liver samples were extracted with ethyl acetate, and zinc phthalocyanine was used as internal standard. The results indicated that the methods were linear and selective for all matrices studied. Analysis of accuracy and precision showed adequate values, with variations lower than 10% in biological samples and lower than 2% in analytical samples. The recoveries were as high as 96% and 99% in the plasma and livers, respectively. The quantification limit of the analytical method was 1.12 ng/ml, and the limits of quantification of the bioanalytical method were 15 ng/ml and 75 ng/g for plasma and liver samples, respectively. The bioanalytical method developed was sensitive in the ranges of 15-100 ng/ml in plasma and 75-500 ng/g in liver samples and was applied to studies of biodistribution and pharmacokinetics of AlClPc. (C) 2011 Elsevier B.V. All rights reserved.

Validated spectrophotometric and spectrofluorimetric methods for determination of chloroaluminum phthalocyanine in nanocarriers

UV-VIS-Spectrophotometric and spectrofluorimetric methods have been developed and validated allowing the quantification of chloroaluminum phthalocyanine (CIAIPc) in nanocarriers. In order to validate the methods, the linearity, limit of detection (LOD), limit of quantification (LOQ), precision, accuracy, and selectivity were examined according to USP 30 and ICH guidelines. Linearities range were found between 0.50-3.00 mu g.mL(-1) (Y=0.3829 X [CIAIPc, mu g.mL(-1)] + 0.0126; r=0.9992) for spectrophotometry, and 0.05-1.00 mu g.mL(-1) (Y=2.24 x 10(6) X [CIAIPc, mu g.L(-1)] + 9.74 x 10(4); r=0.9978) for spectrofluorimetry. In addition, ANOVA and Lack-of-fit tests demonstrated that the regression equations were statistically significant (p<0.05), and the resulting linear model is fully adequate for both analytical methods. The LOD values were 0.09 and 0.01 mu g.mL(-1), while the LOCI were 0.27 and 0.04 mu g.mL(-1) for spectrophotometric and spectrofluorimetric methods, respectively. Repeatability and intermediate precision for proposed methods showed relative standard deviation (RSD) between 0.58% to 4.80%. The percent recovery ranged from 98.9% to 102.7% for spectrophotometric analyses and from 94.2% to 101.2% for spectrofluorimetry. No interferences from common excipients were detected and both methods were considered specific. Therefore, the methods are accurate, precise, specific, and reproducible and hence can be applied for quantification of CIAIPc in nanoemulsions (NE) and nanocapsules (NC).